Method for detecting and positioning inconsistent abnormal monomers of battery cluster

Abstract

The invention discloses a method for detecting and positioning inconsistent abnormal cells of a battery cluster, and belongs to the field of lithium ion battery detection.The method comprises the steps that firstly, charge-discharge cycles are divided based on a charge-discharge strategy of the battery cluster and upper and lower cut-off voltages; according to charging and discharging upper and lower cut-off voltages of a battery cluster, appropriate feature extraction voltages are selected to influence inconsistent monomers of the available capacity of the battery pack, then the inconsistent monomers are further diagnosed and positioned by further utilizing the residual chargeable electric quantity and the residual dischargeable electric quantity, and the battery pack available capacity can be further diagnosed and positioned according to the characteristics of the available capacity of the battery pack. According to the method, real-time detection and positioning can be carried out based on the voltage of the single batteries, the single batteries with relatively high comprehensive inconsistency degree can be preferentially positioned, additional parameter detection is not needed, convenience and rapidness are realized, the inconsistent single batteries can be conveniently and timely processed, and the service life, the performance and the safety of the battery cluster during use are ensured.

Description

Technical Field

[0001] The invention belongs to the field of lithium ion battery detection, and in particular relates to a method for detecting and locating inconsistency abnormal monomers in a battery cluster. Background Art

[0002] Lithium-ion batteries are rechargeable batteries with advantages such as high energy density, long cycle life, and no memory effect. They are widely used in civilian fields such as electric vehicles and energy storage. However, due to the limitations of current single-cell manufacturing technology, when building a power battery pack, it is usually necessary to connect multiple single-cell batteries in series or in parallel to form a battery cluster to meet the actual performance requirements of electric vehicles.

[0003] However, there are often certain differences between the individual cells in the power battery pack. This phenomenon is called the battery consistency problem. The more individual cells there are in the battery cluster, the more obvious the consistency problem is. The battery consistency problem will lead to a series of negative effects such as shortened battery life, reduced battery pack performance and reduced safety.

[0004] Traditional technologies mostly use single parameter evaluation, multi-parameter evaluation and dynamic characteristic evaluation methods to evaluate the parameters of battery inconsistency;

[0005] The single parameter evaluation method only selects one parameter to evaluate the consistency, which cannot reflect the real condition of the battery well;

[0006] Although the multi-parameter evaluation method evaluates battery consistency by combining two or more parameters such as voltage, capacity, internal resistance and discharge rate through clustering or information fusion, it has problems such as poor real-time performance, sensitivity to environmental factors, poor application scope and high cost.

[0007] Dynamic characteristic evaluation methods include battery internal dynamic process evaluation and charge and discharge characteristic curve evaluation. The internal dynamic process evaluation requires special equipment and a long test cycle, which is not conducive to actual production. Although the consistency evaluation based on the charge and discharge characteristic curve can more comprehensively reflect the comprehensive performance of the battery and introduce the charge and discharge voltage into the consistency evaluation model, making the evaluation parameters dynamic variables, this method requires a large number of batteries and experimental data support, is costly, complex to operate, and is not convenient for real-time detection. It is particularly inconvenient to use the above method when testing a large number of batteries, such as a battery cluster consisting of more than 400 batteries. Summary of the invention

[0008] 1. Technical issues to be resolved

[0009] In order to overcome the shortcomings of the existing technology, a method for detecting and locating inconsistent abnormal cells in a battery cluster is proposed, so as to solve the problem that the existing technology is not convenient for real-time detection and positioning of inconsistent cells in a battery cluster composed of a large number of batteries when used, and the detection parameters are single, the cost is high and the scope of application is poor, which leads to the situation that a battery cluster composed of a large number of batteries is prone to shortened life, reduced battery pack performance and reduced safety due to the inability to handle the inconsistency problem in time.

[0010] (II) Technical solution

[0011] The present invention is implemented through the following technical solutions: The present invention proposes a method for detecting and locating inconsistency abnormal cells in a battery cluster, which can comprehensively and accurately evaluate the inconsistency batteries in the lithium-ion battery cluster, facilitate timely positioning and processing of abnormal batteries, and prevent the battery cluster from performance degradation and failure, and is particularly suitable for battery clusters composed of a large number of batteries;

[0012] The following steps are involved:

[0013] Step 1: During the charging and operation of the energy storage system, the battery management system collects data from the batteries in the battery cluster in real time and performs data preprocessing;

[0014] First, collect various data of the batteries in the battery cluster, including time, total voltage, current, single cell maximum voltage, single cell minimum voltage, single cell maximum temperature, single cell minimum temperature, battery single cell voltage and other data, record and store the above data;

[0015] Finally, the above data is preprocessed by removing duplicate values, sorting disordered values, removing abnormal values ​​and filling missing values, so that the above data can be cleaned before fault diagnosis to improve data quality and increase the accuracy and reliability of subsequent algorithms. The screened valid battery cell voltage data is stored and recorded. The battery voltage data is expressed as:

[0016]

[0017] Where m is the number of battery cells in the battery pack, and n is the number of representative sampling points;

[0018] Step 2: In order to comprehensively consider the inconsistency of abnormal cells in the battery cluster that restrict the available capacity of the battery cluster, the first cell to reach the upper cut-off voltage V upper The battery A1 has a discharge cut-off voltage V lower Perform preliminary inconsistency level diagnosis on battery A2;

[0019] First, according to the charging cut-off voltage V specified by the battery cluster upper and discharge cut-off voltage V lower , set a fixed charging high-end voltage Vcharge With the discharge low end voltage V discharge ;

[0020] Secondly, according to the charging high-end voltage V of the battery cluster charge With the discharge low end voltage V discharge , find the two cells A1 and A2 in the battery cluster that reach the upper cut-off voltage of charging and the lower cut-off voltage of discharging the fastest, and calculate the voltage difference V between these two cells and the median of all batteries at the corresponding time diff ;

[0021] Furthermore, the voltage difference V of battery A1 diff The calculation formula is:

[0022] V diff1 =V charge -V meidian1

[0023] Among them, V meidian1 It is the median voltage of all battery cells at the sampling time when charging to the high-end voltage, V diff1 is the voltage difference between the A1 battery and the median voltage;

[0024] The voltage difference of battery A2 is V diff The calculation formula is:

[0025] V diff2 =V discharge -V meidian2

[0026] Among them, V meidian2 It is the median voltage of all battery cells at the sampling time when they are discharged to the low-end voltage, V diff2 is the voltage difference between the A2 battery and the median voltage;

[0027] Finally, fixed thresholds threshold1 and threshold2 are set, and the number of times that batteries A1 and A2 exceed the thresholds in a specified charge and discharge cycle is used to determine whether the inconsistency between the two cells significantly reduces the available capacity of the battery cluster.

[0028] Furthermore, the determination method of whether the number of times that the battery A1 and the battery A2 exceed the fixed threshold is as follows:

[0029]

[0030] Among them, Alarm1 and Alarm2 represent whether the battery A1 and battery A2 exceed the threshold value in this charge and discharge cycle respectively. According to the data to be detected and the detection sensitivity requirements, when A1 exceeds the threshold value threshold1 for m consecutive charge cycles, and when A2 exceeds the threshold value threshold2 for n consecutive charge cycles, it is determined that there is a large inconsistency, which significantly affects the available capacity of the battery cluster;

[0031] Step 3: According to the charging high-end voltage V charge With the discharge low end voltage V discharge , calculate the remaining chargeable capacity QR of the monomer with larger inconsistency i1 and the remaining discharge capacity QD i2 ;

[0032] First, calculate the remaining chargeable capacity QR i1 and the remaining discharge capacity QD i2 , calculated as follows:

[0033]

[0034] Where t_charge0 means that A1, the first battery, reaches the high-end charging voltage V charge time, and t_charge i1 Indicates that the i1th battery reaches the high-end charging voltage V charge Time, QR i1 Indicates the remaining chargeable capacity of the i1th battery; similarly, t_discharge0 indicates that A2 is the first to reach the low-end discharge voltage V discharge time, and t_discharge i2 Indicates that the i2th reaches the discharge low end voltage V discharge Time, QD i2 Indicates the remaining dischargeable capacity of the i2th battery;

[0035] Finally, fixed thresholds threshold3 and threshold4 are set to determine whether there are other inconsistency abnormal cells that affect the available capacity of the battery except battery A1 and battery A2. The determination method is as follows:

[0036]

[0037] Among them, Alarm3 i1 Represents the i1th reaching the charging high-end voltage V charge Battery cell A_charge i1 In a charging cycle, whether the threshold value threshold3 is exceeded, the battery cell A_charge is set according to the data to be detected and the detection sensitivity requirements.i1 When the number of consecutive charging cycles exceeds the threshold value threshold3, it is determined that there is a large inconsistency, which significantly affects the available capacity of the battery cluster;

[0038]

[0039] Among them, Alarm4 i2 Represents the i2th high-end discharge voltage V discharge A_discharge of battery cells i2 Whether a discharge cycle exceeds the threshold value threshold4, according to the data to be detected and the detection sensitivity requirements, it is set that when A_dischargei2 exceeds the threshold value threshold4 for q consecutive discharge cycles, it is determined that there is a large inconsistency, which significantly affects the available capacity of the battery cluster;

[0040] Step 4: Set a time window of length L, based on the pressure difference V diff , and the changes in the remaining chargeable capacity and the remaining dischargeable capacity of the battery cells in this time window, and the changes in the inconsistency degree of these cells are obtained;

[0041] First, according to the voltage difference V diff , Remaining chargeable capacity QR i1 and the remaining discharge capacity QD i2 The sliding time window L is used to iteratively calculate the respective linear regression slopes as follows:

[0042] First, define the window and variables: Assume that the data sequence is A = [A1, A2, ..., An], there are n cycles in total, the time window length is L, and for each starting position j from 1 to n-L+1, calculate the linear regression slope of Aj, Aj+1, ...Aj+L-1 in the window. The calculation formula of the linear regression slope is as follows:

[0043]

[0044] Where aj represents the linear regression slope of the jth time window, V diff , QR i1 and QD i2 Substituting into the above formulas respectively, we get the linear regression slope a1 j , a2 j ,a3i1 j and a4i2 j ;

[0045] Secondly, set fixed thresholds threshold5, threshold6, threshold7 and threshold8 to determine whether there are continuous cases of exceeding the thresholds, and then determine whether the inconsistency of these monomers is further deepened.

[0046] Finally, set the number of times that the threshold can be exceeded in a specific number of cycles, and trigger an alarm when the number is exceeded;

[0047] Furthermore, the formula for determining whether to further deepen is as follows:

[0048]

[0049] in, is the indicator function, when a1 (t-k) When the threshold is exceeded, its value is 1, otherwise it is 0; when the a1 values ​​of the past z5 cycles are all greater than the threshold, the sum is z5, which means that the condition for triggering an alarm is met; Alarm7 i1 Represents the i1th reaching the charging high-end voltage V charge The battery cell A_chargei1 exceeds the threshold value threshold7 for z7 consecutive charging cycles.

[0050] (III) Beneficial effects

[0051] One of the above technical solutions has the following advantages or beneficial effects:

[0052] In order to solve the problem that the existing technology is not convenient for real-time detection and positioning of inconsistent cells in a battery cluster composed of a large number of batteries when used, and the detection parameters are single, the cost is high and the application range is poor, which leads to the situation that a battery cluster composed of a large number of batteries is prone to shortened life, battery pack performance degradation and reduced safety due to the inability to handle inconsistency problems in time, the battery management system collects various data of the battery cluster in real time, removes duplicate values, sorts disordered values, removes abnormal values ​​and fills missing values ​​for the data, and then sorts and stores the voltage data, and automatically selects the batteries in the battery cluster for voltage difference calculation according to the charge and discharge strategy and the characteristics of the upper and lower cut-off voltages of charge and discharge, and then calculates the voltage difference through the battery management system. The pressure difference is compared with the set threshold to make a preliminary record of the inconsistent cells that affect the available capacity of the battery pack. Then, the remaining rechargeable power and the remaining dischargeable power are used to further diagnose and locate the inconsistent cells, so as to achieve real-time positioning while preventing the situation of single detection parameters. At the same time, the detection cost is low and the application range is wide. It is less affected by the environment. Both lithium iron phosphate and ternary lithium batteries are compatible, which is convenient for timely detection and positioning of inconsistent battery cells in a battery cluster composed of a large number of batteries, and can give priority to positioning cells with a relatively large degree of comprehensive inconsistency. No additional parameter detection is required, which is convenient and fast, and easy to handle inconsistent single cells in time to ensure the life, performance and safety of the battery cluster during use. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0054] Figure 1 A flow chart of the battery cluster inconsistency abnormal monomer detection of the present invention;

[0055] Figure 2 The flowchart of the algorithm for detecting inconsistent cells in a battery cluster according to the present invention;

[0056] Figure 3 The flowchart of the algorithm for detecting the degree of change of inconsistent monomers in a battery cluster according to the present invention; DETAILED DESCRIPTION

[0057] The present invention is further described in detail below in conjunction with examples, but the embodiments of the present invention are not limited thereto.

[0058] The present invention provides a method for detecting and locating inconsistency abnormal cells in a battery cluster, comprising the following steps:

[0059] Step 1: During the charging and operation of the energy storage system, the battery management system collects data from the batteries in the battery cluster in real time and performs data preprocessing;

[0060] First, collect various data of the batteries in the battery cluster, including time, total voltage, current, single cell maximum voltage, single cell minimum voltage, single cell maximum temperature, single cell minimum temperature, battery single cell voltage and other data, record and store the above data;

[0061] Finally, the above data is preprocessed by removing duplicate values, sorting disordered values, removing abnormal values ​​and filling missing values, so that the above data can be cleaned before fault diagnosis to improve data quality and increase the accuracy and reliability of subsequent algorithms. The screened valid battery cell voltage data is stored and recorded. The battery voltage data is expressed as:

[0062]

[0063] Where m is the number of battery cells in the battery pack, and n is the number of representative sampling points;

[0064] Step 2: In order to comprehensively consider the inconsistency of abnormal cells in the battery cluster that restrict the available capacity of the battery cluster, the first cell to reach the upper cut-off voltage V upper The battery A1 has a discharge cut-off voltage V lower Perform preliminary inconsistency level diagnosis on battery A2;

[0065] First, according to the charging cut-off voltage V specified by the battery cluster upper and discharge cut-off voltage V lower , set a fixed charging high-end voltage V charge With the discharge low end voltage V discharge ;

[0066] Secondly, according to the charging high-end voltage V of the battery cluster charge With the discharge low end voltage V discharge , find the two cells A1 and A2 in the battery cluster that reach the upper cut-off voltage of charging and the lower cut-off voltage of discharging the fastest, and calculate the voltage difference V between these two cells and the median of all batteries at the corresponding time diff ;

[0067] Furthermore, the voltage difference V of battery A1 diff The calculation formula is:

[0068] V diff1 =V charge -V meidian1

[0069] Among them, V meidian1 It is the median voltage of all battery cells at the sampling time when charging to the high-end voltage, V diff1 is the voltage difference between the A1 battery and the median voltage;

[0070] The voltage difference of battery A2 is V diffThe calculation formula is:

[0071] V diff2 =V discharge -V meidian2

[0072] Among them, V meidian2 It is the median voltage of all battery cells at the sampling time when they are discharged to the low-end voltage, V diff2 is the voltage difference between the A2 battery and the median voltage;

[0073] Finally, fixed thresholds threshold1 and threshold2 are set, and the number of times that batteries A1 and A2 exceed the thresholds in a specified charge and discharge cycle is used to determine whether the inconsistency between the two cells significantly reduces the available capacity of the battery cluster.

[0074] Furthermore, the determination method of whether the number of times that the battery A1 and the battery A2 exceed the fixed threshold is as follows:

[0075]

[0076] Among them, Alarm1 and Alarm2 represent whether the battery A1 and battery A2 exceed the threshold value in this charge and discharge cycle respectively. According to the data to be detected and the detection sensitivity requirements, when A1 exceeds the threshold value threshold1 for m consecutive charge cycles, and when A2 exceeds the threshold value threshold2 for n consecutive charge cycles, it is determined that there is a large inconsistency, which significantly affects the available capacity of the battery cluster;

[0077] Step 3: According to the charging high-end voltage V charge With the discharge low end voltage V discharge , calculate the remaining chargeable capacity QR of the monomer with larger inconsistency i1 and the remaining discharge capacity QD i2 ;

[0078] First, calculate the remaining chargeable capacity QR i1 and the remaining discharge capacity QD i2 , calculated as follows:

[0079]

[0080] Where t_charge0 means that A1, the first battery, reaches the high-end charging voltage V charge time, and t_charge i1 Indicates that the i1th battery reaches the high-end charging voltage V charge Time, QR i1Indicates the remaining chargeable capacity of the i1th battery; similarly, t_discharge0 indicates that A2 is the first to reach the low-end discharge voltage V discharge time, and t_discharge i2 Indicates that the i2th reaches the discharge low end voltage V discharge Time, QD i2 Indicates the remaining dischargeable capacity of the i2th battery;

[0081] Finally, fixed thresholds threshold3 and threshold4 are set to determine whether there are other inconsistency abnormal cells that affect the available capacity of the battery except battery A1 and battery A2. The determination method is as follows:

[0082]

[0083] Among them, Alarm3 i1 Represents the i1th reaching the charging high-end voltage V charge Battery cell A_charge i1 In a charging cycle, whether the threshold value threshold3 is exceeded, the battery cell A_charge is set according to the data to be detected and the detection sensitivity requirements. i1 When the number of consecutive charging cycles exceeds the threshold value threshold3, it is determined that there is a large inconsistency, which significantly affects the available capacity of the battery cluster;

[0084]

[0085] Among them, Alarm4 i2 Represents the i2th high-end discharge voltage V discharge A_discharge of battery cells i2 Whether a discharge cycle exceeds the threshold value threshold4, according to the data to be detected and the detection sensitivity requirements, it is set that when A_dischargei2 exceeds the threshold value threshold4 for q consecutive discharge cycles, it is determined that there is a large inconsistency, which significantly affects the available capacity of the battery cluster;

[0086] Step 4: Set a time window of length L, based on the pressure difference V diff , and the changes in the remaining chargeable capacity and the remaining dischargeable capacity of the battery cells in this time window, and the changes in the inconsistency degree of these cells are obtained;

[0087] First, according to the voltage difference V diff , Remaining chargeable capacity QR i1 and the remaining discharge capacity QD i2The sliding time window L is used to iteratively calculate the respective linear regression slopes as follows:

[0088] First, define the window and variables: Assume that the data sequence is A = [A1, A2, ..., An], there are n cycles in total, the time window length is L, and for each starting position j from 1 to n-L+1, calculate the linear regression slope of Aj, Aj+1, ...Aj+L-1 in the window. The calculation formula of the linear regression slope is as follows:

[0089]

[0090] Where aj represents the linear regression slope of the jth time window, V diff , QR i1 and QD i2 Substituting into the above formulas respectively, we get the linear regression slope a1 j , a2 j ,a3i1 j and a4i2 j ;

[0091] Secondly, set fixed thresholds threshold5, threshold6, threshold7 and threshold8 to determine whether there are continuous cases of exceeding the thresholds, and then determine whether the inconsistency of these monomers is further deepened.

[0092] Finally, set the number of times that the threshold can be exceeded in a specific number of cycles, and trigger an alarm when the number is exceeded;

[0093] Furthermore, the formula for determining whether to further deepen is as follows:

[0094]

[0095] in, is the indicator function, when a1 (t-k) When the threshold is exceeded, its value is 1, otherwise it is 0; when the a1 values ​​of the past z5 cycles are all greater than the threshold, the sum is z5, which means that the condition for triggering an alarm is met; Alarm7 i1 Represents the i1th reaching the charging high-end voltage V charge The battery cell A_chargei1 exceeds the threshold value threshold7 for z7 consecutive charging cycles.

[0096] The battery management system collects various data of the battery in the battery cluster in real time, and performs cleaning processing such as deduplication of duplicate values, sorting of disordered values, elimination of abnormal values ​​and filling of missing values ​​on the data. Then, the voltage data is sorted, stored and recorded. According to the characteristics of the charge and discharge strategy and the upper and lower cut-off voltages of charge and discharge, the battery in the battery cluster is automatically selected for voltage difference calculation. Then, the voltage difference is compared with the set threshold to preliminarily record the inconsistent monomers that affect the available capacity of the battery pack. Then, the remaining chargeable power and the remaining dischargeable power are used to further diagnose and locate the inconsistent monomers, so as to achieve real-time positioning while preventing the situation of single detection parameters. At the same time, the detection cost is low and the application range is wide. It is less affected by the environment. Both lithium iron phosphate and ternary lithium batteries are compatible, which is convenient for timely detection and positioning of inconsistent battery monomers in a battery cluster composed of a large number of batteries, and can give priority to positioning monomers with a relatively large degree of comprehensive inconsistency. No additional parameter detection is required, which is convenient and fast, and convenient for timely processing of inconsistent monomer batteries to ensure the life, performance and safety of the battery cluster during use.

[0097] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0098] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for detecting and locating inconsistency abnormal cells in a battery cluster, characterized in that: The following steps are involved: Step 1: During the charging and operation of the energy storage system, the battery management system collects data from the batteries in the battery cluster in real time and performs data preprocessing; Firstly, various data of the batteries in the battery cluster are collected, recorded and stored; Finally, the above data is preprocessed by removing duplicate values, sorting disordered values, removing abnormal values ​​and filling missing values, and the screened valid battery cell voltage data is stored and recorded; Step 2: The first to reach the charging cut-off voltage V upper The battery A1 has a discharge cut-off voltage V lower Perform preliminary inconsistency level diagnosis on battery A2; First, according to the charging cut-off voltage V specified by the battery cluster upper and discharge cut-off voltage V lower , set a fixed charging high-end voltage V charge With the discharge low end voltage V discharge ; Secondly, according to the charging high-end voltage V of the battery cluster charge With the discharge low end voltage V discharge , find the two cells A1 and A2 in the battery cluster that reach the upper cut-off voltage of charging and the lower cut-off voltage of discharging the fastest, and calculate the voltage difference V between these two cells and the median of all batteries at the corresponding time diff ; Finally, fixed thresholds threshold1 and threshold2 are set, and the number of times that batteries A1 and A2 exceed the thresholds in a specified charge and discharge cycle is used to determine whether the inconsistency between the two cells significantly reduces the available capacity of the battery cluster. Step 3: According to the charging high-end voltage V charge With the discharge low end voltage V discharge , calculate the remaining chargeable capacity QR of the monomer with larger inconsistency i1 and the remaining discharge capacity QD i2 ; First, the remaining chargeable capacity and the remaining dischargeable capacity are calculated; Finally, fixed thresholds threshold3 and threshold4 are set to determine whether there are other inconsistency abnormal cells that affect the available capacity of the battery except battery A1 and battery A2; Step 4: Set a time window of length L, based on the pressure difference V diff , and the changes in the remaining chargeable capacity and the remaining dischargeable capacity of the battery cells in this time window, and the changes in the inconsistency degree of these cells are obtained; First, according to the voltage difference V diff , Remaining chargeable capacity QR i1 and the remaining discharge capacity QD i2 The respective linear regression slopes are iteratively calculated using the sliding time window L; Secondly, set fixed thresholds threshold5, threshold6, threshold7 and threshold8 to determine whether there are situations where the thresholds are exceeded continuously, and then determine whether the inconsistency of these monomers is further deepened; Finally, set the number of times a threshold can be exceeded in a specific number of cycles, triggering an alarm when the number is exceeded.

2. The method for detecting and locating inconsistency abnormal cells in a battery cluster according to claim 1, characterized in that: The voltage difference V of battery A1 in step 2 diff The calculation formula is: V diff1 =V charge -V meidian1 Among them, V meidian1 It is the median voltage of all battery cells at the sampling time when charging to the high-end voltage, V diff1 is the voltage difference between the A1 battery and the median voltage; The voltage difference of battery A2 is V diff The calculation formula is: V diff2 =V discharge -V meidian2 Among them, V meidian2 It is the median voltage of all battery cells at the sampling time when they are discharged to the low-end voltage, V diff2 is the voltage difference between the A2 cell and the median voltage.

3. The method for detecting and locating inconsistency abnormal cells in a battery cluster according to claim 1, characterized in that: In step 2, the method for determining whether the number of times that battery A1 and battery A2 exceed the fixed threshold is as follows: Among them, Alarm1 and Alarm2 represent whether battery A1 and battery A2 exceed the threshold value in this charge and discharge cycle respectively. According to the data to be detected and the detection sensitivity requirements, when A1 exceeds the threshold value threshold1 for m consecutive charging cycles, and when A2 exceeds the threshold value threshold2 for n consecutive charging cycles, it is determined that there is a large inconsistency, which significantly affects the available capacity of the battery cluster.

4. The method for detecting and locating inconsistency abnormal cells in a battery cluster according to claim 1, characterized in that: The calculation method of the remaining chargeable capacity and the remaining dischargeable capacity in step 3 is as follows: Where t_charge0 means that A1, the first battery, reaches the high-end charging voltage V charge time, and t_charge i1 Indicates that the i1th battery reaches the high-end charging voltage V charge Time, QR i1 Indicates the remaining chargeable capacity of the i1th battery; similarly, t_discharge0 indicates that A2 is the first to reach the low-end discharge voltage V discharge time, and t_discharge i2 Indicates that the i2th reaches the discharge low end voltage V discharge Time, QD i2 Indicates the remaining dischargeable capacity of the i2th battery.

5. The method for detecting and locating inconsistency abnormal cells in a battery cluster according to claim 1, characterized in that: In step 3, whether there are other inconsistency abnormal cells that affect the available capacity of the battery in addition to battery A1 and battery A2 is determined as follows: Among them, Alarm3 i1 Represents the i1th reaching the charging high-end voltage V charge Battery cell A_charge i1 In a charging cycle, whether the threshold value threshold3 is exceeded, the battery cell A_charge is set according to the data to be detected and the detection sensitivity requirements. i1 When the number of consecutive charging cycles exceeds the threshold value threshold3, it is determined that there is a large inconsistency, which significantly affects the available capacity of the battery cluster; Among them, Alarm4 i2 Represents the i2th high-end discharge voltage V discharge A_discharge of battery cells i2 Whether a discharge cycle exceeds the threshold value threshold4, according to the data to be detected and the detection sensitivity requirement, it is set that when A_dischargei2 exceeds the threshold value threshold4 for q consecutive discharge cycles, it is determined that there is a large inconsistency, which significantly affects the available capacity of the battery cluster.

6. The method for detecting and locating inconsistency abnormal cells in a battery cluster according to claim 1, characterized in that: The linear regression slope in step 4 is calculated as follows: First, define the window and variables: assume that the data sequence is A = [A1, A2, ..., An], there are n cycles in total, the time window length is L, for each starting position j from 1 to n-L+1, calculate the linear regression slope of Aj, Aj+1, ...Aj+L-1 in the window.

7. A method for detecting and locating inconsistency abnormal cells in a battery cluster according to claim 6, characterized in that: The linear regression slope is calculated as follows: Where aj represents the linear regression slope of the jth time window, V diff , QR i1 and QD i2 Substituting into the above formulas respectively, we get the linear regression slope a1 j , a2 j ,a3i1 j and a4i2 j .

8. The method for detecting and locating inconsistency abnormal cells in a battery cluster according to claim 1, characterized in that: The formula for determining whether to further deepen in step 4 is as follows: Otherwise, it is 0; when the a1 values ​​of the past z5 cycles are all greater than the threshold, the sum is z5, which means that the condition for triggering an alarm is met; Alarm7 i1 Represents the i1th reaching the charging high-end voltage V charge The battery cell A_chargei1 exceeds the threshold value threshold7 for z7 consecutive charging cycles.

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