Consistency diagnosis method and device of energy storage battery

Abstract

The invention provides a consistency diagnosis method and device for an energy storage battery, and belongs to the field of intelligent diagnosis of an energy storage battery system, and the method comprises the steps: collecting original data of the energy storage battery at a set time interval, and sorting the original data according to a time field; cleaning the original data to obtain cleaned data; extracting all static time segments of the energy storage battery according to current information in the cleaned data, and screening out long static segments; screening the highest voltage and the lowest voltage according to the voltage information in the long standing segment, converting the highest voltage and the lowest voltage into corresponding highest capacity and lowest capacity, calculating a difference value between the highest capacity and the lowest capacity, and when the difference value is greater than a set threshold value, determining that the battery is an abnormal battery; screening out single batteries with outlier capacities from the abnormal batteries, and carrying out abnormity alarm; the capacity-based consistency diagnosis can more accurately evaluate and judge the energy performance of the energy storage unit in the charging and discharging process, and the accuracy of consistency detection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent diagnosis of energy storage battery systems, and in particular to a consistency diagnosis method for energy storage batteries. Background Art

[0002] In recent decades, the main development trend of power systems has been the transformation of traditional power systems by incorporating smaller distributed generators into existing power systems to alleviate the growing energy demand and concerns about climate change. In addition, since microgrids have the ability to widely integrate renewable energy, this has promoted their rapid development. However, due to reasons such as frequently changing load demands and the intermittent nature of renewable energy, the supply and demand of microgrids are unbalanced. To solve these problems, energy storage systems are installed and operated in a microgrid as a buffer. Energy storage systems can alleviate the imbalance between supply and demand in microgrids by absorbing excess power during off-peak loads and releasing the same power during peak loads.

[0003] Energy storage systems are very flexible power electronic devices with fast response time, which improve and ensure the power quality and reliability of microgrids through uninterrupted power supply. In the autonomous mode of microgrids, energy storage systems act as basic backup power, provide grid-connected power generation, and provide black start when the microgrid is reconnected to the utility grid. In addition, energy storage systems have the advantages of improving efficiency, shaving peaks, and alleviating congestion in microgrids.

[0004] Due to the high price of batteries, energy storage systems remain one of the most expensive components in microgrids. In order to extend its service life, special attention needs to be paid to the management of the energy storage system to ensure that the battery life does not deteriorate under various operating modes. The main factor that needs to be studied here is the consistency of the capacity of the energy storage unit, which mainly refers to the similarity of the capacity of each single cell in the battery group. In order to ensure higher efficiency and battery health, the energy storage unit needs to maintain its capacity consistency within a certain range. Poor consistency will lead to the following problems:

[0005] (1) The energy storage capacity of the battery cells may vary greatly, which may lead to uneven energy distribution of the entire system during use. Some batteries may run out of energy prematurely, reducing the overall available energy of the system.

[0006] (2) It may also cause some battery cells to lose capacity or performance faster than other cells, resulting in a shortened overall system life because some battery cells are subjected to higher currents or deeper depths of discharge more frequently, which accelerates their aging;

[0007] (3) The system may perform unstably under extreme conditions. For example, in high or low temperature environments, some units may fail or malfunction, thus affecting the reliability of the entire system.

[0008] For multiple distributed energy storage units, their capacity consistency needs to be controlled at a low level as much as possible during the entire operation process to maintain their efficient operation.

[0009] Therefore, it is necessary to study the consistency diagnosis method of energy storage batteries to improve the safety and performance of energy storage systems. At present, the consistency detection of batteries in battery management systems is mainly for voltage detection. For example, the Chinese invention patent application with publication number CN113589174A, "A method and device for predicting the consistency of cells in a lithium-ion power battery system", obtains the charge and discharge curves corresponding to the highest capacity single cell and the lowest capacity single cell in the lithium-ion power battery system to be tested, and calculates the charge and / or discharge terminal pressure difference corresponding to the highest capacity single cell and the lowest capacity single cell based on the preset operating conditions and the charge and discharge curves corresponding to the highest capacity single cell and the lowest capacity single cell. The final pressure difference determines the predicted result of the cell consistency of the lithium-ion power battery system to be tested during the test time period, but the pressure difference cannot reflect the actual battery capacity very well. For example, the static pressure difference of the lithium iron phosphate battery when the capacity is low is much larger than the static pressure difference when the capacity is high. For example, the pressure difference between 6% and 3% of the capacity can reach 100mV, while the pressure difference between 97% and 94% of the capacity is 2mV. It can be seen that the pressure difference cannot accurately describe the actual energy storage and release capabilities of this type of battery, and thus cannot promptly remind relevant personnel to perform maintenance. In the long run, it may affect the capacity consistency of the battery, reduce the overall charging and discharging energy of the energy storage, and even affect its service life. Summary of the invention

[0010] The technical problem to be solved by the present invention is how to improve the accuracy of consistency detection of energy storage batteries.

[0011] The present invention solves the above technical problems through the following technical solutions: a consistency diagnosis method for energy storage batteries, comprising:

[0012] Step 1: Collect raw data of the energy storage battery at a set time interval, and sort the raw data according to the time field;

[0013] Step 2: Clean the original data to obtain cleaned data, where the cleaning includes removing duplicate data, missing data, and abnormal data;

[0014] Step 3: extract all static time segments of the energy storage battery according to the current information in the cleaned data, and select the long static segments;

[0015] Step 4: According to the voltage information in the long static segment, the highest voltage and the lowest voltage are screened out, and converted into the corresponding highest capacity and the lowest capacity, and the difference between the highest capacity and the lowest capacity is calculated. When the difference is greater than the set threshold, it is determined to be an abnormal battery;

[0016] Step 5: Filter out the single cells with outlier capacity from the abnormal batteries and issue an abnormal alarm.

[0017] Beneficial effect: The present invention first extracts the static time segment of the energy storage battery according to the current information, and screens out the long static segment, then screens out the two batteries with the highest voltage and the lowest voltage in the long static segment, and converts the highest voltage and the lowest voltage into the corresponding highest capacity and the lowest capacity, and uses the difference between the highest capacity and the lowest capacity as the basis for determining abnormal batteries, thereby screening out single cells with outliers in capacity, and timely capturing and analyzing the performance differences of single cells in the entire energy storage system. Compared with the diagnosis method based on pressure difference, the consistency diagnosis of the capacity of the battery cell according to the present invention can more accurately evaluate and judge the energy performance of the energy storage unit during the charging and discharging process, and can more comprehensively consider the actual energy storage and release capabilities of the battery.

[0018] Preferably, in step 2, duplicate data and missing data in the original data are eliminated by directly deleting duplicate data and missing data, and abnormal data are eliminated by using the standard deviation method, the interquartile range method, or the Z-score method.

[0019] Preferably, step 3 comprises:

[0020] 3.1. According to the current information in the cleaned data, the moment when the absolute value of the current is less than the current setting threshold is determined as the static moment;

[0021] 3.2. Determine the data whose time interval between two adjacent rows is less than the first time setting threshold as the same continuous time segment to obtain a static time segment;

[0022] 3.3. Extract the time information and voltage information of the start time and the end time from the static time segment, and determine the time segment whose time interval between the start time and the end time is greater than the second time setting threshold as a long static segment.

[0023] Beneficial effect: The present invention evaluates the battery status based on the voltage of each single battery at a static moment, and can accurately evaluate the battery status.

[0024] Preferably, in step 4, the highest voltage and the lowest voltage screened out in the long static segment are converted into the corresponding highest capacity and the lowest capacity according to the open circuit voltage method, and when the open circuit voltage method is used for capacity conversion, a voltage interval with a large rate of change on the SOC-OCV curve is selected.

[0025] Beneficial effect: The present invention uses the open circuit voltage method to perform capacity conversion and selects a voltage interval with a large change rate on the SOC-OCV curve, which can directly correspond to the actual stored energy of the battery and can accurately describe the actual energy storage and release capacity of the battery.

[0026] Preferably, the step 5 of screening out the single cells with outlier capacity from the abnormal cells specifically includes:

[0027] 5.1. Use the open circuit voltage method to convert all single cell voltages in abnormal batteries into single cell capacity;

[0028] 5.2. Filter out the number of other monomer capacities within the capacity setting threshold range of each monomer capacity as the outlier value of the monomer capacity;

[0029] 5.3. Extract the minimum value from all outliers, and subtract the minimum value from the outlier value of each single cell capacity to obtain the calculation result of each single cell. The single cell whose calculation result is less than the outlier set threshold is determined as a single cell with outlier capacity.

[0030] Beneficial effects: The present invention uses a simple threshold method to screen out individual cells with outlier capacity from abnormal batteries, which can greatly reduce the resources and computing time required for operation, improve the computing speed, complete diagnosis and response in a shorter time, improve real-time performance and diagnostic efficiency, and reduce the operating cost of the energy management system.

[0031] Preferably, when an abnormal alarm is performed in step 5, the difference between the highest capacity and the lowest capacity is used to divide the abnormal level. The larger the difference, the higher the abnormal level and the more serious the abnormality. The abnormal level is indicated by a digital status code.

[0032] Beneficial effects: The present invention uses the difference between the highest capacity and the lowest capacity to classify abnormal levels, and intuitively displays the abnormal level when an abnormal alarm is triggered, which can remind relevant personnel to take corresponding treatment measures for timely maintenance.

[0033] The present invention also provides a consistency diagnosis device for an energy storage battery, comprising:

[0034] A data acquisition unit, used to collect raw data of the energy storage battery at set time intervals and sort the raw data according to the time field;

[0035] A data processing unit is used to clean the original data to obtain cleaned data, wherein the cleaning includes removing duplicate data, missing data and abnormal data;

[0036] A static segment extraction unit is used to extract all static time segments of the energy storage battery according to the current information in the cleaned data, and to filter out the long static segments;

[0037] A consistency calculation unit is used to screen out the highest voltage and the lowest voltage according to the voltage information in the long static segment, convert them into the corresponding highest capacity and the lowest capacity, calculate the difference between the highest capacity and the lowest capacity, and determine the battery as abnormal when the difference is greater than a set threshold;

[0038] The abnormal diagnosis alarm unit is used to screen out the single cells with outlier capacity from the abnormal batteries and issue an abnormal alarm.

[0039] Preferably, the data processing unit removes duplicate data and missing data from the original data by directly deleting duplicate data and missing data, and removes abnormal data by using the standard deviation method, the interquartile range method, or the Z-score method.

[0040] Preferably, the static segment extraction unit is further used for:

[0041] 3.1. According to the current information in the cleaned data, the moment when the absolute value of the current is less than the current setting threshold is determined as the static moment;

[0042] 3.2. Determine the data whose time interval between two adjacent rows is less than the first time setting threshold as the same continuous time segment to obtain a static time segment;

[0043] 3.3. Extract the time information and voltage information of the start time and the end time from the static time segment, and determine the time segment whose time interval between the start time and the end time is greater than the second time setting threshold as a long static segment.

[0044] Preferably, the consistency calculation unit converts the highest voltage and the lowest voltage screened out in the long static segment into the corresponding highest capacity and the lowest capacity according to the open circuit voltage method, and selects the voltage interval with a large change rate on the SOC-OCV curve when using the open circuit voltage method for capacity conversion.

[0045] Preferably, the abnormal diagnosis and alarm unit selects outlier single cells from abnormal cells and specifically includes:

[0046] 5.1. Use the open circuit voltage method to convert all single cell voltages in abnormal batteries into single cell capacity;

[0047] 5.2. Filter out the number of other monomer capacities within the capacity setting threshold range of each monomer capacity as the outlier value of the monomer capacity;

[0048] 5.3. Extract the minimum value from all outliers, and subtract the minimum value from the outlier value of each single cell capacity to obtain the calculation result of each single cell. The single cell whose calculation result is less than the outlier set threshold is determined as a single cell with outlier capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flow chart of a method for diagnosing consistency of an energy storage battery provided by an embodiment of the present invention;

[0050] Figure 2 This is a schematic diagram of the SOC-OCV curve of a lithium iron phosphate battery;

[0051] Figure 3 Schematic diagram of SOC-OCV curve of ternary battery;

[0052] Figure 4 A schematic diagram of a consistency diagnosis device for an energy storage battery provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the technical solution of the present invention is clearly and completely described below in combination with specific embodiments and with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Related terms:

[0055] Battery capacity is a measure of the amount of charge a battery can store and release. It is usually used to measure the duration of current or the total amount of power a battery can supply under specific conditions. The common unit of measurement for battery capacity is ampere-hour, which is equal to the amount of charge a battery can release or receive in one hour at a current of one ampere.

[0056] The open circuit voltage method is one of the commonly used methods to measure the battery state of charge. It is based on the relationship between the open circuit voltage and the charge state of the battery. It infers the current charge state of the battery by measuring the voltage of the battery in the open circuit state.

[0057] Battery consistency refers to the similarity or consistency in performance and characteristics of different battery cells in the same batch or system. This consistency covers the similarity in battery capacity, internal resistance, charge and discharge characteristics, life, etc. The present invention mainly refers to the consistency of capacity. Battery consistency has a significant impact on the performance, life, safety and maintenance cost of the battery system.

[0058] like Figure 1 As shown, the present invention provides a consistency diagnosis method for an energy storage battery, comprising the following steps:

[0059] Step 1: Collect raw data of the energy storage battery at a set time interval, and sort the raw data according to the time field;

[0060] Step 2: Clean the original data to obtain cleaned data, where the cleaning includes removing duplicate data, missing data, and abnormal data;

[0061] Step 3: extract all static time segments of the energy storage battery according to the current information in the cleaned data, and select the long static segments;

[0062] Step 4: According to the voltage information in the long static segment, the highest voltage and the lowest voltage are screened out, and converted into the corresponding highest capacity and the lowest capacity, and the difference between the highest capacity and the lowest capacity is calculated. When the difference is greater than the set threshold, it is determined to be an abnormal battery;

[0063] Step 5: Filter out the single cells with outlier capacity from the abnormal batteries and issue an abnormal alarm.

[0064] The present invention first extracts the static time segment of the energy storage battery according to the current information, and selects the long static segment, then selects the two batteries with the highest voltage and the lowest voltage in the long static segment, and converts the highest voltage and the lowest voltage into the corresponding highest capacity and the lowest capacity, and uses the difference between the highest capacity and the lowest capacity as the basis for determining the abnormal battery, and then selects the single cell with outlier capacity, and timely captures and analyzes the performance difference of the single cell in the whole energy storage system. Compared with the diagnosis method based on pressure difference, the consistency diagnosis of the capacity of the battery cell according to the present invention can more accurately evaluate and judge the energy performance of the energy storage cell during the charging and discharging process, and can more comprehensively consider the actual energy storage and release capacity of the battery.

[0065] Example 1

[0066] This embodiment takes a lithium iron phosphate battery as an example to introduce a consistency diagnosis method for an energy storage battery. The consistency diagnosis method includes the following steps:

[0067] Step 1: Collect the raw data of the energy storage battery at intervals of 15 seconds. The raw data includes the time, position, current, voltage and other data of the energy storage battery, and sort the raw data according to the time field;

[0068] Step 2: Clean the original data to obtain the cleaned data. Cleaning includes removing duplicate data, missing data and abnormal data. When removing duplicate data and missing data, directly delete the duplicate data and missing data. When removing abnormal data, use the standard deviation method to process and delete it. Take the voltage abnormal value as an example for processing:

[0069]

[0070] Among them, x i is the i-th data collected, is the mean value of all data, and s is the standard deviation of all data.

[0071] After calculating the mean value and standard deviation, remove the voltage less than and voltage greater than All data.

[0072] Step 3: extract all static time segments of the energy storage battery according to the current information in the cleaned data, and select the long static segments; specifically, the following processes are included:

[0073] 3.1. According to the current information in the cleaned data, the moment when the absolute value of the current is less than 3A is determined as the static moment;

[0074] 3.2. Determine the data with a time interval of less than 180s between two adjacent rows as the same continuous time segment to obtain a static time segment;

[0075] 3.3. Extract the time information and voltage information of the start and end times from the static time segments, and determine the time segments with a time interval between the start and end times greater than 10 minutes as long static segments.

[0076] Step 4: According to the voltage information in the long static segment, the highest voltage and the lowest voltage are screened out, and converted into the corresponding highest capacity and lowest capacity. The difference between the highest capacity and the lowest capacity is calculated. When the difference is greater than 10%, it is determined to be an abnormal battery; according to the open circuit voltage method, the highest voltage and the lowest voltage screened out in the long static segment are converted into the corresponding highest capacity and the lowest capacity. When using the open circuit voltage method for capacity conversion, the voltage range with a large change rate on the SOC-OCV curve is selected. Figure 2 As shown, on the SOC-OCV curve of the lithium iron phosphate battery, a voltage range of (maximum voltage less than 3.28V) or (maximum voltage greater than 3.28V and minimum voltage less than 3.25V) is selected for conversion. Using the open circuit voltage method for capacity conversion, selecting a voltage range with a large rate of change on the SOC-OCV curve can directly correspond to the actual energy storage of the battery and accurately describe the actual energy storage and release capabilities of the battery.

[0077] Step 5: Filter out the single cells with outlier capacity from the abnormal batteries and issue an abnormal alarm.

[0078] Screening out cells with outlier capacity from abnormal cells specifically includes:

[0079] 5.1. Use the open circuit voltage method to convert all single cell voltages in abnormal batteries into single cell capacity;

[0080] 5.2. The number of other monomer capacities within the range of ±5% of each monomer capacity is selected as the outlier value of the monomer capacity to determine the degree of outlier of the monomer capacity;

[0081] 5.3. Extract the minimum value from all outliers, and subtract the minimum value from the outlier value of each single cell capacity to obtain the calculation result of each single cell. The single cell with a calculation result less than 5 is determined as a single cell with outlier capacity.

[0082] Using a simple threshold method to screen out cells with outlier capacity from abnormal batteries can greatly reduce the resources and computing time required for operation, increase computing speed, complete diagnosis and response in a shorter time, improve real-time performance and diagnostic efficiency, and reduce the operating cost of the energy management system.

[0083] Abnormal alarm is issued according to the corresponding diagnostic information. When the alarm is issued, the abnormal level is indicated according to the difference between the calculated maximum capacity and the minimum capacity, and the time, location and corresponding outlier single battery are indicated, which can remind relevant personnel to take corresponding treatment measures for timely maintenance. When the abnormal alarm is issued, the difference between the maximum capacity and the minimum capacity greater than 10% is marked as level 1 abnormality, the difference between the maximum capacity and the minimum capacity greater than 15% is marked as level 2 abnormality, the difference between the maximum capacity and the minimum capacity greater than 20% is marked as level 3 abnormality, and the difference between the maximum capacity and the minimum capacity greater than 25% is marked as level 4 abnormality. Various abnormal levels are indicated by digital status codes, with level 1 abnormality as 1, level 2 abnormality as 2, level 3 abnormality as 3, and level 4 abnormality as 4.

[0084] Example 2

[0085] This embodiment takes a ternary battery as an example to introduce a consistency diagnosis method for an energy storage battery. The consistency diagnosis method includes the following steps:

[0086] Step 1: Collect the raw data of the energy storage battery at 10s intervals. The raw data includes the time, position, current, voltage and other data of the energy storage battery, and sort the raw data according to the time field;

[0087] Step 2: Clean the original data to obtain the cleaned data. Cleaning includes removing duplicate data, missing data and abnormal data. When removing duplicate data and missing data, directly delete the duplicate data and missing data. When removing abnormal data, use the Z-score method to process and delete it. Take the voltage abnormal value as an example for processing:

[0088]

[0089] Among them, x i is the i-th data collected, is the mean of all data, s is the standard deviation of all data, and Z is the Z score value.

[0090] After calculating the Z score, all data with a Z score less than 3 and a Z score greater than 3 are eliminated.

[0091] Step 3: extract all static time segments of the energy storage battery according to the current information in the cleaned data, and select the long static segments; specifically, the following processes are included:

[0092] 3.1. According to the current information in the cleaned data, the moment when the absolute value of the current is less than 5A is determined as the static moment;

[0093] 3.2. Determine the data with a time interval of less than 130s between two adjacent rows as the same continuous time segment to obtain a static time segment;

[0094] 3.3. Extract the time information and voltage information of the start and end times from the static time segments, and determine the time segments with a time interval between the start and end times greater than 8 minutes as long static segments.

[0095] Step 4: According to the voltage information in the long static segment, the highest voltage and the lowest voltage are screened out, and converted into the corresponding highest capacity and lowest capacity. The difference between the highest capacity and the lowest capacity is calculated. When the difference is greater than 15%, it is determined to be an abnormal battery; according to the open circuit voltage method, the highest voltage and the lowest voltage screened out in the long static segment are converted into the corresponding highest capacity and the lowest capacity. When using the open circuit voltage method for capacity conversion, select the voltage range with a large change rate on the SOC-OCV curve. Figure 3 As shown, a voltage range with a maximum voltage less than 4.2V and a minimum voltage greater than 3.5V is selected for conversion on the SOC-OCV curve of the ternary battery.

[0096] Step 5: Filter out the single cells with outlier capacity from the abnormal batteries and issue an abnormal alarm.

[0097] Screening out cells with outlier capacity from abnormal cells specifically includes:

[0098] 5.1. Use the open circuit voltage method to convert all single cell voltages in abnormal batteries into single cell capacity;

[0099] 5.2. The number of other monomer capacities within the range of ±8% of each monomer capacity is selected as the outlier value of the monomer capacity to determine the degree of outlier of the monomer capacity;

[0100] 5.3. Extract the minimum value from all outliers, and subtract the minimum value from the outlier value of each single cell capacity to obtain the calculation result of each single cell. The single cell with a calculation result less than 3 is determined as a single cell with outlier capacity.

[0101] Abnormal alarm is made according to the corresponding diagnostic information. When making an alarm, the abnormal level is indicated according to the difference between the calculated maximum capacity and the minimum capacity, and the time, location and corresponding outlier battery are indicated. When making an abnormal alarm, the difference between the maximum capacity and the minimum capacity greater than 15% is marked as level 1 abnormality, the difference between the maximum capacity and the minimum capacity greater than 20% is marked as level 2 abnormality, the difference between the maximum capacity and the minimum capacity greater than 25% is marked as level 3 abnormality, and the difference between the maximum capacity and the minimum capacity greater than 30% is marked as level 4 abnormality. Various abnormal levels are indicated by digital status codes, with level 1 abnormality as 1, level 2 abnormality as 2, level 3 abnormality as 3, and level 4 abnormality as 4.

[0102] Example 3

[0103] The present invention also provides a consistency diagnosis device for an energy storage battery, comprising:

[0104] The data acquisition unit is used to collect the raw data of the energy storage battery at a set time interval, the raw data including the time, position, current, voltage and other data of the energy storage battery, and sort the raw data according to the time field;

[0105] The data processing unit is used to clean the original data to obtain the cleaned data, wherein the cleaning includes eliminating duplicate data, missing data and abnormal data; the data processing unit eliminates duplicate data and missing data in the original data by directly deleting duplicate data and missing data, and eliminates abnormal data by using the standard deviation method, the interquartile range method or the Z-score method.

[0106] The static segment extraction unit is used to extract all static time segments of the energy storage battery according to the current information in the cleaned data, and filter out the long static segments; the static segment extraction unit is also used to:

[0107] 3.1. According to the current information in the cleaned data, the moment when the absolute value of the current is less than the current setting threshold is determined as the static moment;

[0108] 3.2. Determine the data whose time interval between two adjacent rows is less than the first time setting threshold as the same continuous time segment to obtain a static time segment;

[0109] 3.3. Extract the time information and voltage information of the start time and the end time from the static time segment, and determine the time segment whose time interval between the start time and the end time is greater than the second time setting threshold as a long static segment.

[0110] The consistency calculation unit is used to screen out the highest voltage and the lowest voltage according to the voltage information in the long static segment, and convert them into the corresponding highest capacity and lowest capacity, calculate the difference between the highest capacity and the lowest capacity, and when the difference is greater than the set threshold, it is determined to be an abnormal battery; the consistency calculation unit converts the highest voltage and the lowest voltage screened out in the long static segment into the corresponding highest capacity and the lowest capacity according to the open circuit voltage method, and selects the voltage range with a large change rate on the SOC-OCV curve when using the open circuit voltage method for capacity conversion.

[0111] The abnormal diagnosis alarm unit is used to screen out the single cells with outlier capacity from the abnormal batteries and issue an abnormal alarm.

[0112] The abnormal diagnosis alarm unit selects outlier cells from abnormal cells by:

[0113] 5.1. Use the open circuit voltage method to convert all single cell voltages in abnormal batteries into single cell capacity;

[0114] 5.2. Filter out the number of other monomer capacities within the capacity setting threshold range of each monomer capacity as the outlier value of the monomer capacity;

[0115] 5.3. Extract the minimum value from all outliers, and subtract the minimum value from the outlier value of each single cell capacity to obtain the calculation result of each single cell. The single cell whose calculation result is less than the outlier set threshold is determined as a single cell with outlier capacity.

[0116] When an abnormal alarm is issued, the difference between the highest capacity and the lowest capacity is used to divide the abnormal level. The larger the difference, the higher the abnormal level and the more serious the abnormality. The abnormal level is indicated by a digital status code.

[0117] The present invention also provides a storage battery consistency diagnosis device, comprising:

[0118] A memory for storing computer instructions for executing a method for diagnosing consistency of an energy storage battery;

[0119] A processor is used to execute computer instructions for implementing the steps of a method for diagnosing consistency of an energy storage battery.

[0120] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a computer processor, the steps of the above-mentioned energy storage battery consistency diagnosis method are implemented.

[0121] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A consistency diagnosis method for energy storage batteries, characterized in that: include: Step 1: Collect raw data of the energy storage battery at a set time interval, and sort the raw data according to the time field; Step 2: Clean the original data to obtain cleaned data, where the cleaning includes removing duplicate data, missing data, and abnormal data; Step 3: extract all static time segments of the energy storage battery according to the current information in the cleaned data, and select the long static segments; Step 4: According to the voltage information in the long static segment, the highest voltage and the lowest voltage are screened out, and converted into the corresponding highest capacity and the lowest capacity, and the difference between the highest capacity and the lowest capacity is calculated. When the difference is greater than the set threshold, it is determined to be an abnormal battery; Step 5: Filter out the single cells with outlier capacity from the abnormal batteries and issue an abnormal alarm.

2. The consistency diagnosis method of the energy storage battery according to claim 1, characterized in that: In step 2, duplicate data and missing data in the original data are removed by directly deleting duplicate data and missing data, and abnormal data are removed by using the standard deviation method, the interquartile range method, or the Z-score method.

3. The consistency diagnosis method of the energy storage battery according to claim 1, characterized in that: The step 3 comprises: 3.

1. According to the current information in the cleaned data, the moment when the absolute value of the current is less than the current setting threshold is determined as the static moment; 3.

2. Determine the data whose time interval between two adjacent rows is less than the first time setting threshold as the same continuous time segment to obtain a static time segment; 3.

3. Extract the time information and voltage information of the start time and the end time from the static time segment, and determine the time segment whose time interval between the start time and the end time is greater than the second time setting threshold as a long static segment.

4. The consistency diagnosis method of the energy storage battery according to claim 1, characterized in that: In step 4, the highest voltage and the lowest voltage screened out in the long static segment are converted into the corresponding highest capacity and the lowest capacity according to the open circuit voltage method, and when the open circuit voltage method is used for capacity conversion, a voltage interval with a large rate of change on the SOC-OCV curve is selected.

5. The consistency diagnosis method of the energy storage battery according to claim 1, characterized in that: The step 5 of selecting the single cells with outlier capacity from the abnormal cells specifically includes: 5.

1. Use the open circuit voltage method to convert all single cell voltages in abnormal batteries into single cell capacity; 5.

2. Filter out the number of other monomer capacities within the capacity setting threshold range of each monomer capacity as the outlier value of the monomer capacity; 5.

3. Extract the minimum value from all outliers, and subtract the minimum value from the outlier value of each single cell capacity to obtain the calculation result of each single cell. The single cell whose calculation result is less than the outlier set threshold is determined as a single cell with outlier capacity.

6. The consistency diagnosis method of the energy storage battery according to claim 1, characterized in that: When an abnormal alarm is performed in step 5, the difference between the highest capacity and the lowest capacity is used to classify the abnormality level. The larger the difference, the higher the abnormality level and the more serious the abnormality. The abnormality level is indicated by a digital status code.

7. A consistency diagnosis device for energy storage batteries, characterized in that: include: A data acquisition unit, used to collect raw data of the energy storage battery at set time intervals and sort the raw data according to the time field; A data processing unit is used to clean the original data to obtain cleaned data, wherein the cleaning includes removing duplicate data, missing data and abnormal data; A static segment extraction unit is used to extract all static time segments of the energy storage battery according to the current information in the cleaned data, and to filter out the long static segments; A consistency calculation unit is used to screen out the highest voltage and the lowest voltage according to the voltage information in the long static segment, convert them into the corresponding highest capacity and the lowest capacity, calculate the difference between the highest capacity and the lowest capacity, and determine the battery as abnormal when the difference is greater than a set threshold; The abnormal diagnosis alarm unit is used to screen out the single cells with outlier capacity from the abnormal batteries and issue an abnormal alarm.

8. The consistency diagnosis device for energy storage batteries according to claim 7, characterized in that: The data processing unit removes duplicate data and missing data from the original data by directly deleting duplicate data and missing data, and removes abnormal data using the standard deviation method, the interquartile range method, or the Z-score method.

9. The consistency diagnosis device for energy storage batteries according to claim 7, characterized in that: The static segment extraction unit is also used for: 3.

1. According to the current information in the cleaned data, the moment when the absolute value of the current is less than the current setting threshold is determined as the static moment; 3.

2. Determine the data whose time interval between two adjacent rows is less than the first time setting threshold as the same continuous time segment to obtain a static time segment; 3.

3. Extract the time information and voltage information of the start time and the end time from the static time segment, and determine the time segment whose time interval between the start time and the end time is greater than the second time setting threshold as a long static segment.

10. The consistency diagnosis device for energy storage batteries according to claim 7, characterized in that: The consistency calculation unit converts the highest voltage and the lowest voltage screened out in the long static segment into the corresponding highest capacity and the lowest capacity according to the open circuit voltage method, and selects the voltage interval with a large change rate on the SOC-OCV curve when using the open circuit voltage method for capacity conversion.

Images