A power supply side energy storage device fault diagnosis method and system

Abstract

The application relates to a power supply side energy storage equipment fault diagnosis method and system, wherein the method comprises the following steps: S1, obtaining operation information of related parameters in normal operation of energy storage equipment operation data; S2, establishing a judgment basis for operation abnormality of the related parameters in energy storage equipment operation failure; S3, collecting operation information of the related parameters in energy storage equipment operation; and S4, performing fault identification on the energy storage equipment operation failure based on the judgment basis. Compared with the prior art, the application combines machine learning technology, more comprehensively and accurately realizes fault diagnosis on the power supply side energy storage equipment, and ensures normal operation of the power supply side energy storage equipment.

Description

Technical Field

[0001] This invention relates to the field of energy storage equipment fault diagnosis technology, and in particular to a method and system for fault diagnosis of power supply-side energy storage equipment. Background Technology

[0002] Energy storage can be used to mitigate power fluctuations from distributed renewable energy sources such as wind and solar power. These power fluctuations cause fluctuations in the power output of the distribution network. By utilizing the rapid charging and discharging characteristics of energy storage systems, the impact of renewable energy grid connection on the distribution network can be reduced, enhancing the controllability of the distribution network. Energy storage devices are characterized by high conversion efficiency and rapid operation, enabling independent active and reactive power exchange with the system. Combining energy storage devices with advanced power conversion and control technologies can achieve rapid control of the power grid, improve the static and dynamic characteristics of the grid, and meet the needs of renewable energy systems.

[0003] Existing energy storage equipment operation and maintenance methods mainly rely on manual, periodic preventive maintenance. Comprehensive safety early warning and protection technologies still need improvement. There is an urgent need to establish a power station system safety system applicable to the information blind spots in the limited space of lithium battery energy storage power stations, so as to achieve comprehensive perception and prediction of the safety status of energy storage power stations in the event of fires and other disasters, thereby ensuring safe and reliable power services from energy storage power stations. Summary of the Invention

[0004] The purpose of this invention is to overcome the defects of the prior art and provide a method and system for fault diagnosis of power supply side energy storage equipment.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A fault diagnosis method for power-side energy storage devices includes the following steps:

[0007] S1. Obtain operational information of relevant parameters from the energy storage device's operational data to ensure normal operation;

[0008] S2. Establish criteria for judging abnormal operation of relevant parameters when energy storage equipment malfunctions;

[0009] S3. Collect operational information on relevant parameters of the energy storage device during operation;

[0010] S4. Based on the judgment criteria, identify the operational faults of the energy storage equipment.

[0011] Furthermore, the relevant parameters for normal operation in the energy storage device operation data include the charging current, charging voltage, discharging current, discharging voltage, charging power, discharging power, operating temperature, and device events of the energy storage device generated during operation.

[0012] Furthermore, in step S2, the operational information includes battery sensor measurement data and battery historical data.

[0013] Furthermore, in step S2, establishing the criteria for judging abnormal operation of relevant parameters when an energy storage device malfunctions includes the following sub-steps:

[0014] The Kalman filter algorithm is used to fuse information from battery sensor measurement data and battery historical data.

[0015] Based on the filtering results calculated by the Kalman filter algorithm, a preliminary judgment can be made as to whether the energy storage device has malfunctioned.

[0016] If it is initially determined that a short circuit fault has occurred in a certain battery cell of the energy storage device, the voltage of the faulty battery cell can be solved based on the nodal voltage method and the branch current method.

[0017] The voltage of the faulty battery cell is compared with the voltage of the normal battery cell to confirm whether the battery cell is faulty.

[0018] Furthermore, in step S2, the fusion of battery sensor measurement data and battery historical data using the Kalman filter algorithm specifically includes the following steps:

[0019] If the voltage sequence extracted from the battery history data is: In the formula, Let t be the voltage of the i-th cell in the battery pack during the c-th charge-discharge cycle; J be the number of cells in the battery pack; and K be the number of historical cycles.

[0020] For each cycle, the most likely voltage value of a single cell in different SOC ranges is calculated to obtain the SOC value. soc -V sequence Where S soc s represents the state of charge, where s is the SOC interval number;

[0021] S soc The formula for calculating the -V sequence is:

[0022]

[0023] In the formula, M med () indicates taking the median; V i (t) is the dataset of voltage values ​​up to the i-th battery in this cycle;

[0024] S soc -V represents the estimated value from the Kalman filter. The battery voltage measured by the sensor is considered the observed value, leading to the following Kalman formula:

[0025] H k =P k-1 / (Q k-1 -P k-1 )

[0026]

[0027] P k =(1-H k-1 )P k-1

[0028] In the formula, H k V is the Kalman gain; k This is the optimal estimate; P k-1 The optimal error before the update; P k The updated optimal estimation error; Q k-1 This represents the initial measurement error;

[0029] The optimal estimate calculated by the Kalman filter algorithm is the final filtering result.

[0030] Further, in step S2, the preliminary judgment on whether the energy storage device has malfunctioned based on the filtering result calculated by the Kalman filter algorithm specifically includes the following steps:

[0031] Determine if the voltage expressions for each individual cell in the battery pack conform to the following:

[0032] V i =E-IR i

[0033] In the formula, V i R is the voltage of the i-th cell; E is the open-circuit voltage; I is the current; R i Let be the equivalent internal resistance of the i-th monomer;

[0034] If the condition does not meet the requirements, it is preliminarily determined that the battery cell has an internal short circuit fault.

[0035] Furthermore, in step S2, if it is preliminarily determined that a short-circuit fault has occurred in a certain battery cell of the energy storage device, the circuit formula of that battery cell is listed using the node voltage method and the branch current method:

[0036] EI R R = I ISC R ISC

[0037] I R -I ISC =I

[0038] In the formula, I R R is the current flowing through the branch with equivalent internal resistance; R is the equivalent internal resistance of the battery; I ISC This is the short-circuit current; R ISC This is the equivalent short-circuit resistance;

[0039] Calculate the current I in the short-circuit branch. ISC for:

[0040] I ISC = (E-IR) / (R+R) ISC )

[0041] The voltage V2 of the faulty battery cell, obtained using the nodal voltage method, is:

[0042] V2 = R ISC (E-IR) / (R+R ISC )

[0043] If the voltage V2 of the faulty battery cell is lower than that of the normal battery cell, then the battery cell is confirmed to be faulty.

[0044] A fault diagnosis system for power-side energy storage equipment includes a core processing unit, a program startup unit, a setting unit, a polling unit, a judgment unit, and an alarm unit.

[0045] The program startup unit, setting unit, polling unit, judgment unit, and alarm unit are respectively connected to the core processing unit;

[0046] The program startup unit is used to start the program;

[0047] The setting unit is used to set the normal operating information of relevant parameters in the operating data of the energy storage device to be collected;

[0048] The polling unit is used to collect operational information of normal relevant parameters from the energy storage device's operational data;

[0049] The judgment unit is used to identify faults in the operation of energy storage equipment.

[0050] The alarm unit is used to trigger an alarm when a fault occurs in the energy storage device being tested.

[0051] The core processing unit is used to provide overall processing of the program startup unit, setting unit, polling unit, judgment unit and alarm unit;

[0052] After the program processing startup unit sends a startup signal, the core processing unit first reads the normal relevant parameters from the energy storage device operation data to be collected stored in the setting unit; secondly, it sends a data acquisition signal to the polling unit to collect the normal relevant parameters from the collected energy storage device operation data; the next step is to transmit the normal relevant parameters from the collected energy storage device operation data to the judgment unit to identify faults in the energy storage device operation; if the detected energy storage device is indeed faulty, the core processing unit sends an alarm signal to the alarm unit.

[0053] Furthermore, the fault identification process for the energy storage device in the judgment unit includes the following sub-steps:

[0054] The Kalman filter algorithm is used to fuse information from battery sensor measurement data and battery historical data.

[0055] Based on the filtering results calculated by the Kalman filter algorithm, a preliminary judgment can be made as to whether the energy storage device has malfunctioned.

[0056] If it is initially determined that a short circuit fault has occurred in a certain battery cell of the energy storage device, the voltage of the faulty battery cell can be solved based on the nodal voltage method and the branch current method.

[0057] The voltage of the faulty battery cell is compared with the voltage of the normal battery cell to confirm whether the battery cell is faulty.

[0058] Furthermore, the process of fusing battery sensor measurement data and battery historical data using the Kalman filter algorithm specifically includes the following steps:

[0059] If the voltage sequence extracted from the battery history data is: In the formula, Let t be the voltage of the i-th cell in the battery pack during the c-th charge-discharge cycle; J be the number of cells in the battery pack; and K be the number of historical cycles.

[0060] For each cycle, the most likely voltage value of a single cell in different SOC ranges is calculated to obtain the SOC value. soc -V sequence Where S soc s represents the state of charge, where s is the SOC interval number;

[0061] S soc The formula for calculating the -V sequence is:

[0062]

[0063] In the formula, M med () indicates taking the median; V i (t) is the dataset of voltage values ​​up to the i-th battery in this cycle;

[0064] S soc -V represents the estimated value from the Kalman filter. The battery voltage measured by the sensor is considered the observed value, leading to the following Kalman formula:

[0065] H k =P k-1 / (Q k-1 -P k-1 )

[0066] Vk =V k -1+H k (V c i (t)-V k-1 )

[0067] P k =(1-H k-1 )P k-1

[0068] In the formula, H k V is the Kalman gain; k This is the optimal estimate; P k-1 The optimal error before the update; P k The updated optimal estimation error; Q k-1 This represents the initial measurement error;

[0069] The optimal estimate calculated by the Kalman filter algorithm is the final filtering result;

[0070] Based on the filtering results calculated by the Kalman filter algorithm, a preliminary judgment is made as to whether the energy storage device has malfunctioned, and whether the voltage expressions of each battery cell in the battery pack conform to the following:

[0071] V i =E-IR i

[0072] In the formula, V i R is the voltage of the i-th cell; E is the open-circuit voltage; I is the current; R i Let be the equivalent internal resistance of the i-th monomer;

[0073] If the condition does not meet the requirements, it is preliminarily determined that the battery cell has an internal short circuit fault.

[0074] For a battery cell in an energy storage device initially diagnosed as having a short-circuit fault, the circuit formula for that battery cell is derived using the node voltage method and the branch current method:

[0075] EI R R = I ISC R ISC

[0076] I R -I ISC =I

[0077] In the formula, I R R is the current flowing through the branch with equivalent internal resistance; R is the equivalent internal resistance of the battery; I ISC This is the short-circuit current; R ISC This is the equivalent short-circuit resistance;

[0078] Calculate the current I in the short-circuit branch. ISC for:

[0079] I ISC = (E-IR) / (R+R) ISC )

[0080] The voltage V2 of the faulty battery cell, obtained using the nodal voltage method, is:

[0081] V2 = R ISC (E-IR) / (R+R ISC )

[0082] If the voltage V2 of the faulty battery cell is lower than that of the normal battery cell, then the battery cell is confirmed to be faulty.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] This invention provides a fault diagnosis method and system for power-side energy storage devices. By analyzing operational information from the tested power-side energy storage device's operating data, including charging current, charging voltage, discharging current, discharging voltage, charging power, discharging power, and operating temperature, and combining this with machine learning technology, a fault model for the energy storage device is formed. This model establishes criteria for judging abnormal operation of relevant parameters when the energy storage device malfunctions, detects potential fault events during the operation of the power-side energy storage device, and issues technical alarms for detected faults. This method provides more comprehensive and accurate fault diagnosis for power-side energy storage devices and has broad application prospects for ensuring the normal operation of these devices. Attached Figure Description

[0085] Figure 1 This is a flowchart of a fault diagnosis method for power-side energy storage devices proposed in this invention;

[0086] Figure 2 This is a flowchart of data acquisition in an embodiment of the present invention;

[0087] Figure 3 This is a structural block diagram of a fault diagnosis system for power-side energy storage devices proposed in this invention. Detailed Implementation

[0088] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention and therefore only showing the components relevant to the invention. This embodiment is implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0089] Example 1

[0090] like Figure 1 and Figure 2 As shown, the present invention provides a fault diagnosis method for power-side energy storage devices, comprising the following steps:

[0091] Step S1: Obtain operational information of relevant parameters for normal operation from the energy storage device's operational data;

[0092] Step S2: Establish the criteria for judging abnormal operation of relevant parameters when the energy storage device malfunctions;

[0093] Step S3: Collect operational information on relevant parameters of the energy storage device during operation;

[0094] Step S4: Based on the judgment criteria, identify the faults in the operation of the energy storage equipment.

[0095] The energy storage described in this invention is power-side energy storage, which is a type of energy storage. Energy storage is applied in the development model of "renewable energy + energy storage" such as centralized supporting facilities and distributed microgrids. It can smooth the fluctuation of renewable energy, increase the absorption capacity of renewable energy, and effectively solve the current problems of wind and solar curtailment and carbon emission reduction targets.

[0096] Optionally, the relevant parameters for normal operation in the energy storage device's operational data include the charging current, charging voltage, discharging current, discharging voltage, charging power, discharging power, and operating temperature generated during the operation of the monitored energy storage device, as well as device events. Specifically, the analysis of maintenance events of the energy storage device will capture fault events.

[0097] Because the actual battery data collected contains a lot of noise, the raw voltage data needs to be denoised to improve the accuracy of fault diagnosis. Analysis of historical data from electric vehicles reveals that battery voltage data does not change significantly within a very small state of charge (SOC) range. Therefore, historical voltage data in different SOC ranges can serve as a reference for data cleaning.

[0098] This invention employs a Kalman filter algorithm to fuse information from battery sensor measurement data and historical battery data, resulting in collected data that more closely approximates the battery's actual operating state. Assume the voltage sequence extracted from the historical battery data is as follows: In the formula, V c i (t) represents the voltage of the i-th cell in the battery pack at time t during the c-th charge-discharge cycle; J represents the number of cells in the battery pack; and K represents the number of historical cycles.

[0099] For each cycle, it is necessary to calculate the most likely voltage value of a single cell in different SOC ranges to obtain S. soc -V sequence Where S socHere, s represents the state of charge (SOC) interval number. soc The formula for calculating the -V sequence is Equation (1):

[0100]

[0101] In the formula, M med () indicates taking the median; V io (t) is the dataset of voltage values ​​up to the i-th battery in this cycle.

[0102] Get S soc After the -V sequence, this sequence is considered as an estimate of the Kalman filter, and the battery voltage value measured by the sensor is considered as the observed value. The following Kalman formula can be obtained:

[0103] H k =P k-1 / (Q k-1 -P k-1 (2)

[0104] V k =V k -1+H k (V c i (t)-V k-1 (3)

[0105] P k =(1-H k-1 )P k-1 (4)

[0106] In the formula, H k V is the Kalman gain; k This is the optimal estimate; P k-1 The optimal error before the update; P k The updated optimal estimation error; Q k-1 This represents the initial measurement error. The optimal estimate calculated by the Kalman filter algorithm is the final filtered result. The estimate will be very close to the battery's historical data during normal operation, and random interference signals will be removed.

[0107] Under normal conditions, the voltage expression of each battery cell in the battery pack is shown in equation (5).

[0108] V i =E-IR i (5)

[0109] In the formula, V i R is the voltage of the i-th cell; E is the open-circuit voltage; I is the current; R i Let be the equivalent internal resistance of the i-th monomer.

[0110] When a battery experiences an internal short circuit fault, the formula for the single-cell circuit can be derived using the node voltage method and the branch current method, as shown in equations (6) and (7).

[0111] EI R R = I ISC R ISC (6)

[0112] I R -I ISC =I (7)

[0113] In the formula, I R R is the current flowing through the branch with equivalent internal resistance; R is the equivalent internal resistance of the battery; I ISC This is the short-circuit current; R ISC This is the equivalent short-circuit resistance.

[0114] The current I of the short-circuit branch can be calculated using equations (6) and (7). ISC for:

[0115] I ISC = (E-IR) / (R+R) ISC (8)

[0116] More specifically, the voltage V2 of the faulty battery cell can be obtained by the node voltage method as shown in equation (9).

[0117] V2 = I ISC R ISC (9)

[0118] More specifically, by combining equations (8) and (9), the voltage V2 of the faulty battery cell can be calculated as shown in equation (10).

[0119] V2 = R ISC (E-IR) / (R+R ISC (10)

[0120] It can be seen that, by comparing equations (5) and (10), since R ISC / (R+R ISC If V2 < 1, the voltage of a single battery cell will be lower than that of other battery cells in the event of a short circuit.

[0121] In this embodiment, step S1 requires relevant personnel to input the normal operating information of relevant parameters in the energy storage device's operating data in advance, so as to establish the basis for judging the abnormal operation of relevant parameters when the energy storage device malfunctions in step S2.

[0122] Step S2 uses machine learning to learn the normal operating information of relevant parameters in the operating data of energy storage equipment, forming an energy storage equipment operation fault model, and establishing the judgment criteria for abnormal operation of relevant parameters when the energy storage equipment fails.

[0123] Step S3 includes an interface program for the energy storage device operation management program to collect operational information of relevant parameters during the operation of the energy storage device. The collected operational information of relevant parameters during the operation of the energy storage device is compared with the judgment criteria for abnormal operation of relevant parameters when the energy storage device fails, established in Step 2, to determine whether there are any abnormalities in the energy storage device operation data.

[0124] Once the data acquisition service starts, it is necessary to check whether the interface program is properly connected to the energy storage device operation and management program. If the connection fails, the data acquisition service should be restarted.

[0125] like Figure 2 As shown, the interface program uses a timed polling method to collect the operating information of relevant parameters of the energy storage device issued by the operation management program of the energy storage device under test. It receives the operating information of relevant parameters of the energy storage device issued by the operation management program of the energy storage device in seconds.

[0126] Step S4, based on the judgment criteria for abnormal operation of relevant parameters when the energy storage device malfunctions, established in Step S2, judges the operation information of relevant parameters of the energy storage device issued by the energy storage device operation management program. If it is determined that the detected energy storage device has a fault event, the fault is identified and an alarm is issued, and then the interface program is closed; if it is determined that the detected energy storage device does not have a fault event, the interface program is closed directly.

[0127] Example 2

[0128] On the other hand, such as Figure 3 As shown, the present invention also provides a fault diagnosis system for power-side energy storage devices, used to implement the fault diagnosis method for power-side energy storage devices as described in the above embodiments, including a core processing unit, a program startup unit, a setting unit, a polling unit, a judgment unit, and an alarm unit.

[0129] The program startup unit, setting unit, polling unit, judgment unit, and alarm unit are all connected to the core processing unit.

[0130] The program startup unit is used to start the program; the setting unit is used to set the normal relevant parameters in the energy storage device operation data to be collected; the polling unit is used to collect the normal relevant parameters in the energy storage device operation data; the judgment unit is used to identify the faults in the operation of the energy storage device; and the alarm unit is used to issue an alarm when the detected energy storage device malfunctions.

[0131] The core processing unit coordinates the operation of the program startup unit, setting unit, polling unit, judgment unit, and alarm unit. When the program startup unit sends a startup signal, the core processing unit first reads the normal operating parameters from the energy storage device operation data stored in the setting unit. Next, it sends a data acquisition signal to the polling unit to collect the normal operating parameters from the collected energy storage device operation data. The next step is to transmit the collected normal operating parameters from the energy storage device operation data to the judgment unit for fault identification. If the detected energy storage device is indeed faulty, the core processing unit sends an alarm signal to the alarm unit.

[0132] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A fault diagnosis method for power supply-side energy storage equipment, characterized in that, Includes the following steps: S1. Obtain operational information of relevant parameters from the energy storage device's operational data to ensure normal operation; S2. Establish criteria for judging abnormal operation of relevant parameters when energy storage equipment malfunctions; S3. Collect operational information on relevant parameters of the energy storage device during operation; S4. Based on the judgment criteria, identify the operational faults of the energy storage equipment; In step S2, establishing the criteria for judging abnormal operation of relevant parameters when energy storage equipment malfunctions includes the following sub-steps: The Kalman filter algorithm is used to fuse information from battery sensor measurement data and battery historical data. Based on the filtering results calculated by the Kalman filter algorithm, a preliminary judgment can be made as to whether the energy storage device has malfunctioned. If it is initially determined that a short circuit fault has occurred in a certain battery cell of the energy storage device, the voltage of the faulty battery cell can be solved based on the nodal voltage method and the branch current method. Compare the voltage of the faulty battery cell with the voltage of the normal battery cell to confirm whether the battery cell is faulty. In step S2, the process of fusing battery sensor measurement data and battery historical data using the Kalman filter algorithm specifically includes the following steps: If the voltage sequence extracted from the battery history data is: In the formula, for t The first time in the battery pack i The monomer in the first c Voltage during a charge-discharge cycle; J This represents the number of batteries in the battery pack. K The number of historical cycles; For each loop process, calculate different... SOC The most likely voltage value of a single cell in the range, to obtain S soc -V sequence ,in S soc In a charged state, s for SOC Interval index; S soc -V The formula for calculating the sequence is: In the formula, This indicates taking the median; For the period ending at the end of this cycle i A dataset of voltage values ​​for individual batteries; S soc -V That is, the estimated value of the Kalman filter, with the battery voltage value measured by the sensor as the observed value, yields the following Kalman formula: In the formula, Kalman gain; This is the optimal estimate; This represents the optimal error before the update. The updated optimal estimation error; This represents the initial measurement error; The optimal estimate calculated by the Kalman filter algorithm is the final filtering result.

2. The fault diagnosis method for a power supply-side energy storage device according to claim 1, characterized in that, The relevant parameters for normal operation in the energy storage device operation data include the charging current, charging voltage, discharging current, discharging voltage, charging power, discharging power, operating temperature, and device events generated by the monitored energy storage device during operation.

3. The fault diagnosis method for a power supply-side energy storage device according to claim 1, characterized in that, In step S2, the operating information includes battery sensor measurement data and battery historical data.

4. The fault diagnosis method for a power supply-side energy storage device according to claim 1, characterized in that, In step S2, the preliminary judgment on whether the energy storage device has malfunctioned based on the filtering result calculated by the Kalman filter algorithm specifically includes the following steps: Determine if the voltage expressions for each individual cell in the battery pack conform to the following: In the formula, For the first i The voltage of the unit; E This is the open-circuit voltage; I For current; For the first i The equivalent internal resistance of the unit cell; If the condition does not meet the requirements, it is preliminarily determined that the battery cell has an internal short circuit fault.

5. The fault diagnosis method for a power supply-side energy storage device according to claim 4, characterized in that, In step S2, if it is preliminarily determined that a short circuit fault has occurred in a certain battery cell of the energy storage device, the circuit formula of that battery cell is listed using the node voltage method and the branch current method: ’ In the formula, The current flowing through the branch with equivalent internal resistance; R This is the equivalent internal resistance of the battery; This is the short-circuit current; This is the equivalent short-circuit resistance; Calculate the current in the short-circuit branch. for: The voltage of the faulty battery cell was determined using the nodal voltage method. V 2 for: If the voltage of the faulty battery cell V 2 If the voltage is lower than that of a normal battery cell, then the battery cell is confirmed to be faulty.

6. A fault diagnosis system for power supply-side energy storage equipment, characterized in that, It includes a core processing unit, a program startup unit, a setting unit, a polling unit, a judgment unit, and an alarm unit; The program startup unit, setting unit, polling unit, judgment unit, and alarm unit are respectively connected to the core processing unit; The program startup unit is used to start the program; The setting unit is used to set the normal operating information of relevant parameters in the operating data of the energy storage device to be collected; The polling unit is used to collect operational information of normal relevant parameters from the energy storage device's operational data; The judgment unit is used to identify faults in the operation of energy storage equipment. The alarm unit is used to trigger an alarm when a fault occurs in the energy storage device being tested. The core processing unit is used to provide overall processing of the program startup unit, setting unit, polling unit, judgment unit and alarm unit; When the program processing unit sends a start signal, the core processing unit first reads the normal relevant parameters of the energy storage device operation data that needs to be collected from the setting unit; Secondly, a data acquisition signal is sent to the polling unit to collect the normal relevant parameters in the collected energy storage equipment operation data; the next step is to transmit the normal relevant parameters in the collected energy storage equipment operation data to the judgment unit to identify the fault in the energy storage equipment operation; if the detected energy storage equipment is indeed faulty, the core processing unit sends an alarm signal to the alarm unit. The judgment unit performs fault identification for energy storage equipment operation faults, including the following sub-steps: The Kalman filter algorithm is used to fuse information from battery sensor measurement data and battery historical data. Based on the filtering results calculated by the Kalman filter algorithm, a preliminary judgment can be made as to whether the energy storage device has malfunctioned. If it is initially determined that a short circuit fault has occurred in a certain battery cell of the energy storage device, the voltage of the faulty battery cell can be solved based on the nodal voltage method and the branch current method. Compare the voltage of the faulty battery cell with the voltage of the normal battery cell to confirm whether the battery cell is faulty. The process of fusing battery sensor measurement data and battery historical data using the Kalman filter algorithm specifically includes the following steps: If the voltage sequence extracted from the battery history data is: In the formula, for t The first time in the battery pack i The monomer in the first c Voltage during a charge-discharge cycle; J This represents the number of batteries in the battery pack. K The number of historical cycles; For each loop process, calculate different... SOC The most likely voltage value of a single cell in the range, to obtain S soc -V sequence ,in S soc In a charged state, s for SOC Interval index; S soc -V The formula for calculating the sequence is: In the formula, This indicates taking the median; For the period ending at the end of this cycle i A dataset of voltage values ​​for individual batteries; S soc -V That is, the estimated value of the Kalman filter, with the battery voltage value measured by the sensor as the observed value, yields the following Kalman formula: In the formula, Kalman gain; This is the optimal estimate; This represents the optimal error before the update. The updated optimal estimation error; This represents the initial measurement error; The optimal estimate calculated by the Kalman filter algorithm is the final filtering result; Based on the filtering results calculated by the Kalman filter algorithm, a preliminary judgment is made as to whether the energy storage device has malfunctioned, and whether the voltage expressions of each battery cell in the battery pack conform to the following: In the formula, For the first i The voltage of the unit; E This is the open-circuit voltage; I For current; For the first i The equivalent internal resistance of the unit cell; If the condition does not meet the requirements, it is preliminarily determined that the battery cell has an internal short circuit fault. For a battery cell in an energy storage device initially diagnosed as having a short-circuit fault, the circuit formula for that battery cell is derived using the node voltage method and the branch current method: ’ In the formula, The current flowing through the branch with equivalent internal resistance; R This is the equivalent internal resistance of the battery; This is the short-circuit current; This is the equivalent short-circuit resistance; Calculate the current in the short-circuit branch. for: The voltage of the faulty battery cell was determined using the nodal voltage method. V 2 for: If the voltage of the faulty battery cell V 2 If the voltage is lower than that of a normal battery cell, then the battery cell is confirmed to be faulty.

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