Energy storage device group fault detection method and device, computer device and readable storage medium

Abstract

The application relates to a fault detection method and device of an energy storage device group, computer equipment and a readable storage medium. The method comprises the following steps: acquiring 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; detecting an average state-of-charge value of the energy storage device group based on the average voltage; for each energy storage device, detecting a single-cell state-of-charge value corresponding to the single-cell voltage based on the average state-of-charge value and a voltage difference between the single-cell voltage and the average voltage, and detecting a single-cell equivalent capacity of the energy storage device according to the single-cell state-of-charge value; generating a single-cell fault detection result of all the energy storage devices according to the average state-of-charge value and the single-cell equivalent capacity and the single-cell state-of-charge value of each energy storage device; and generating a fault detection result of the energy storage device group. The method can accurately detect the fault of the energy storage device group.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fault detection, in particular to a fault detection method and device for a storage device group, a computer device, a computer readable storage medium and a computer program product. BACKGROUND

[0002] Fault detection is a technology frequently used in daily production and life. In the use of a storage device group, such as a battery group, fault detection is also an extremely important link. Through fault detection of the storage device group, the use state of the storage device can be detected in a timely manner, and the faulty storage device can be replaced in time.

[0003] At present, the method for detecting faults of the storage device group is usually a state inconsistency estimation method based on a mean-difference model, that is, a dynamic SOC (State of Charge, battery state of charge value) adjustment strategy is proposed, the SOC of the storage device group is intermittently reduced to 30%, voltage measurement is performed, such as average OCV (Opencircuit voltage, open circuit voltage) measurement of the storage device group, and single OCV measurement of each storage device in the storage device group is performed one by one, and the difference between the estimated single OCV of the single storage device and the average voltage is mapped to the SOC difference by using the SOC-OCV difference curve, and finally, based on the battery SOC difference, the fault of the storage device group is directly detected.

[0004] However, the current fault detection method of the storage device group has the problem of inaccuracy. SUMMARY

[0005] Therefore, it is necessary to provide an accurate fault detection method, device, computer device, computer readable storage medium and computer program product for a storage device group in view of the above technical problems.

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

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

[0008] detecting an average state of charge value of the storage device group based on the average voltage;

[0009] for each storage device, detecting a single state of charge value corresponding to the single voltage based on the average state of charge value and a voltage difference between the single voltage and the average voltage, and detecting a single equivalent capacity of the storage device according to the single state of charge value;

[0010] generate the cell fault detection results of all the energy storage devices according to the average state of charge value and the cell equivalent capacity and the cell state of charge value of each energy storage device;

[0011] generate the fault detection result of the energy storage device group based on the cell fault detection results of all the energy storage devices.

[0012] In one of the embodiments, the cell fault detection results of all the energy storage devices are generated according to the average state of charge value and the cell equivalent capacity and the cell state of charge value of each energy storage device, including:

[0013] the energy storage device with the cell equivalent capacity lower than the preset capacity threshold is taken as a marked energy storage device, and the energy storage device with the cell equivalent capacity higher than or equal to the preset capacity threshold is taken as a non-marked energy storage device;

[0014] the cell fault detection result of the non-marked energy storage device is determined as no fault, and the capacity average of the cell equivalent capacity of all the non-marked energy storage devices is obtained;

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

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

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

[0018] in the case that the cell equivalent capacity is less than the first preset proportion of the capacity average and the time period number proportion is greater than the second preset proportion, it is determined that the target marked energy storage device has a low capacity fault;

[0019] in the case that the cell equivalent capacity is greater than or equal to the first preset proportion of the capacity average and the time period number proportion is less than the second preset proportion, it is determined that the target marked energy storage device has a micro-short circuit fault.

[0020] In one of the embodiments, the average state of charge value of the energy storage device group is detected based on the average voltage of the energy storage device group, including:

[0021] a mapping relationship between the voltage and the state of charge value is obtained;

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

[0023] In one of the embodiments, based on the average state of charge value and the voltage difference between the single cell voltage and the average voltage, the single cell voltage corresponding single cell state of charge value is detected, including:

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

[0025] The state update step of the initial single cell state of charge value is detected;

[0026] 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.

[0027] In one of the embodiments, according to the single cell state of charge value, the single cell equivalent capacity of the energy storage device is detected, including:

[0028] The discharged electric quantity of the energy storage device is obtained;

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

[0030] In a second aspect, the application further provides a fault detection device of an energy storage device group, including:

[0031] The voltage acquisition module 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;

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

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

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

[0035] The energy storage equipment group fault detection module is used to generate the fault detection results of the energy storage equipment group based on the individual fault detection results of all energy storage equipment.

[0036] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0037] The average voltage of the energy storage device group at the high-frequency sampling time point and the individual 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 are obtained.

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

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

[0040] Based on the average state of charge value, and the single equivalent capacity and single state of charge value of each energy storage device, the single fault detection results of all energy storage devices are generated.

[0041] Based on the individual fault detection results of all energy storage devices, the fault detection results of the energy storage device group are generated.

[0042] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0043] The average voltage of the energy storage device group at the high-frequency sampling time point and the individual 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 are obtained.

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

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

[0046] Based on the average state of charge value, and the single equivalent capacity and single state of charge value of each energy storage device, the single fault detection results of all energy storage devices are generated.

[0047] Based on the individual fault detection results of all energy storage devices, the fault detection results of the energy storage device group are generated.

[0048] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0049] The average voltage of the energy storage device group at the high-frequency sampling time point and the individual 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 are obtained.

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

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

[0052] Based on the average state of charge value, and the single equivalent capacity and single state of charge value of each energy storage device, the single fault detection results of all energy storage devices are generated.

[0053] Based on the individual fault detection results of all energy storage devices, the fault detection results of the energy storage device group are generated.

[0054] The aforementioned fault detection method, apparatus, computer equipment, computer-readable storage medium, and computer program product for energy storage device groups acquire the average voltage of the energy storage device group at high-frequency sampling time points and acquire the individual voltage of each energy storage device in the energy storage device group at the corresponding low-frequency sampling time points. That is, effective voltage detection is performed on a large number of individual energy storage devices on an aligned time reference, thereby improving the accuracy of voltage acquisition. Furthermore, during the fault detection process, based on the average voltage, the average state of charge (SOC) value of the energy storage device group is detected. For each energy storage device, based on the average SOC value and the voltage difference between the individual voltage and the average voltage, the individual SOC value corresponding to the individual voltage is detected. Based on the individual SOC value, the individual equivalent capacity of the energy storage device is detected. This achieves accurate quantification of the fault detection parameters of the energy storage devices through the average SOC value, the individual equivalent capacity of each energy storage device, and the individual SOC value, so as to accurately generate the individual fault detection results of the energy storage devices. Finally, the individual fault detection results of all energy storage devices are summarized to accurately generate the fault detection results of the energy storage device group. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

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

[0057] Figure 2 This is a flowchart illustrating a fault detection method for an energy storage device group in one embodiment;

[0058] Figure 3 This is a comparative schematic diagram showing the measurement of individual unit voltages of each energy storage device under two different methods in one embodiment;

[0059] Figure 4 This is a comparative schematic diagram of the individual voltage distribution of the energy storage device under two different methods in one embodiment;

[0060] Figure 5 This is a flowchart illustrating a fault detection method for an energy storage device group in another embodiment;

[0061] Figure 6 This is a schematic diagram illustrating the mapping relationship between voltage and state of charge values ​​in a specific application embodiment.

[0062] Figure 7 This is a schematic diagram illustrating the mapping relationship between voltage and state of charge values, and the relative relationship between the two, in a specific application embodiment.

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

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

[0065] Figure 10 This is a structural block diagram of a fault detection device for an energy storage device group in one embodiment;

[0066] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0068] The fault detection method for energy storage equipment groups provided in this application embodiment can be applied locally at the terminal of an energy storage power station, or it can be applied to other applications such as... Figure 1The application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 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 the fault detection operation of the energy storage device group on the terminal 102, and the terminal 102 sends a fault detection request to the server 104 in response to the fault detection operation. 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; based on the average voltage, detect the 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, detect the single state of charge value corresponding to the single voltage, and according to the single state of charge value, detect the single equivalent capacity of the energy storage device; 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, generate the single fault detection result of all energy storage devices; based on the single fault detection result of all energy storage devices, generate the fault detection result of the energy storage device group. Further, the fault detection result can be pushed to the terminal 102 and displayed to the user by the terminal 102.

[0070] Among them, the terminal 102 can be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things device can be a smart speaker, a smart TV, a smart air conditioner, a smart vehicle device, a projection device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be a standalone physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0071] In an exemplary embodiment, as Figure 2 shown, a fault detection method for an energy storage device group is provided. The method is applied to the server 104 in Figure 1 for example. Among them:

[0072] S100, 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.

[0073] The energy storage device group can be various types of battery groups, and the energy storage device group includes a plurality of energy storage devices. The high-frequency sampling time point refers to a time point of sampling at a higher frequency. Since the total voltage and current are measured at the output end of the energy storage device group, high-frequency sampling can be realized at the output end of the energy storage device group to form a high-frequency average model, which provides rapid evaluation capability for the state of the energy storage device group. The low-frequency sampling time point refers to a time point of sampling at a lower frequency. The voltage in the present application is the open circuit voltage OCV.

[0074] Specifically, in the energy storage power station, due to the large number of single cells of the energy storage device and high bus load, it is difficult to realize high-frequency synchronous sampling of all cell voltages. The traditional polling mechanism for voltage measurement of the energy storage device usually causes a time difference of tens of seconds, which is not conducive to real-time construction of the voltage state model of the energy storage device, that is, the voltage of the energy storage device has the problems of low sampling frequency and different sampling time stamps.

[0075] To solve the above problems, the present application proposes an improved fault detection method for the energy storage device group combining high-frequency average 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 large, and high-frequency sampling of each energy storage device in the energy storage device group cannot be realized. The establishment of the high-frequency average model makes it necessary to measure the output of the energy storage device group, which can ensure the measurement frequency and does not exist cumulative error of all energy storage device voltages. Through the high-frequency average model at a high frequency, the average voltage of the energy storage device group is obtained at the high-frequency sampling time point, and the state 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 state of all energy storage devices in the energy storage device group.

[0077] Furthermore, the low-frequency measurement time points of the energy storage device's voltage are aligned to the time reference of the high-frequency averaging model. This alignment refers to directly finding the high-frequency sampling time point corresponding to the low-frequency sampling time point. On the aligned time reference, the difference between the individual voltage of the energy storage device and the average voltage of the energy storage device group is calculated, establishing a low-frequency difference model. This difference model can capture the state distribution characteristics among energy storage devices, supplementing the overall analytical capabilities of the high-frequency averaging model, thereby achieving a more refined individual state assessment. In addition, the individual voltage of the energy storage device in the low-frequency difference model can be compared with the values ​​of the high-frequency averaging model at the low-frequency sampling points to reconstruct the approximate operating state of any individual energy storage device. In practical applications, since the current input and output of energy storage power stations are relatively smooth, the low-frequency difference model is sufficient to describe and reconstruct the energy storage device at any low-frequency sampling time.

[0078] In one exemplary embodiment, such as Figure 3 As shown, the left side displays the individual voltages of each energy storage device under the original polling measurement method, while the right side displays the voltages after processing with the "high-frequency mean + low-frequency difference" model, resulting in significant differences in voltage performance between different energy storage devices. Furthermore, a comparison was made between the individual voltage distributions of each energy storage device under the original polling measurement method and the measurement method of this application, as well as the individual voltage distributions under the "high-frequency mean + low-frequency difference" model. The results are as follows. Figure 4 As can be seen from the box plot, the individual voltage distributions of each energy storage device are very similar under the original polling measurement method. However, after processing with the "high frequency mean + low frequency difference" model, the voltage distributions of different energy storage devices show their own characteristics.

[0079] S200 detects the average state of charge (SOC) value of an energy storage device group based on average voltage.

[0080] Among them, the state of charge (SOC) value is the ratio of the remaining capacity of an energy storage device after a period of use or long-term disuse to its capacity when fully charged. It is usually expressed as a percentage and its value ranges from 0 to 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 an energy storage device group can approximately reflect the overall state of all energy storage devices in the group. Therefore, the average state of charge of the energy storage device group can be detected based on its average voltage.

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

[0083] The monomer state of charge value refers to the state of charge value of the energy storage device at a specific time point, and the equivalent capacity reflects the effective discharge capacity that the energy storage device can provide under the existing fault condition and working cycle.

[0084] Specifically, in the prior art, the state of charge value corresponding to the voltage difference is obtained directly based on the voltage difference between the monomer voltage and the average voltage, and the monomer state of charge value corresponding to the monomer 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 makes certain modifications to accurately detect the monomer state of charge value corresponding to the monomer voltage. More specifically, this modification can be achieved by taking the derivative of the monomer state of charge value under the original detection method and modifying based on the derivative result.

[0085] Further, during the online use of the energy storage device group, as the use time elapses, the energy storage device will gradually show a trend of capacity degradation and health state deterioration. For the energy storage device, due to the complexity of the energy storage device group and the limitations of online operation, it is a great challenge to directly quantify the fault state and health degree of the monomer energy storage device. The internal resistance model and capacity degradation are very complex, and the change of the battery internal resistance cannot fully reflect the availability of the capacity, while the change of the 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 degradation and micro-short circuit. In addition, different capacity batteries have different sensitivities to micro-short circuit resistance, so in actual application, a more intuitive and easy-to-operate fault diagnosis standard is needed. Therefore, the concept of "equivalent capacity" is proposed to directly quantify the available capacity of the energy storage device under the current fault state. Further, in the present application, the monomer equivalent capacity of the energy storage device can be detected by the monomer state of charge value of the energy storage device.

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

[0088] Specifically, the average state of charge value, and the monomer equivalent capacity and the monomer 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, and the monomer equivalent capacity and the monomer state of charge value of each energy storage device, the energy storage device is subjected to fault detection, and the monomer fault detection results of all energy storage devices are generated. That is, for any energy storage device, the monomer fault detection result is generated according to the average state of charge value, the monomer equivalent capacity and the monomer state of charge value.

[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 grading method is proposed, according to the severity of the fault, which is divided into serious faults and minor faults / warnings. When the fault severity of the energy storage device is a serious fault, it is directly reported to the user for danger notification, and when the fault severity of the energy storage device is a minor fault / warning, internal fault handling or further monitoring of potentially dangerous equipment can be performed.

[0090] S500, based on the single fault detection results of all energy storage devices, generating the fault detection result of the energy storage device group.

[0091] Specifically, in actual use, in addition to the need to locate and evaluate the fault of the single energy storage device, it is also necessary to comprehensively analyze the fault degree of the entire energy storage device group. The fault detection result of the energy storage device group is actually a summary of the single fault detection results of all energy storage devices. 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 that has failed in the energy storage device group can be quickly and accurately located.

[0092] In the above-mentioned fault detection method of the energy storage device group, the average voltage of the energy storage device group is obtained at the high-frequency sampling time point, and the single voltage of each energy storage device in the energy storage device group is obtained at the low-frequency sampling time corresponding to the high-frequency sampling time point, that is, a large number of single energy storage devices are effectively voltage detected on the same time reference, thereby improving the accuracy of voltage acquisition. Further, in the fault detection process, 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 voltage and the average voltage, the single state of charge value corresponding to the single voltage is detected, and the single equivalent capacity of the energy storage device is detected according to the single state of charge value, thereby accurately quantifying the fault detection parameters of the energy storage device through the average state of charge value and the single equivalent capacity and the single state of charge value of each energy storage device, so as to accurately generate the single fault detection result of the energy storage device, and then summarize the single fault detection results of all energy storage devices to accurately generate the fault detection result of the energy storage device group.

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

[0094] S420, the energy storage device with a single equivalent capacity lower than the preset capacity threshold is regarded as a marked energy storage device, and the energy storage device with a single equivalent capacity higher than or equal to the preset capacity threshold is regarded as a non-marked energy storage device.

[0095] S440, determine that the single cell fault detection result of the non-marked energy storage device is no fault, and obtain the capacity mean value of the single cell equivalent capacity of all non-marked energy storage devices.

[0096] S460, for any target marked energy storage device in 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 mean value, and the single cell equivalent capacity and the single cell state of charge value of the target marked energy storage device.

[0097] 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 detecting the single cell fault detection result of the energy storage device in the present application, first, the capacity threshold of the energy storage device is determined, that is, it is determined whether the single cell equivalent capacity of the single cell energy storage device is lower than the preset capacity threshold, wherein the preset capacity threshold is the product of the pre-warning capacity threshold and the nominal capacity. The energy storage device with a single cell equivalent capacity lower than the preset capacity threshold is a marked energy storage device, which is a fault energy storage device that needs to be reported, and the energy storage device with a single cell equivalent capacity higher than or equal to the preset capacity threshold is a non-marked energy storage device, and the single cell fault detection result of the non-marked energy storage device is no fault, which is a normal energy storage device. At this time, the capacity mean value of the single cell equivalent capacity of all non-marked energy storage devices is obtained.

[0099] Further, for any target marked energy storage device in the marked energy storage devices, the target marked energy storage device is comprehensively detected 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 marked energy storage device, and the single cell fault detection result of the target marked energy storage device is generated. At this time, the single cell fault detection result is the fault type of the target marked energy storage device.

[0100] In the above embodiment, the energy storage devices are divided into normal batteries and fault batteries by the single cell equivalent capacity, the single cell fault detection of the energy storage devices is realized, and then the fault type analysis of the fault batteries can be more accurate without the need to analyze the fault type of all batteries, thereby improving the efficiency and accuracy of fault detection.

[0101] In one exemplary embodiment, generating the single cell fault detection result of the target marked energy storage device 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 marked energy storage device includes:

[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 proportion 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; in a case where the single cell equivalent capacity is less than the capacity mean value of the first preset proportion and the proportion of the number of time periods is greater than the second preset proportion, it is determined that the target marked energy storage device has a low capacity fault; in a case where the single cell equivalent capacity is greater than or equal to the capacity mean value of the first preset proportion and the proportion of the number of time periods is less than the second preset proportion, 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 in all preset charging time periods.

[0104] Taking the energy storage device as a battery as an example, the corresponding principles and fault characteristics of the low capacity fault and the micro short circuit fault are as follows:

[0105] 1. Micro short circuit fault:

[0106] Unlike the short circuit fault, the micro short circuit fault refers to a short circuit fault with a large equivalent short circuit resistance. The micro short circuit fault usually does not have obvious leakage or temperature rise phenomenon within a time period of days. The early stage of both internal short circuit fault and external short circuit fault can be referred to as micro short circuit. The internal short circuit fault is caused by conductive particles or foreign matter inside the battery, resulting in the formation of an abnormal weak current path between the positive and negative electrodes, and the inducing causes include lithium dendrite overgrowth, damaged separator, electrolyte separation, etc. The external short circuit fault is mainly caused by the short circuit of the external circuit, including positive and negative electrode solder contact, loose and exposed wire, etc.

[0107] Electrical characteristics of micro short circuit fault:

[0108] During discharging, the SOC of the micro short circuit battery decreases faster than other batteries due to the continuous consumption of the short circuit resistance. Lithium iron phosphate batteries have a unique platform period characteristic. During the platform period, even if the battery SOC has some differences, the voltage difference is still small. However, the micro short circuit battery has lower power, and it is easy to leave the platform period during the discharging stage, resulting in a sharp drop in voltage. It is also worth noting that all lithium iron phosphate batteries will have a rapid voltage drop process at the end of the platform period, so the voltage drop at the end of discharging cannot be used as the only criterion for various faults.

[0109] During the standing stage, the self-discharge rate of the current normal lithium ion battery is low, and a large amount of power loss is generally not observed in a process of days. However, the short circuit resistance of the micro short circuit battery continuously consumes power, resulting in a slow decline in the voltage of the battery during the stable process.

[0110] In the charging process, the initial value of the SOC of the micro-short-circuit battery is relatively lower than that of other batteries due to the loss of electric quantity, and the phenomenon of electric quantity loss exists, and it will generally reach the full state later than other batteries. In a battery pack without an equalization system, it may even cause other batteries to overcharge or the micro-short-circuit battery to age rapidly due to uneven equalization.

[0111] 2. Low-capacity failure:

[0112] Unlike the capacity reduction caused by normal battery aging, low-capacity failure of the battery is a battery failure in which the capacity reduction is significantly faster than normal aging. Possible causes of low-capacity failure include slight capacity differences at the time of battery factory shipment. In the same standard charging and discharging process, the low-capacity individual experiences overcharging and over-discharging stages, resulting in loss of active material. At the same time, the inconsistency increases, causing all batteries to experience different SOC cycles during charging and discharging, further accelerating the aging process of the battery, and the capacity difference of all battery cells is further expanded, thus evolving into a low-capacity failure.

[0113] Circuit characteristics of low-capacity failure:

[0114] In the discharging process, due to the low capacity of the battery, the SOC of the low-capacity battery drops faster, and the voltage also drops faster, making it more likely to over-discharge. The performance characteristics are similar to micro-short-circuit failure, but the principles are different.

[0115] In the steady process, the SOC and voltage of the low-capacity battery remain stable and do not appear abnormal.

[0116] In the charging process, due to the low capacity of the battery, when the same electric quantity is charged into the series circuit, the SOC and voltage of the low-capacity battery also rise faster, making it more likely to overcharge. The performance characteristics are opposite to those of micro-short-circuit failure.

[0117] Specifically, when determining that the target marked energy storage device is a faulty energy storage device, further type analysis of the faulty energy storage device is required. The failure types involved in this application include low-capacity failure and micro-short-circuit failure.

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

[0119] (1) SOC abnormality determination: determine whether 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 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 marked energy storage device exceeds 1.02 times the average state of charge value within all preset charging time periods. If it exceeds, it is determined that the target marked energy storage device may have overcharging, and the proportion 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 needs to be continuously detected. If the proportion reaches a first preset proportion, for example, the proportion of the SOC of the battery 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 marked energy storage device is lower than the first preset proportion of the capacity mean value, for example, the single cell equivalent capacity of the target marked energy storage device is lower than 97% of the capacity mean value, and the charging segment rises rapidly, there is no phenomenon of being forced to power off, it is determined that the target marked energy storage device has a low capacity fault. In the case where the single cell equivalent capacity is greater than or equal to the first preset proportion of the capacity mean value and the time period number proportion is less than the second preset proportion, it is determined that the target marked energy storage device has a micro-short circuit fault.

[0121] In the above embodiment, the energy storage device is detected layer by layer for single cell fault by the average state of charge value and the single cell equivalent capacity and the 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, the average state of charge value of the energy storage device group is detected based on the average voltage of the energy storage device group, comprising:

[0123] Obtaining a mapping relationship between voltage and state of charge value; based on the mapping relationship, querying the state of charge value corresponding to the average voltage, and taking 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 SOC, and the mapping relationship between the voltage and the state of charge value is obtained. 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 is used as the basic data source for fault detection. That is, the mapping relationship between the voltage and the state of charge value can not only reflect the SOC distribution of the energy storage device group as a whole, but also can identify the inconsistency of the battery by comparing the SOC difference between different energy storage devices, thereby providing more in-depth state analysis.

[0125] When estimating the SOC of the energy storage device based on the voltage of the energy storage device, first, the average voltage corresponding state of charge value is queried based on the mapping relationship, and the average voltage corresponding state of charge value is taken as the average state of charge value of the energy storage device group, and the single cell voltage corresponding single cell state of charge value of each energy storage device in the energy storage device group is detected based on the average state of charge value of 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 presents the characteristics of steep change at both ends, such as SOC close to 0% and 100%. This nonlinear characteristic can cause significant voltage fluctuations. In the first and last sections of SOC, the voltage is too sensitive to the change of SOC, which easily amplifies the slight deviation and affects the stability of the diagnosis result. At the same time, due to the nonlinear characteristic, the SOC estimation error based on voltage will increase significantly, which may cause false fault judgment. Therefore, the single cell voltage data of SOC between 10% and 80% can be reserved for analysis to avoid the interference of the data of the first and last sections of SOC to fault detection.

[0127] In an exemplary embodiment, the generation process of the mapping relationship between voltage and state of charge value includes: first, fitting the OCV-SOC mapping relationship by the total voltage and total current output by the "high-frequency mean model". Here, the internal resistance model can be used to simulate the Ohmic internal resistance voltage drop caused by the current. At this time, the terminal voltage can be measured, which 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, and it will change with the change of the circuit load. The open circuit voltage refers to the voltage between the two endpoints of the circuit when there is no current flowing. The terminal voltage is the internal resistance multiplied by the current plus the open circuit voltage, and the average error of the terminal voltage is small. Subsequently, the OCV-SOC mapping relationship is fitted again by using a three-order model, for example, the OCV-SOC mapping relationship is fitted as follows Figure 6 The relative relationship between the terminal voltage and the OCV-SOC mapping relationship is shown in Figure 7 In addition, the open circuit voltage can also be estimated by the terminal voltage. The relationship of open circuit voltages of different points is shown in Figure 8 According to the regulation that the discharge is positive and the charging is negative, the OCV-SOC curve can be divided into the charging area on the upper side and the discharging area on the lower side.

[0128] In an example embodiment, the mapping relationship between OCV-SOC can also reveal characteristic patterns related to battery fault types, for example, a low-capacity battery behaves as "fast charging, fast discharging", and a micro-short circuit type battery shows a faster decline in SOC under discharging. Through analysis based on the mapping relationship, the faulty battery can be accurately located in the subsequent, and further correlated to a specific fault type according to the trend of the 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 taken as the average state of charge value of the energy storage device group, so that the single state of charge value of the energy storage device can be more accurately detected based on the average state of charge value.

[0130] In an example embodiment, based on the average state of charge value and the voltage difference between the single voltage and the average voltage, the single state of charge value corresponding to the single voltage is detected, including:

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

[0132] Specifically, in the case where the mapping relationship includes a fitting relationship curve, the horizontal direction of the fitting relationship curve is the state of charge value, and the vertical direction of the fitting relationship curve is the voltage, based on the average state of charge value and the voltage difference between the single voltage and the average voltage, the initial single state of charge value corresponding to the single voltage is queried from the mapping relationship, including: determining the mean curve point corresponding to the average state of charge value on the fitting relationship curve, and based on the voltage difference between the single voltage and the average voltage, obtaining the first intersection point of the first tangent line corresponding to the mean curve point and the single voltage; obtaining the second intersection point of the vertical line of the first intersection point in the horizontal direction and the fitting relationship curve, and the single state of charge value corresponding to the second intersection point is the initial single state of charge 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, the initial single cell state of charge value of the second intersection point on the fitting relationship curve is differentiated to generate the target derivative, that is, the target tangent slope of the second intersection point on the fitting relationship curve; obtaining the average curve point corresponding to the average state of charge value, and the slope of the third intersection point of the single cell voltage with the slope of the target tangent slope; the single cell state of charge value corresponding to the third intersection point is taken 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 shown in Figure 9 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] As shown in Figure 9 The average voltage of the energy storage device group is U1, the average state of charge value is Q, the average curve point corresponding to the average state of charge value on the fitting relationship curve is point A, the first tangent L1 of the fitting relationship curve is drawn through point A, the first intersection point B of the first tangent L1 and the single cell voltage U2 of the energy storage device to be detected is detected, and the vertical line in the horizontal axis direction is drawn through the first intersection point B, the second intersection point C of the vertical line and the fitting relationship curve is obtained, the second tangent L2 of the fitting relationship curve is drawn through point C, the tangent slope of the second tangent L2 is obtained, and the tangent with the same tangent slope as the energy storage device is drawn through point A. The single cell state of charge value corresponding to point D, that is, the value of point D in the horizontal axis direction, is taken 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 differentiated, the accurate single cell state of charge value corresponding to the single cell voltage can be obtained, and the capacity misjudgment caused by the rapid reduction of the terminal voltage at the end of the platform period can be effectively solved.

[0137] In an exemplary embodiment, the average voltage of the energy storage device group is obtained, including:

[0138] The number of energy storage devices of 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, the 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 voltages of all energy storage devices in the energy storage device group. Based on this, the average voltage of the energy storage device group can be generated based on the total voltage and the number of energy storage devices in the energy storage device group by measuring the total voltage at the output end of the energy storage device group and obtaining the number of energy storage devices in the energy storage device group.

[0140] Further, generating the average voltage of the energy storage device group based on the total voltage and the number of energy storage devices means dividing the total voltage by the number of energy storage devices to generate the average voltage of the energy storage device group. That is, the high-frequency average model in the present application means that based on the characteristics of the series circuit, the average voltage obtained by dividing the total output voltage of the energy storage device group by the total number of energy storage devices is used to represent the average state of each energy storage device in the energy storage device group.

[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 the single equivalent capacity of the energy storage device according to the single state of charge value comprises:

[0143] Obtaining the discharged electric quantity of the energy storage device; and detecting the single equivalent capacity of the energy storage device according to the discharged electric quantity of the energy storage device and the single state of charge value.

[0144] The discharged electric quantity is the cumulative discharge data reported by the battery management unit.

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

[0146] It is assumed that the SOC difference between the energy storage devices is negligible at full charge (SOC = 100%), and the capacity consistency between the energy storage devices is high at full charge. Therefore, the single equivalent capacity is defined as: single equivalent capacity of the energy storage device = discharged electric quantity of the energy storage device / (100 - single state of charge value of the energy storage device).

[0147] The design of the single equivalent capacity takes into account the characteristics that the voltages of the energy storage devices are basically consistent at full charge (SOC is 100%) in a balanced system. Therefore, it can be considered that there is no difference in the state of charge value at full charge.

[0148] In an exemplary embodiment, the equivalent capacity of a single cell can be regarded as the maximum usable capacity. Taking the energy storage device as a battery as an example, the equivalent capacity of several representative battery cells is shown in Table 1 below.

[0149]

[0150] For battery 3 with SOH=92.25%, it is highly likely to experience an abnormal voltage drop on a certain day.

[0151] In the above embodiments, the equivalent capacity of a single energy storage device is generated based on the discharged electricity and the state of charge value of the single device. Based on the equivalent capacity of the single device, the faults of the energy storage device can be accurately detected. Compared with complex circuit models or statistical analysis, the concept of equivalent capacity of a single device is simple, intuitive and easy to understand and apply.

[0152] In an exemplary embodiment, taking a battery pack as an example of an energy storage device group, the battery pack includes several batteries, and the fault detection method for 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 a high-frequency sampling time point are obtained. Based on the total voltage and the number of energy storage devices, the average voltage of the battery pack is generated.

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

[0156] Individual cell voltage data is typically acquired through a polling mechanism, resulting in a low acquisition frequency and timestamp asynchrony. To address this issue, the following method is proposed: align the low-frequency measurement time points of individual cell voltages to the time reference of a high-frequency averaging model. On this aligned time reference, detect the difference between the individual cell voltage and the average voltage of the battery pack, thus establishing a low-frequency difference model.

[0157] Fitting of S3 and OCV-SOC relationships.

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

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

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

[0161] S5, monomer state of charge value estimation.

[0162] As shown in Figure 9 , based on the voltage difference between the monomer 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 monomer state of charge value is preliminarily determined. Based on the average state of charge value, the monomer 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, and 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. Draw the first tangent L1 of the fitting relationship curve through point A. Detect the first intersection point B of the first tangent L1 and the monomer voltage U2 of the energy storage device to be detected. Draw a vertical line in the horizontal axis direction through the first intersection point B. Obtain the second intersection point C of the vertical line and the fitting relationship curve. Draw the second tangent L2 of the fitting relationship curve through point C. Obtain the tangent slope of the second tangent L2. Draw a tangent with the same tangent slope as point A, which intersects the monomer voltage U2 of the energy storage device at the third intersection point D. Then the monomer state of charge value corresponding to point D, that is, the value of point D in the horizontal axis direction, is taken as the monomer state of charge value of the energy storage device.

[0164] S6, monomer equivalent capacity estimation.

[0165] The monomer equivalent capacity of the battery = the discharged capacity of the battery / (100 - the monomer SOC of the battery).

[0166] S7, monomer battery fault detection.

[0167] The battery with a monomer equivalent capacity lower than the product of the preset expected capacity threshold and the nominal capacity is taken as a marked battery, and the battery with a monomer equivalent capacity higher than or equal to the preset capacity threshold is taken as a non-marked battery. Obtain the capacity mean value of the monomer equivalent capacity of all non-marked energy storage devices; wherein the fault detection result of the non-marked energy storage device indicates that there is no fault.

[0168] For any target marked battery in the marked battery, if the SOC of the battery exceeds the average SOC by more than 3% in all SOC segments when the monomer state of charge value exceeds 1.02 times the average state of charge value, it is considered that the battery charging segment rises too fast.

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

[0170] When the battery has a fault, a warning information is pushed, and the state of the battery monomer is warned through the warning information.

[0171] S8, based on the single fault detection result of all energy storage devices, a fault detection result of the energy storage device group is generated.

[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, and based on the fault detection result of the energy storage device group, the energy storage device that has a fault in the energy storage device group can be quickly and accurately located.

[0173] When the battery group has a fault, a warning information is pushed, and the state of the battery group is warned through the warning information.

[0174] Based on the above analysis, it can be seen that:

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

[0176] 2. Based on the equivalent circuit parameters of different levels and different types of faults that are difficult to quantify under different working conditions, the concept of equivalent capacity is proposed to describe the actual available capacity of the battery when a fault occurs, considering the actual application of the energy storage power station every day.

[0177] 3. For different levels of battery packs: battery and battery group, and different fault types: low capacity fault type and micro-short circuit fault type, two levels of early warning and fault are proposed respectively, and the threshold value can be adjusted at any time, which is convenient for switching between different reference scenarios.

[0178] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately executed with at least some of the other steps or steps or stages in other steps.

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

[0180] In one exemplary embodiment, as shown in Figure 10 a fault detection device of a group of energy storage devices is provided, comprising: a voltage acquisition module 100, an average state of charge value detection module 200, a single battery state of charge value and capacity detection module 300, a single battery fault detection module 400, and a group of energy storage devices fault detection module 500, wherein:

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

[0182] The average state of charge value detection module 200 is configured to detect an average state of charge value of the group of energy storage devices based on the average voltage;

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

[0184] The single battery fault detection module 400 is configured to generate a single battery fault detection result of all the energy storage devices according to the average state of charge value, and the single battery equivalent capacity and the single battery state of charge value of each energy storage device;

[0185] The energy storage device group fault detection module 500 is configured 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.

[0186] In one embodiment, the single-unit fault detection module 400 is further configured to determine that the single-unit fault detection result of the non-marked energy storage device is no fault, and obtain a capacity mean value of the single-unit equivalent capacity of all the non-marked energy storage devices; and generate, for any target marked energy storage device in the marked energy storage devices, a single-unit fault detection result of the target marked energy storage device according to the average state-of-charge value, the capacity mean value, and the single-unit equivalent capacity and the single-unit state-of-charge value of the target marked energy storage device.

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

[0188] In one embodiment, the average state-of-charge value detection module 200 is further configured to obtain a mapping relationship between the voltage and the state-of-charge value; and query, based on the mapping relationship, a state-of-charge value corresponding to the average voltage, and take 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 single-unit state-of-charge value and capacity detection module 300 is further configured to query, based on the average state-of-charge value and a voltage difference between the single-unit voltage and the average voltage, an initial single-unit state-of-charge value corresponding to the single-unit voltage from the mapping relationship; detect a state update step length of the initial single-unit state-of-charge value; and update the initial single-unit state-of-charge value based on the average state-of-charge value and the state update step length to obtain the single-unit state-of-charge value of the single-unit voltage.

[0190] In one embodiment, the single-unit state-of-charge value and capacity detection module 300 is further configured to obtain a discharged electric quantity of the energy storage device; and detect the single-unit equivalent capacity of the energy storage device according to the discharged electric quantity of the energy storage device and the single-unit state-of-charge value.

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

[0192] In an example embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in Figure 11 The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. 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. The processor of the computer device is configured 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 running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store data such as average voltages of the energy storage device group at high-frequency sampling time points and single-cell voltages of each energy storage device in the energy storage device group at low-frequency sampling time points corresponding to the high-frequency sampling time points. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with terminals outside through a network connection. The computer program is executed by the processor to implement a fault detection method for an energy storage device group.

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

[0194] In an embodiment, a computer device is also provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0195] In an embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0196] In an embodiment, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0197] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0198] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0199] The above embodiments only express several implementation ways of the present application, and the description is specific and detailed, but it should not be understood as a limitation to the patent scope of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for detecting a failure of a group of energy storage devices, characterized by, The method comprises: acquiring an average voltage of the 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; detecting an average state of charge value of the energy storage device group based on the average voltage; for each energy storage device, acquiring a fitting relationship curve, wherein an abscissa direction of the fitting relationship curve is a state of charge value, an ordinate direction of the fitting relationship curve is a voltage, determining a mean curve point corresponding to the average state of charge value on the fitting relationship curve, and acquiring a first intersection point of a first tangent line corresponding to the mean curve point and the single voltage based on a voltage difference between the single voltage and the average voltage, acquiring a second intersection point of a vertical line of the first intersection point in the abscissa direction and the fitting relationship curve, and determining a single state of charge value corresponding to the second intersection point as an initial single state of charge value, determining a target tangent line slope of the second intersection point, and acquiring a third intersection point of a slanted line with a target tangent line slope and the single voltage, and determining a single state of charge value corresponding to the third intersection point as a single state of charge value corresponding to the single voltage, and detecting a single equivalent capacity of the energy storage device according to the single state of charge value; generating a single fault detection result of all the energy storage devices 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 a fault detection result of the energy storage device group based on the single fault detection result of all the energy storage devices.

2. The method of claim 1, wherein, The generating of the single fault detection result of all the energy storage devices 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 comprises: regarding an energy storage device with a single equivalent capacity lower than a preset capacity threshold as a marked energy storage device, and regarding an energy storage device with a single equivalent capacity higher than or equal to the preset capacity threshold as a non-marked energy storage device; determining that the single fault detection result of the non-marked energy storage device is non-fault, and acquiring a capacity mean value of the single equivalent capacity of all the non-marked energy storage devices; for any target marked energy storage device in the marked energy storage devices, generating a single fault detection result of the target marked energy storage device according to the average state of charge value, the capacity mean value, and the single equivalent capacity and the single state of charge value of the target marked energy storage device.

3. The method of claim 2, wherein, The generating of the single fault detection result of the target marked energy storage device according to the average state of charge value, the capacity mean value, and the single equivalent capacity and the single state of charge value of the target marked energy storage device comprises: 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 a target charging time period, acquiring a time period number proportion of the single state of charge value exceeding the average state of charge value within all preset charging time periods, wherein the target charging time period is any time period in all preset charging time periods. In a case where the single-cell equivalent capacity is less than the capacity mean value by a first preset ratio and the number of time periods is greater than a second preset ratio, it is determined that the target marked energy storage device has a low-capacity fault; In a case where the single-cell equivalent capacity is greater than or equal to the capacity mean value by the first preset ratio and 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.

4. The method of claim 1, wherein, The average voltage-based detection of the average state-of-charge value 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 taken as the average state-of-charge value of the energy storage device group.

5. The method of claim 1, wherein, The single-cell equivalent capacity detection of the energy storage device according to the single-cell state-of-charge value includes: Obtaining the discharged electric quantity of the energy storage device; According to the discharged electric quantity and the single-cell state-of-charge value of the energy storage device, the single-cell equivalent capacity of the energy storage device is detected.

6. A fault detection apparatus for a group of energy storage devices, characterized by, The device includes: A voltage obtaining module is configured to obtain the average voltage of the energy storage device group at a high-frequency sampling time point and the 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; An average state-of-charge value detection module is configured to detect the average state-of-charge value of the energy storage device group based on the average voltage; A single-cell state-of-charge value and capacity detection module is configured to obtain, for each energy storage device, a fitting relationship curve, wherein the horizontal axis direction of the fitting relationship curve is the state-of-charge value, and the vertical axis direction of the fitting relationship curve is the voltage, determine a mean curve point on the fitting relationship curve corresponding to the average state-of-charge value, obtain a first intersection point of a first tangent line of the mean curve point and the single-cell voltage based on the voltage difference between the single-cell voltage and the average voltage, obtain a second intersection point of a vertical line of the first intersection point in the horizontal axis direction and the fitting relationship curve, and determine the single-cell state-of-charge value corresponding to the second intersection point as an initial single-cell state-of-charge value, determine a target tangent line slope of the second intersection point, obtain a third intersection point of a tangent line with the target tangent line slope and the single-cell voltage, and determine 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, and detect the single-cell equivalent capacity of the energy storage device according to the single-cell state-of-charge value; A single-cell fault detection module is configured 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 the single-cell state-of-charge value of each energy storage device; An energy storage device group fault detection module is configured to generate a fault detection result of the energy storage device group based on the single-cell fault detection results of all the energy storage devices.

7. The apparatus of claim 6, wherein, The single-cell state-of-charge value and capacity detection module is further configured to obtain the discharged electric quantity of the energy storage device, and detect the single-cell equivalent capacity of the energy storage device according to the discharged electric quantity and the single-cell state-of-charge value of the energy storage device.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

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