Fault detection method and device of energy storage equipment group, computer equipment and readable storage medium

Abstract

The invention relates to a fault detection method and device for an energy storage equipment group, computer equipment and a readable storage medium. The method comprises the following steps: acquiring an average voltage of an energy storage device group at a high-frequency sampling time point, and a single voltage of each energy storage device in the energy storage device group at a low-frequency sampling time point corresponding to the high-frequency sampling time point; based on the average voltage, detecting an average state-of-charge value of the energy storage device group; for each energy storage device, based on the average state-of-charge value and the voltage difference between the single voltage and the average voltage, detecting the single state-of-charge value corresponding to the single voltage, and detecting the single equivalent capacity of the energy storage device according to the single state-of-charge value; according to the average state-of-charge value and the single equivalent capacity and the single state-of-charge value of each energy storage device, generating single fault detection results of all the energy storage devices; and generating a fault detection result of the energy storage equipment group. By adopting the method, the fault detection of the energy storage equipment group can be accurately carried out.

Description

Technical Field

[0001] The present application relates to the technical field of fault detection, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for fault detection of an energy storage device group. Background Art

[0002] Fault detection is a technology that is often used in daily production and life. In the use of energy storage equipment groups, such as battery packs, fault detection is also an extremely important part. By detecting the faults of energy storage equipment groups, the use status of energy storage equipment can be detected in time, and the faulty energy storage equipment can be replaced in time.

[0003] At present, the method for fault detection of energy storage device groups is usually a state inconsistency estimation method based on the mean-difference model, that is, a dynamic SOC (State of Charge) adjustment strategy is proposed to intermittently reduce the SOC of the energy storage device group to 30%, and perform voltage measurement. For example, the average OCV (Opencircuit voltage) of the energy storage device group is measured, and the single-cell OCV of each energy storage device in the energy storage device group is measured one by one. The SOC-OCV difference curve is used to map the difference between the estimated single-cell OCV of the single energy storage device and the average voltage to the SOC difference. Finally, based on the battery SOC difference, the energy storage device group is directly fault detected.

[0004] However, the current fault detection method for energy storage device groups is not accurate enough. Summary of the invention

[0005] Based on this, it is necessary to provide an accurate fault detection method, device, computer equipment, computer-readable storage medium and computer program product for the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for detecting a fault of an energy storage device group, comprising:

[0007] Obtaining the average voltage of the energy storage device group at the high-frequency sampling time point, and the single-cell voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point;

[0008] Based on the average voltage, detecting the average state of charge value of the energy storage device group;

[0009] For each energy storage device, based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, the single cell state of charge value corresponding to the single cell voltage is detected, and according to the single cell state of charge value, the single cell equivalent capacity of the energy storage device is detected;

[0010] Generate single-cell fault detection results of all energy storage devices based on the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each energy storage device;

[0011] Based on the single-unit fault detection results of all energy storage devices, a fault detection result of the energy storage device group is generated.

[0012] In one embodiment, based on the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each energy storage device, single-cell fault detection results of all energy storage devices are generated, including:

[0013] Energy storage devices with a single equivalent capacity lower than a preset capacity threshold are marked as energy storage devices, and energy storage devices with a single equivalent capacity higher than or equal to the preset capacity threshold are unmarked energy storage devices;

[0014] Determine that a single-unit fault detection result of the unmarked energy storage device is that there is no fault, and obtain a capacity average of the single-unit equivalent capacities of all unmarked energy storage devices;

[0015] For any target marked energy storage device among the marked energy storage devices, a single cell fault detection result of the target marked energy storage device is generated according to the average state of charge value, the average capacity, and the single cell equivalent capacity and the single cell state of charge value of the target marked energy storage device.

[0016] In one embodiment, a single cell fault detection result of a target energy storage device is generated according to the average state of charge value, the capacity mean value, and the single cell equivalent capacity and the single cell state of charge value of the target energy storage device, including:

[0017] When the single state of charge value of the target marked energy storage device exceeds the average state of charge value by a preset multiple within the target charging time period, the ratio of the number of time periods in which the single state of charge value exceeds the average state of charge value within all the preset charging time periods is obtained, wherein the target charging time period is any time period among all the preset charging time periods;

[0018] When the monomer equivalent capacity is less than the capacity average of the first preset ratio and the time period quantity ratio is greater than the second preset ratio, it is determined that the target marked energy storage device has a low capacity fault;

[0019] When the monomer equivalent capacity is greater than or equal to the capacity average of the first preset ratio and the time period quantity ratio is less than the second preset ratio, it is determined that the target marked energy storage device has a micro short circuit fault.

[0020] In one embodiment, detecting an average state of charge value of the energy storage device group based on an average voltage of the energy storage device group includes:

[0021] Obtaining a mapping relationship between voltage and state of charge value;

[0022] Based on the mapping relationship, the state of charge value corresponding to the average voltage is queried, and the state of charge value corresponding to the average voltage is used as the average state of charge value of the energy storage device group.

[0023] In one embodiment, based on the average state of charge value and the voltage difference between the cell voltage and the average voltage, detecting the cell state of charge value corresponding to the cell voltage includes:

[0024] Based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, query the initial single cell state of charge value corresponding to the single cell voltage from the mapping relationship;

[0025] Detect the state update step of the initial single cell state of charge value;

[0026] Based on the average state of charge value and the state update step size, the initial single cell state of charge value is updated to obtain the single cell state of charge value of the single cell voltage.

[0027] In one embodiment, detecting the equivalent capacity of a single cell of an energy storage device according to the state of charge value of the single cell includes:

[0028] Obtain the discharged power of the energy storage device;

[0029] According to the discharged electricity of the energy storage device and the charge state value of the single cell, the single cell equivalent capacity of the energy storage device is detected.

[0030] In a second aspect, the present application also provides a fault detection device for an energy storage device group, comprising:

[0031] A voltage acquisition module is used to obtain the average voltage of the energy storage device group at the high-frequency sampling time point, and the single-cell voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point;

[0032] An average state of charge value detection module, used to detect the average state of charge value of the energy storage device group based on the average voltage;

[0033] A single cell state of charge value and capacity detection module is used to detect the single cell state of charge value corresponding to the single cell voltage for each energy storage device based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, and detect the single cell equivalent capacity of the energy storage device according to the single cell state of charge value;

[0034] A single-cell fault detection module is used to generate single-cell fault detection results of all energy storage devices according to the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each energy storage device;

[0035] The energy storage device group fault detection module is used to generate a fault detection result of the energy storage device group based on the single-unit fault detection results of all energy storage devices.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Obtaining the average voltage of the energy storage device group at the high-frequency sampling time point, and the single-cell voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point;

[0038] Based on the average voltage, detecting the average state of charge value of the energy storage device group;

[0039] For each energy storage device, based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, the single cell state of charge value corresponding to the single cell voltage is detected, and according to the single cell state of charge value, the single cell equivalent capacity of the energy storage device is detected;

[0040] Generate single-cell fault detection results of all energy storage devices based on the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each energy storage device;

[0041] Based on the single-unit fault detection results of all energy storage devices, a fault detection result of the energy storage device group is generated.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0043] Obtaining the average voltage of the energy storage device group at the high-frequency sampling time point, and the single-cell voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point;

[0044] Based on the average voltage, detecting the average state of charge value of the energy storage device group;

[0045] For each energy storage device, based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, the single cell state of charge value corresponding to the single cell voltage is detected, and according to the single cell state of charge value, the single cell equivalent capacity of the energy storage device is detected;

[0046] Generate single-cell fault detection results of all energy storage devices based on the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each energy storage device;

[0047] Based on the single-unit fault detection results of all energy storage devices, a fault detection result of the energy storage device group is generated.

[0048] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0049] Obtaining the average voltage of the energy storage device group at the high-frequency sampling time point, and the single-cell voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point;

[0050] Based on the average voltage, detecting the average state of charge value of the energy storage device group;

[0051] For each energy storage device, based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, the single cell state of charge value corresponding to the single cell voltage is detected, and according to the single cell state of charge value, the single cell equivalent capacity of the energy storage device is detected;

[0052] Generate single-cell fault detection results of all energy storage devices based on the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each energy storage device;

[0053] Based on the single-unit fault detection results of all energy storage devices, a fault detection result of the energy storage device group is generated.

[0054] The above-mentioned energy storage device group fault detection method, apparatus, computer equipment, computer-readable storage medium and computer program product obtain the average voltage of the energy storage device group at the high-frequency sampling time point, and obtain the single cell voltage of each energy storage device in the energy storage device group at the low-frequency sampling time corresponding to the high-frequency sampling time point, that is, on the aligned time basis, effectively detect the voltage of a large number of single cell energy storage devices, so as to improve the accuracy of voltage acquisition. Furthermore, in the process of fault detection, based on the average voltage, the average state of charge value of the energy storage device group is detected, and for each energy storage device, based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, the single cell state of charge value corresponding to the single cell voltage is detected, and according to the single cell state of charge value, the single cell equivalent capacity of the energy storage device is detected, so as to achieve accurate quantification of the fault detection parameters of the energy storage device through the average state of charge value, the single cell equivalent capacity and the single cell state of charge value of each energy storage device, so as to accurately generate the single cell fault detection result of the energy storage device, and then summarize the single cell fault detection results of all energy storage devices, and accurately generate the fault detection result of the energy storage device group. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments of the present application or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 An application environment diagram of a method for detecting a fault of an energy storage device group in one embodiment;

[0057] Figure 2 A schematic diagram of a flow chart of a method for detecting a fault of an energy storage device group in one embodiment;

[0058] Figure 3 A schematic diagram showing a comparison of two methods for measuring the single cell voltage of each energy storage device in one embodiment;

[0059] Figure 4 A schematic diagram comparing the single cell voltage distribution of an energy storage device in two modes in one embodiment;

[0060] Figure 5 A schematic flow chart of a method for detecting a fault of an energy storage device group in another embodiment;

[0061] Figure 6 It is a schematic diagram of the mapping relationship between voltage and state of charge value in a specific application embodiment;

[0062] Figure 7 A schematic diagram of a mapping relationship between voltage and state of charge value, and a relative relationship between terminal voltages in a specific application embodiment;

[0063] Figure 8 is a schematic diagram of the distribution of open circuit voltage in one embodiment;

[0064] Fig. 9 is a schematic diagram of a fitting relationship curve between voltage and state of charge value in another specific application embodiment;

[0065] Fig.10 A structural block diagram of a fault detection device for an energy storage device group in one embodiment;

[0066] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are used to explain the present application and are not used to limit the present application.

[0068] The energy storage device group fault detection method provided in the embodiment of the present application can be applied alone to the terminal of the energy storage power station, and can also be applied to Figure 1In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0069] The user triggers a fault detection operation for the energy storage device group on the terminal 102. The terminal 102 responds to the fault detection operation and sends a fault detection request to the server 104. After receiving the fault detection request, the server 104 controls the voltage detection device to detect the average voltage of the energy storage device group at the high-frequency sampling time point, and detects the single voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point. The server 104 obtains the average voltage of the energy storage device group at the high-frequency sampling time point, and the single voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point. The cell voltage of each energy storage device; based on the average voltage, the average state of charge value of the energy storage device group is detected; for each energy storage device, based on the average state of charge value and the voltage difference between the cell voltage and the average voltage, the cell state of charge value corresponding to the cell voltage is detected, and the cell equivalent capacity of the energy storage device is detected according to the cell state of charge value; based on the average state of charge value, the cell equivalent capacity and the cell state of charge value of each energy storage device, the cell fault detection results of all energy storage devices are generated; based on the cell fault detection results of all energy storage devices, the fault detection results of the energy storage device group are generated. Furthermore, the fault detection results can be pushed to the terminal 102, and displayed to the user by the terminal 102.

[0070] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0071] In an exemplary embodiment, Figure 2 As shown, a method for detecting a fault of an energy storage device group is provided. Figure 1 The server 104 in FIG. 1 is used as an example for explanation. Among them:

[0072] S100, obtaining an average voltage of the energy storage device group at a high-frequency sampling time point, and a single cell voltage of each energy storage device in the energy storage device group at a low-frequency sampling time point corresponding to the high-frequency sampling time point.

[0073] Among them, the energy storage device group can be various types of battery groups, and the energy storage device group includes several energy storage devices. The high-frequency sampling time point refers to the time point at which sampling is performed at a higher frequency. Since the total voltage and current are measured at the output end of the energy storage device group, high-frequency collection can be achieved at the output end of the energy storage device group to form a high-frequency mean model. The high-frequency mean model provides a rapid assessment capability of the state of the energy storage device group. The low-frequency sampling time point refers to the time point at which sampling is performed at a lower frequency. The voltage in this application is the open circuit voltage OCV.

[0074] Specifically, in energy storage power stations, due to the large number of single cells in the energy storage equipment and the high bus load, it is difficult to achieve high-frequency synchronous acquisition of the voltages of all battery cells. The traditional voltage measurement of the energy storage equipment using a polling mechanism usually results in a time difference of tens of seconds, which is not conducive to real-time construction of a voltage state model of the energy storage equipment. In other words, the voltage of the energy storage equipment has the problem of low acquisition frequency and asynchronous sampling timestamps.

[0075] To solve the above problems, the present application proposes an improved fault detection method for an energy storage device group that combines high-frequency mean and low-frequency difference to improve the accuracy and efficiency of fault detection.

[0076] Specifically, for large-scale energy storage device groups, the bus reporting pressure is high, and it is impossible to achieve high-frequency collection of each energy storage device in the energy storage device group. The establishment of a high-frequency mean model requires measuring the output of the energy storage device group, which can ensure the measurement frequency and there is no cumulative error in the voltage of all energy storage devices. Through the high-frequency high-frequency mean model, the average voltage of the energy storage device group is obtained at the high-frequency sampling time point, and the status of all energy storage devices in the energy storage device group is estimated. At this time, the average voltage of the energy storage device group can approximately reflect the overall status of all energy storage devices in the energy storage device group.

[0077] Then, the low-frequency measurement time point of the voltage of the energy storage device is aligned to the time base of the high-frequency mean model. The alignment here refers to directly finding the high-frequency sampling time point corresponding to the low-frequency sampling time point. On the aligned time base, the difference between the single cell voltage of the energy storage device and the average voltage of the energy storage device group is calculated to establish a low-frequency difference model. This difference model can capture the state distribution characteristics between energy storage devices and is used to supplement the overall analysis capability of the high-frequency mean model, thereby achieving a more refined single cell state assessment. In addition, the single cell voltage of the energy storage device of the low-frequency difference model can also be compared with the numerical value of the high-frequency mean model at the low-frequency sampling point to restore the approximate working state of any single cell energy storage device. In practical applications, since the current input and output of the energy storage power station are relatively smooth, the low-frequency difference model is sufficient to complete the description and restoration of the energy storage device at any low-frequency sampling.

[0078] In an exemplary embodiment, Figure 3 As shown, the left side shows the single cell voltage of each energy storage device under the original polling measurement method, and the right side shows the voltage performance of different energy storage devices after being processed by the "high frequency mean + low frequency difference" model. At the same time, the single cell voltage distribution of each energy storage device under the original polling measurement method and the measurement method of this application and the single cell voltage distribution of the "high frequency mean + low frequency difference" model are compared, and the results are as follows Figure 4 ,It can be seen from the box and whisker plot that the single ,voltage distribution of each energy storage device is very close under the original ,polling measurement method, however, after being processed by the “high frequency mean + low frequency ,difference” model, the voltage distribution of different energy storage devices shows ,their own characteristics.

[0079] S200: Detect an average state of charge value of the energy storage device group based on the average voltage.

[0080] Among them, the state of charge value is the ratio of the remaining capacity of the energy storage device after it has been used for a period of time or has been unused for a long time to its capacity in a fully charged state. It is usually expressed as a percentage, and its value range is 0~1. When SOC=0, it means that the battery is fully discharged, and when SOC=1, it means that the battery is fully charged.

[0081] Specifically, the average voltage of the energy storage device group can approximately reflect the overall state of all energy storage devices in the energy storage device group. Therefore, the average state of charge value of the energy storage device group can be detected based on the average voltage of the energy storage device group.

[0082] S300, for each energy storage device, based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, detect the single cell state of charge value corresponding to the single cell voltage, and detect the single cell equivalent capacity of the energy storage device according to the single cell state of charge value.

[0083] Among them, the single cell state of charge value refers to the state of charge value of the energy storage device at a specific point in time, and the equivalent capacity reflects the effective discharge capacity that the energy storage device can provide under the existing fault conditions and working cycles.

[0084] Specifically, in the conventional technology, the state of charge value difference corresponding to the voltage difference is directly obtained based on the voltage difference between the single cell voltage and the average voltage, and the single cell state of charge value corresponding to the single cell voltage is queried based on the state of charge value difference and the average state of charge value. This method is not accurate enough, and the present application will make certain corrections to it to accurately detect the single cell state of charge value corresponding to the single cell voltage. More specifically, this correction can be achieved by taking the derivative of the single cell state of charge value under the original detection method and making corrections based on the derivative result.

[0085] Furthermore, during the online use of the energy storage device group, as the use time goes by, the energy storage device will gradually show a trend of capacity decline and health deterioration. For energy storage devices, due to the complexity of the energy storage device group and the limitations of online operation, it is very challenging to directly quantify the fault status and health of individual energy storage devices. The internal resistance model and capacity decline are very complex. The change in battery internal resistance cannot fully reflect the availability of capacity, and the change in actual capacity is the key indicator of whether the battery can continue to operate safely.

[0086] Although the traditional equivalent circuit model can simulate the electrochemical behavior of the battery, it has great limitations in describing complex fault types (such as capacity decay, micro-short circuit, etc.). In addition, batteries of different capacities have different sensitivities to micro-short circuit resistance. Therefore, in practical applications, a more intuitive and easy-to-operate fault diagnosis standard is needed. To this end, the concept of "equivalent capacity" is proposed to directly quantify the available capacity of the energy storage device under the current fault state. Furthermore, in this application, the single-cell equivalent capacity of the energy storage device can be detected by the single-cell state of charge value of the energy storage device.

[0087] S400, generating single-cell fault detection results of all energy storage devices according to the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each energy storage device.

[0088] Specifically, the average state of charge value, the equivalent capacity of each energy storage device and the state of charge value of each energy storage device are used as detection parameters for fault detection. According to the average state of charge value, the equivalent capacity of each energy storage device and the state of charge value of each energy storage device, the energy storage device is fault-detected to generate the single-cell fault detection results of all energy storage devices. In other words, for any energy storage device, the single-cell fault detection result is generated according to the average state of charge value, the equivalent capacity of each energy storage device and the state of charge value of each energy storage device.

[0089] In an exemplary embodiment, the main fault types of the energy storage device include low capacity faults and micro short circuit faults. In order to effectively monitor and manage these faults, a fault severity classification method is proposed, which is divided into severe faults and minor faults / warnings according to the severity of the faults. When the severity of the fault of the energy storage device is a severe fault, it is directly reported to the user for danger notification, and when the severity of the fault of the energy storage device is a minor fault / warning, internal fault processing or further monitoring of potentially dangerous equipment can be performed.

[0090] S500: Generate a fault detection result of an energy storage device group based on the single-unit fault detection results of all energy storage devices.

[0091] Specifically, in actual use, in addition to locating and evaluating the faults of individual energy storage devices, it is also necessary to conduct a comprehensive analysis of the fault degree of the entire energy storage device group. The fault detection results of the energy storage device group are actually a summary of the single fault detection results of all energy storage devices. The fault detection results of the energy storage device group include the single fault detection results of each energy storage device in the energy storage device group. Based on the fault detection results of the energy storage device group, the energy storage device that has failed in the energy storage device group can be quickly and accurately located.

[0092] In the above-mentioned method for detecting faults of energy storage device groups, by acquiring the average voltage of the energy storage device group at a high-frequency sampling time point, and acquiring the single-cell voltage of each energy storage device in the energy storage device group at a low-frequency sampling time corresponding to the high-frequency sampling time point, that is, on an aligned time basis, effective voltage detection is performed on a large number of single-cell energy storage devices, so as to improve the accuracy of voltage acquisition. Furthermore, in the process of fault detection, the average state of charge value of the energy storage device group is detected based on the average voltage, and for each energy storage device, the single-cell state of charge value corresponding to the single-cell voltage is detected based on the average state of charge value and the voltage difference between the single-cell voltage and the average voltage, and the single-cell equivalent capacity of the energy storage device is detected according to the single-cell state of charge value, so as to achieve accurate quantification of the fault detection parameters of the energy storage device through the average state of charge value, the single-cell equivalent capacity of each energy storage device and the single-cell state of charge value, so as to accurately generate the single-cell fault detection result of the energy storage device, and then summarize the single-cell fault detection results of all energy storage devices, and accurately generate the fault detection result of the energy storage device group.

[0093] In an exemplary embodiment, Figure 5 As shown, S400 includes:

[0094] S420: energy storage devices whose single equivalent capacity is lower than a preset capacity threshold are marked as energy storage devices, and energy storage devices whose single equivalent capacity is higher than or equal to the preset capacity threshold are unmarked energy storage devices.

[0095] S440, determining that a single-unit fault detection result of the unmarked energy storage device is that there is no fault, and obtaining a capacity average of single-unit equivalent capacities of all unmarked energy storage devices.

[0096] S460, for any target marked energy storage device among the marked energy storage devices, generating a single cell fault detection result of the target marked energy storage device according to the average state of charge value, the average capacity, and the single cell equivalent capacity and the single cell state of charge value of the target marked energy storage device.

[0097] Among them, the non-target marked energy storage device is a marked energy storage device other than the target marked energy storage device.

[0098] Specifically, in the process of performing fault detection on the single-unit fault detection result of the energy storage device in the present application, first, a capacity threshold determination is performed on the energy storage device, that is, whether the single-unit equivalent capacity of the single-unit energy storage device is lower than the preset capacity threshold, wherein the preset capacity threshold is the product of the warning capacity threshold and the nominal capacity, and the energy storage device whose single-unit equivalent capacity is lower than the preset capacity threshold is regarded as a marked energy storage device, and the marked energy storage device is a faulty energy storage device that needs to be reported, and the energy storage device whose single-unit equivalent capacity is higher than or equal to the preset capacity threshold is regarded as an unmarked energy storage device, and the single-unit fault detection result of the unmarked energy storage device is that there is no fault, and it is a normal energy storage device. At this time, the capacity mean of the single-unit equivalent capacity of all unmarked energy storage devices is obtained.

[0099] Furthermore, for any target tagged energy storage device among the tagged energy storage devices, a comprehensive detection is performed on the target tagged energy storage device based on the average state of charge value, the capacity mean, and the single cell equivalent capacity and single cell state of charge value of the target tagged energy storage device to generate a single cell fault detection result of the target tagged energy storage device. At this time, the single cell fault detection result is the fault type of the detected target tagged energy storage device.

[0100] In the above embodiment, the energy storage device is divided into normal batteries and faulty batteries by the single cell equivalent capacity, thereby realizing single cell fault detection of the energy storage device, and then a more accurate fault type analysis can be performed on the faulty battery without performing fault type analysis on all batteries, thereby improving the efficiency and accuracy of fault detection.

[0101] In an exemplary embodiment, a single cell fault detection result of a target energy storage device is generated according to the average state of charge value, the capacity mean value, and the single cell equivalent capacity and the single cell state of charge value of the target energy storage device, including:

[0102] When the single cell state of charge value of the target marked energy storage device exceeds the average state of charge value by a preset multiple within the target charging time period, the ratio of the number of time periods in which the single cell state of charge value exceeds the average state of charge value within all preset charging time periods is obtained; when the single cell equivalent capacity is less than the capacity mean of the first preset ratio, and the ratio of the number of time periods is greater than the second preset ratio, it is determined that the target marked energy storage device has a low capacity fault; when the single cell equivalent capacity is greater than or equal to the capacity mean of the first preset ratio, and the ratio of the number of time periods is less than the second preset ratio, it is determined that the target marked energy storage device has a micro short circuit fault.

[0103] The target charging time period is any time period among all preset charging time periods.

[0104] Taking batteries as an example, the corresponding principles and fault characteristics of low-capacity faults and micro-short-circuit faults are as follows:

[0105] 1. Micro short circuit fault:

[0106] Different from a short-circuit fault, a micro-short-circuit fault refers to a short-circuit fault with a large equivalent short-circuit resistance. A micro-short-circuit fault usually does not cause obvious leakage or temperature rise within a period of days. The early stages of both internal and external short-circuit faults can be called micro-shorts. Internal short-circuit faults originate from conductive particles or foreign matter inside the battery, which causes an abnormal weak current path between the positive and negative electrodes. The inducing causes include excessive growth of lithium dendrites, damage to the diaphragm, and separation of the electrolyte. External short-circuit faults are mainly caused by short-circuiting of external circuits, including contact between positive and negative electrode solders, loose and exposed wires, etc.

[0107] Electrical characteristics of micro short circuit fault:

[0108] During the discharge process, the SOC of the micro-short-circuit battery drops faster than other batteries due to the continuous power consumption of the short-circuit resistor. Lithium iron phosphate batteries have unique plateau characteristics. During the plateau period, even if there is a certain difference in the battery SOC, the voltage difference is still small. However, the micro-short-circuit battery has a lower power and can easily leave the plateau during the discharge phase, causing the voltage to drop sharply. It is also worth noting that all lithium iron phosphate batteries will have a rapid voltage drop process at the end of the plateau period, so the voltage drop at the end of the discharge cannot be used as the only criterion for various types of faults.

[0109] During the static stage, the self-discharge rate of normal lithium-ion batteries is low, and a large amount of power loss is generally not observed in the process of days. However, the short-circuit resistance of the micro-short-circuit battery continues to consume power, causing the battery voltage to slowly drop during the stable process.

[0110] During the charging process, due to the loss of power in the micro-short-circuited battery, the initial SOC value is generally lower than that of other batteries. At the same time, there is a phenomenon of power loss, and it will generally reach a full state later than other batteries. In a battery pack without a balancing system, it may even cause other batteries to be overcharged or the micro-short-circuited battery to age rapidly due to uneven balancing.

[0111] 2. Low capacity failure:

[0112] Different from the capacity reduction caused by normal battery aging, low-capacity battery failure is a battery failure in which the capacity decreases significantly faster than normal aging. Possible causes of low-capacity failure include slight capacity differences when the battery leaves the factory. During the same standard charge and discharge process, low-capacity individuals experience overcharge and over-discharge stages, resulting in loss of active substances. At the same time, increased inconsistency leads to different SOC cycles experienced by all batteries during the charge and discharge process, further accelerating the battery aging process. The capacity differences of all cells are further expanded, thus evolving into low-capacity failures.

[0113] Circuit characteristics of low capacity fault:

[0114] During the discharge process, due to the low battery capacity, the SOC of the low-capacity battery drops faster, the voltage also drops faster, and over-discharge is more likely to occur. The performance characteristics are similar to those of a micro-short circuit fault, but the principle is different.

[0115] During the smooth process, the SOC and voltage of the low-capacity battery remain stable without any abnormal situation.

[0116] During the charging process, due to the low battery capacity, when the same amount of electricity is charged into the series circuit, the SOC and voltage of the low-capacity battery will rise faster, making it more likely to be overcharged. The performance characteristics are opposite to those of a micro short circuit fault.

[0117] Specifically, when determining that the target marked energy storage device is a faulty energy storage device, it is necessary to perform further type analysis on the faulty energy storage device. The fault types involved in this application include low capacity faults and micro short circuit faults.

[0118] The process of detecting the fault type of the target marked energy storage device in this application is as follows:

[0119] (1) SOC abnormality judgment: Determine whether the single cell state of charge value of the target energy storage device exceeds the average state of charge value by a preset multiple within a certain charging time period. For example, whether there is a charging time period in which the single cell state of charge value of the target energy storage device exceeds 1.02 times the average state of charge value within all preset charging time periods. If so, it is determined that the target energy storage device may be overcharged. It is necessary to continue to detect the proportion of time periods in which the single cell state of charge value exceeds the average state of charge value within all preset charging time periods. If the proportion reaches the first preset proportion, for example, the proportion of the battery's SOC exceeding the average SOC in all SOC segments exceeds 3%, it is considered that the energy storage device rises rapidly in the charging segment.

[0120] (2) If the single-cell equivalent capacity of the target energy storage device is lower than the capacity average of the first preset ratio, for example, the single-cell equivalent capacity of the target energy storage device is lower than 97% of the capacity average, and the charging period rises rapidly, and there is no prohibited power-off phenomenon, then it is determined that the target energy storage device has a low-capacity fault. If the single-cell equivalent capacity is greater than or equal to the capacity average of the first preset ratio, and the ratio of the number of time periods is less than the second preset ratio, it is determined that the target energy storage device has a micro-short circuit fault.

[0121] In the above embodiment, the energy storage device is subjected to layer-by-layer single-cell fault detection based on the average state of charge value and the single-cell equivalent capacity and single-cell state of charge value of each energy storage device. The multi-layer single-cell fault detection makes the generated fault detection result more accurate.

[0122] In an exemplary embodiment, detecting an average state of charge value of the energy storage device group based on an average voltage of the energy storage device group includes:

[0123] Obtain a mapping relationship between voltage and state of charge value; based on the mapping relationship, query the state of charge value corresponding to the average voltage, and use the state of charge value corresponding to the average voltage as the average state of charge value of the energy storage device group.

[0124] Specifically, in order to improve the estimation accuracy of the state of charge value, a cubic function is used to fit the relationship between the open circuit voltage OCV and the state of charge value SOC to obtain the mapping relationship between the voltage and the state of charge value. Based on the mapping relationship obtained after fitting, the single cell SOC of the energy storage device can be estimated through the single cell voltage of the energy storage device, which serves as the basic data source for fault detection. In other words, the mapping relationship between voltage and state of charge value can not only reflect the overall SOC distribution of the energy storage device group, but also identify the inconsistency of the battery by comparing the SOC differences between different energy storage devices, thereby providing a more in-depth state analysis.

[0125] When estimating the SOC of the energy storage device through the voltage of the energy storage device, first, it is necessary to query the state of charge value corresponding to the average voltage based on the mapping relationship, and use the state of charge value corresponding to the average voltage as the average state of charge value of the energy storage device group, and based on the average state of charge value of the energy storage device group, detect the single cell state of charge value corresponding to the single cell voltage of each energy storage device in the energy storage device group.

[0126] In an exemplary embodiment, when the mapping relationship between voltage and state of charge value is represented in the form of a curve, during the charging and discharging process of the energy storage device group, the OCV-SOC curve usually shows a steep change characteristic at both ends, for example, when the SOC is close to 0% and 100%. This nonlinear characteristic may cause significant voltage fluctuations. In the first and last sections of SOC, the voltage is too sensitive to the change of SOC, and it is easy to amplify small deviations, affecting the stability of the diagnosis results. At the same time, due to the nonlinear characteristics, the error of SOC estimation based on voltage will increase significantly, which may cause erroneous fault judgment. Therefore, the single cell voltage data with SOC between 10% and 80% can be retained for analysis to avoid the interference of the data in the first and last sections of SOC on fault detection.

[0127] In an exemplary embodiment, the process of generating the mapping relationship between voltage and state of charge value includes: first, fitting the OCV-SOC mapping relationship through the total voltage and total current output by the "high-frequency mean model". The internal resistance model can be used here to simulate the ohmic internal resistance voltage drop caused by the current. At this time, the terminal voltage can be measured. The terminal voltage refers to the voltage difference between any two points in the circuit. It can be the voltage between any two endpoints. The terminal voltage is usually related to the working state of the circuit. It will change with the change of the circuit load. The open circuit voltage refers to the voltage when there is no current flowing between the two endpoints of the circuit. The terminal voltage is the internal resistance multiplied by the current plus the open circuit voltage. The average error of the terminal voltage is small. Subsequently, the third-order model is used to fit the OCV-SOC mapping relationship again. For example, the fitting OCV-SOC mapping relationship is as follows Figure 6 , the relative relationship between the terminal voltage and the OCV-SOC mapping relationship is as follows Figure 7 In addition, the open circuit voltage can also be estimated through the terminal voltage. The relationship between the open circuit voltages at different points is shown in Figure 8 As shown, according to the specified current discharge is positive and charge is negative, the OCV-SOC curve can be divided into an upper charging area and a lower discharging area.

[0128] In an exemplary embodiment, the mapping relationship between OCV and SOC can also reveal characteristic patterns related to battery fault types. For example, low-capacity batteries exhibit "fast charging and fast discharging", while micro-short-circuit batteries experience a faster decline in SOC under discharge. Through analysis based on the mapping relationship, the faulty battery can be accurately located in the subsequent process, and further associated with a specific fault type based on the change trend of SOC. This method makes up for the shortcomings of traditional data-driven fault diagnosis methods in fault type determination.

[0129] In the above embodiment, by obtaining the mapping relationship between voltage and state of charge value, the state of charge value corresponding to the average voltage can be accurately queried from the mapping relationship, and then the state of charge value corresponding to the average voltage is used as the average state of charge value of the energy storage device group, so as to more accurately detect the single state of charge value of the energy storage device based on the average state of charge value.

[0130] In an exemplary embodiment, based on the average state of charge value and the voltage difference between the cell voltage and the average voltage, detecting the cell state of charge value corresponding to the cell voltage includes:

[0131] Based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, the initial single cell state of charge value corresponding to the single cell voltage is queried from the mapping relationship; the state update step of the initial single cell state of charge value is detected; based on the average state of charge value and the state update step, the initial single cell state of charge value is updated to obtain the single cell state of charge value of the single cell voltage.

[0132] Specifically, when the mapping relationship includes a fitting relationship curve, the horizontal axis direction of the fitting relationship curve is the charge state value, and the vertical axis direction of the fitting relationship curve is the voltage, based on the average charge state value and the voltage difference between the cell voltage and the average voltage, the initial cell charge state value corresponding to the cell voltage is queried from the mapping relationship, including: determining the mean curve point corresponding to the average charge state value on the fitting relationship curve, and based on the voltage difference between the cell voltage and the average voltage, obtaining the first intersection point of the first tangent corresponding to the mean curve point and the cell voltage; obtaining the second intersection point of the vertical line of the first intersection point in the horizontal axis direction and the fitting relationship curve, and the cell charge state value corresponding to the second intersection point is the initial cell charge state value.

[0133] At this time, the initial state of charge value is corrected, and the correction process includes: based on the mapping relationship, detecting the state update step of the initial single cell state of charge value, that is, taking the derivative of the initial single cell state of charge value at the second intersection point on the fitting relationship curve to generate a target derivative, which is the target tangent slope at the second intersection point on the fitting relationship curve; obtaining the third intersection point of the oblique line passing through the mean curve point corresponding to the average state of charge value and having a slope of the target tangent slope and the single cell voltage; and using the single cell state of charge value corresponding to the third intersection point as the single cell state of charge value corresponding to the single cell voltage.

[0134] For example, the fitting relationship curve between voltage and state of charge value is as follows: Fig. 9 As shown, the horizontal axis direction of the fitting relationship curve is the state of charge value SOC, and the vertical axis direction of the fitting relationship curve is the voltage OCV.

[0135] Still Fig. 9 As shown, let the average voltage of the energy storage device group be U1, the average state of charge value be Q, determine the mean curve point corresponding to the average state of charge value on the fitting relationship curve as point A, make the first tangent L1 of the fitting relationship curve for point A, detect the first intersection point B of the first tangent L1 and the single cell voltage U2 of the energy storage device to be detected, and make a vertical line in the horizontal axis direction for the first intersection point B, obtain the second intersection point C of the vertical line and the fitting relationship curve, make the second tangent L2 of the fitting relationship curve for point C, obtain the tangent slope of the second tangent L2, make a tangent with the same tangent slope for point A, and intersect with the single cell voltage U2 of the energy storage device at the third intersection point D, then take the single cell state of charge value corresponding to point D, that is, the value of point D in the horizontal axis direction as the single cell state of charge value of the energy storage device.

[0136] In the above embodiment, based on the fitting relationship curve between voltage and state of charge value, the average state of charge value and the single cell voltage are derived, so as to obtain the accurate single cell state of charge value corresponding to the single cell voltage, and effectively solve the capacity misjudgment caused by the rapid decrease of voltage at the end of the plateau period.

[0137] In an exemplary embodiment, obtaining an average voltage of an energy storage device group includes:

[0138] The number of energy storage devices in the energy storage device group and the total voltage output by the energy storage device group at the high-frequency sampling time point are obtained; based on the total voltage and the number of energy storage devices, an average voltage of the energy storage device group is generated.

[0139] Specifically, according to the characteristics of the series circuit in the energy storage device group, the total voltage is the sum of the single-cell voltages of all energy storage device groups in the energy storage device group. Based on this, the total voltage at the output end of the energy storage device group can be measured, and the number of energy storage devices in the energy storage device group can be obtained, so as to generate the average voltage of the energy storage device group based on the total voltage and the number of energy storage devices.

[0140] Furthermore, based on the total voltage and the number of energy storage devices, generating the average voltage of the energy storage device group means dividing the total voltage by the number of energy storage devices to generate the average voltage of the energy storage device group. In other words, the high-frequency mean model in this application means that based on the characteristics of the series circuit, the average state of each energy storage device in the energy storage device group is represented by the average voltage obtained by dividing the total output voltage of the energy storage device group by the total number of energy storage devices.

[0141] In the above embodiment, by obtaining the number of energy storage devices in the energy storage device group and the total voltage output by the energy storage device group at the high-frequency sampling time point, the average voltage of the energy storage device group can be accurately generated, and the average state of each energy storage device in the energy storage device group can be accurately determined.

[0142] In an exemplary embodiment, detecting a cell equivalent capacity of an energy storage device according to a cell state of charge value includes:

[0143] The discharged power of the energy storage device is obtained; and the single-cell equivalent capacity of the energy storage device is detected according to the discharged power of the energy storage device and the single-cell charge state value.

[0144] The discharged power refers to the accumulated discharge data reported by the battery management unit.

[0145] Specifically, when SOC inconsistency is significant, it is difficult for traditional methods to directly extract representative capacity information from real-time data. In the present application, the discharged power of the energy storage device can be directly obtained, and the discharged power of the energy storage device and the single-cell state of charge value can be used to generate the single-cell equivalent capacity of the energy storage device without additional complex experiments or modeling, so as to perform online and timely fault detection.

[0146] Assuming that in the fully charged state (SOC=100%), the SOC differences between the energy storage devices are negligible, and the capacity consistency between the energy storage devices is high when fully charged, the single-cell equivalent capacity is specifically defined as: the single-cell equivalent capacity of the energy storage device = the discharged power of the energy storage device / (100-the single-cell state of charge value of the energy storage device).

[0147] The design of the single-cell equivalent capacity takes into account the characteristic that in a balanced system, when the energy storage devices are in a fully charged state (SOC is 100%), the voltages between the energy storage devices are basically the same. Therefore, it can be considered that there is no difference in the state of charge value when fully charged.

[0148] In an exemplary embodiment, the monomer equivalent capacity may be regarded as the maximum available capacity. Taking the energy storage device as a battery as an example, several representative battery monomer equivalent capacities are shown in Table 1 below.

[0149]

[0150] In the operating state of the battery 3 with SOH=92.25% on a certain day, it is highly likely that the voltage will drop abnormally.

[0151] In the above embodiment, the single cell equivalent capacity of the energy storage device is generated according to the discharged power of the energy storage device and the single cell charge state value, so that the fault of the energy storage device is accurately detected based on the single cell equivalent capacity. Compared with complex circuit models or statistical analysis, the concept of single cell equivalent capacity is simple and intuitive, easy to understand and apply.

[0152] In an exemplary embodiment, taking the energy storage device group as a battery pack as an example, the battery pack includes a plurality of batteries, and the fault detection method of the battery pack includes:

[0153] S1. Establish a high-frequency mean model.

[0154] The number of batteries in the battery pack and the total voltage output by the battery pack at the high-frequency sampling time point are obtained, and the average voltage of the battery pack is generated based on the total voltage and the number of energy storage devices.

[0155] S2. Establish a low-frequency difference model.

[0156] The voltage data of a single cell is usually obtained through a polling mechanism, which has a low acquisition frequency and a time stamp asynchronization problem. To solve this problem, the following method is proposed: align the low-frequency measurement time points of the single cell voltage to the time base of the high-frequency mean model, detect the difference between the single cell voltage of the battery and the average voltage of the battery pack on the aligned time base, and establish a low-frequency difference model.

[0157] S3. Fitting of OCV-SOC relationship.

[0158] In order to improve the estimation accuracy of the state of charge value SOC, a cubic function is used to fit the relationship between the open circuit voltage OCV and the state of charge value SOC. The OCV-SOC fitting relationship curve between the voltage and the state of charge value is obtained. Furthermore, in order to reduce the interference of the data at the beginning and end of the curve on the OCV-SOC relationship, the OCV-SOC relationship curve between SOC 10%-80% is retained.

[0159] S4. Estimation of average state of charge value.

[0160] Still Fig. 9As shown, the average state of charge value corresponding to the average voltage is detected on the OCV-SOC fitting relationship curve, and the mean curve point corresponding to the average voltage and the average state of charge value is Fig. 9 Point A in .

[0161] S5. Estimation of the charge state of a single cell.

[0162] Still Fig. 9 As shown, based on the voltage difference between the single cell voltage and the average voltage, the state of charge value difference is determined, and based on the state of charge value difference and the average state of charge value, the initial single cell state of charge value is preliminarily determined, and based on the average state of charge value, the single cell state of charge value is corrected and updated to obtain the state of charge value.

[0163] For example, let the average voltage of the energy storage device group be U1, the average state of charge value be Q, determine that the mean curve point corresponding to the average state of charge value on the fitting relationship curve is point A, make the first tangent L1 of the fitting relationship curve for point A, detect the first intersection point B of the first tangent L1 and the single cell voltage U2 of the energy storage device to be detected, and make a perpendicular line in the horizontal axis direction to the first intersection point B, obtain the second intersection point C of the perpendicular line and the fitting relationship curve, make the second tangent L2 of the fitting relationship curve for point C, obtain the tangent slope of the second tangent L2, make a tangent with the same tangent slope for point A, and intersect with the single cell voltage U2 of the energy storage device at the third intersection point D, then take the single cell state of charge value corresponding to point D, that is, the value of point D in the horizontal axis direction as the single cell state of charge value of the energy storage device.

[0164] S6. Estimation of monomer equivalent capacity.

[0165] The equivalent capacity of a single battery cell = the amount of electricity discharged from the battery / (100 - the single battery SOC).

[0166] S7, single cell battery failure detection.

[0167] Batteries whose single-cell equivalent capacity is lower than the product of a preset estimated capacity threshold and the nominal capacity are regarded as marked batteries, and batteries whose single-cell equivalent capacity is higher than or equal to the preset capacity threshold are regarded as unmarked batteries, and the capacity average of the single-cell equivalent capacity of all unmarked energy storage devices is obtained; wherein the fault detection result of the unmarked energy storage device indicates that there is no fault.

[0168] For any target marked battery among the marked batteries, when the single cell state of charge value exceeds 1.02 times the average state of charge value, if the proportion of the SOC of the battery exceeding the average SOC in all SOC segments exceeds 3%, it is considered that the battery charging segment rises rapidly.

[0169] When the equivalent capacity of a single cell is less than 97% of the average capacity and the charging section of the battery rises rapidly, it is determined that the target energy storage device has a low capacity fault; when the equivalent capacity of a single cell is greater than or equal to 97% of the average capacity and the charging section of the battery rises slowly, it is determined that the target energy storage device has a micro short circuit fault.

[0170] When a battery failure occurs, an early warning message is pushed to warn of the status of the battery cell.

[0171] S8. Generate a fault detection result of the energy storage device group based on the single-unit fault detection results of all energy storage devices.

[0172] The fault detection result of the energy storage device group includes the single fault detection result of each energy storage device in the energy storage device group. Based on the fault detection result of the energy storage device group, the energy storage device in which the fault occurs in the energy storage device group can be quickly and accurately located.

[0173] When a battery in the battery pack fails, an early warning message is pushed to warn of the battery pack status through the early warning message.

[0174] Based on the above analysis, we can see that:

[0175] 1. This application constructs a framework for fault diagnosis of different levels of battery cells in large-scale energy storage power stations. The "high-frequency mean + low-frequency difference" model is used to identify low-sampling frequency cells. The core is to effectively identify parameters and diagnose faults for a large number of devices, low sampling frequencies, and asynchronous data.

[0176] 2. Based on the different levels and types of faults involved, it is difficult to quantify the equivalent circuit parameters of the faults under different working conditions. Considering the daily practical application of energy storage power stations, the concept of equivalent capacity is proposed to describe the actual available capacity of the battery when a fault occurs.

[0177] 3. For different levels in the battery pack: batteries and battery groups, and different fault types: low-capacity fault type and micro-short circuit fault type, two levels of warning and fault are proposed respectively. At the same time, the threshold can be adjusted at any time to facilitate switching between different reference scenarios.

[0178] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0179] Based on the same inventive concept, the embodiment of the present application also provides a fault detection device for an energy storage device group for implementing the above-mentioned fault detection method for an energy storage device group. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the fault detection device for one or more energy storage device groups provided below can refer to the limitations of the fault detection method for an energy storage device group above, and will not be repeated here.

[0180] In an exemplary embodiment, Fig.10 As shown, a fault detection device for an energy storage device group is provided, comprising: a voltage acquisition module 100, an average state of charge value detection module 200, a single state of charge value and capacity detection module 300, a single fault detection module 400 and an energy storage device group fault detection module 500, wherein:

[0181] The voltage acquisition module 100 is used to acquire the average voltage of the energy storage device group at the high-frequency sampling time point, and the single-cell voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point;

[0182] An average state of charge value detection module 200, used to detect an average state of charge value of the energy storage device group based on the average voltage;

[0183] The cell state of charge value and capacity detection module 300 is used to detect the cell state of charge value corresponding to the cell voltage for each energy storage device based on the average state of charge value and the voltage difference between the cell voltage and the average voltage, and detect the cell equivalent capacity of the energy storage device according to the cell state of charge value;

[0184] A single-cell fault detection module 400 is used to generate single-cell fault detection results of all energy storage devices according to the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each energy storage device;

[0185] The energy storage device group fault detection module 500 is used to generate a fault detection result of the energy storage device group based on the single-unit fault detection results of all energy storage devices.

[0186] In one embodiment, the single-cell fault detection module 400 is further used to treat energy storage devices whose single-cell equivalent capacity is lower than a preset capacity threshold as marked energy storage devices, and treat energy storage devices whose single-cell equivalent capacity is higher than or equal to the preset capacity threshold as unmarked energy storage devices; determine that the single-cell fault detection result of the unmarked energy storage device is that there is no fault, and obtain the capacity average of the single-cell equivalent capacity of all unmarked energy storage devices; for any target marked energy storage device among the marked energy storage devices, generate the single-cell fault detection result of the target marked energy storage device according to the average state of charge value, the capacity average, and the single-cell equivalent capacity and the single-cell state of charge value of the target marked energy storage device.

[0187] In one embodiment, the single-cell fault detection module 400 is also used to obtain the ratio of the number of time periods in which the single-cell state of charge value exceeds the average state of charge value in all preset charging time periods when the single-cell state of charge value of the target-marked energy storage device exceeds the average state of charge value by a preset multiple within the target charging time period, wherein the target charging time period is any time period among all preset charging time periods; when the single-cell equivalent capacity is less than the capacity mean of the first preset ratio and the ratio of the number of time periods is greater than the second preset ratio, it is determined that the target-marked energy storage device has a low capacity fault; when the single-cell equivalent capacity is greater than or equal to the capacity mean of the first preset ratio and the ratio of the number of time periods is less than the second preset ratio, it is determined that the target-marked energy storage device has a micro-short circuit fault.

[0188] In one embodiment, the average state of charge value detection module 200 is also used to obtain a mapping relationship between voltage and state of charge value; based on the mapping relationship, query the state of charge value corresponding to the average voltage, and use the state of charge value corresponding to the average voltage as the average state of charge value of the energy storage device group.

[0189] In one embodiment, the cell state of charge value and capacity detection module 300 is also used to query the initial cell state of charge value corresponding to the cell voltage from the mapping relationship based on the average state of charge value and the voltage difference between the cell voltage and the average voltage; detect the state update step of the initial cell state of charge value; based on the average state of charge value and the state update step, update the initial cell state of charge value to obtain the cell state of charge value of the cell voltage.

[0190] In one embodiment, the cell state of charge value and capacity detection module 300 is also used to obtain the discharged power of the energy storage device; and detect the cell equivalent capacity of the energy storage device according to the discharged power of the energy storage device and the cell state of charge value.

[0191] Each module in the fault detection device of the energy storage device group can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0192] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as the average voltage of the energy storage device group at the high-frequency sampling time point, and the single voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a fault detection method for an energy storage device group is implemented.

[0193] Those skilled in the art will understand that Fig.11 The structure shown in the figure is a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0194] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0195] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0196] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0197] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0198] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0199] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for detecting a fault of an energy storage device group, characterized in that: The method comprises: Obtaining the average voltage of the energy storage device group at the high-frequency sampling time point, and the single-cell voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point; Based on the average voltage, detecting an average state of charge value of the energy storage device group; For each of the energy storage devices, based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, detect the single cell state of charge value corresponding to the single cell voltage, and detect the single cell equivalent capacity of the energy storage device according to the single cell state of charge value; Generate single-cell fault detection results of all the energy storage devices according to the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each of the energy storage devices; Based on the single-unit fault detection results of all the energy storage devices, a fault detection result of the energy storage device group is generated.

2. The method according to claim 1, characterized in that The generating of single-cell fault detection results of all the energy storage devices according to the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each of the energy storage devices includes: The energy storage device whose monomer equivalent capacity is lower than the preset capacity threshold is regarded as a marked energy storage device, and the energy storage device whose monomer equivalent capacity is higher than or equal to the preset capacity threshold is regarded as a non-marked energy storage device; Determine that a single-unit fault detection result of the non-marked energy storage device is that there is no fault, and obtain a capacity average of the single-unit equivalent capacities of all the non-marked energy storage devices; For any target marked energy storage device among the marked energy storage devices, a single cell fault detection result of the target marked energy storage device is generated according to the average state of charge value, the capacity mean, and the single cell equivalent capacity and single cell state of charge value of the target marked energy storage device.

3. The method according to claim 2, characterized in that The generating a single-cell fault detection result of the target energy storage device according to the average state of charge value, the capacity mean value, and the single-cell equivalent capacity and single-cell state of charge value of the target energy storage device includes: When the single state of charge value of the target energy storage device exceeds the average state of charge value by a preset multiple within the target charging time period, obtaining the ratio of the number of time periods in which the single state of charge value exceeds the average state of charge value within all preset charging time periods, wherein the target charging time period is any time period among all preset charging time periods; When the monomer equivalent capacity is less than the capacity mean of the first preset ratio and the time period quantity ratio is greater than the second preset ratio, it is determined that the target marked energy storage device has a low capacity fault; When the monomer equivalent capacity is greater than or equal to the capacity average of the first preset ratio, and the time period quantity ratio is less than the second preset ratio, it is determined that the target marked energy storage device has a micro short circuit fault.

4. The method according to claim 1, characterized in that: The detecting the average state of charge value of the energy storage device group based on the average voltage of the energy storage device group includes: Obtaining a mapping relationship between voltage and state of charge value; Based on the mapping relationship, the state of charge value corresponding to the average voltage is queried, and the state of charge value corresponding to the average voltage is used as the average state of charge value of the energy storage device group.

5. The method according to claim 4, characterized in that The detecting the cell state of charge value corresponding to the cell voltage based on the average state of charge value and the voltage difference between the cell voltage and the average voltage includes: Based on the average state of charge value and the voltage difference between the cell voltage and the average voltage, querying the initial cell state of charge value corresponding to the cell voltage from the mapping relationship; Detecting a state update step length of the initial single cell state of charge value; Based on the average state of charge value and the state update step, the initial cell state of charge value is updated to obtain the cell state of charge value of the cell voltage.

6. The method according to claim 1, characterized in that The detecting the monomer equivalent capacity of the energy storage device according to the monomer state of charge value includes: Obtaining the amount of electricity discharged from the energy storage device; The single cell equivalent capacity of the energy storage device is detected according to the discharged electricity of the energy storage device and the single cell charge state value.

7. A fault detection device for an energy storage device group, characterized in that: The device comprises: A voltage acquisition module, used to acquire the average voltage of the energy storage device group at the high-frequency sampling time point, and the single-cell voltage of each energy storage device in the energy storage device group at the low-frequency sampling time point corresponding to the high-frequency sampling time point; An average state of charge value detection module, used to detect an average state of charge value of the energy storage device group based on the average voltage; A cell state of charge value and capacity detection module, for detecting, for each of the energy storage devices, a cell state of charge value corresponding to the cell voltage based on the average state of charge value and a voltage difference between the cell voltage and the average voltage, and detecting a cell equivalent capacity of the energy storage device according to the cell state of charge value; A single-cell fault detection module, used to generate single-cell fault detection results of all the energy storage devices according to the average state of charge value, and the single-cell equivalent capacity and single-cell state of charge value of each of the energy storage devices; The energy storage device group fault detection module is used to generate a fault detection result of the energy storage device group based on the single-unit fault detection results of all the energy storage devices.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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