CN117805671A - Method and system for detecting micro short circuit of battery in energy storage power station container - Google Patents
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Abstract
The invention relates to the field of detection of energy storage batteries, in particular to a method and a system for detecting micro short circuit of batteries in an energy storage power station container. The method comprises the following steps: collecting and detecting battery cluster data of the container of the energy storage power station in the process of charging and discharging for a plurality of times, preprocessing the battery cluster data, and then cutting according to a module; finding out the cell which charges the fastest in each module; calculating average voltages of the fastest-charging single batteries in each module in two platform stages in the charging process, and recording time differences between other single batteries in each module and the fastest-charging single batteries in the charging process; calculating leakage current in each module by using the charge quantity difference between other single batteries in each module and the single battery which charges fastest and the time difference reaching the average voltage; calculating the resistance values of all the single batteries in each module by utilizing the leakage current; and judging whether the single battery is in micro short circuit or not according to the resistance value and the time difference and a DBSCAN clustering method.
Description
Technical Field
The invention relates to the field of detection of energy storage batteries, in particular to a method and a system for detecting micro short circuit of batteries in an energy storage power station container.
Background
Micro short circuit is one of the important causes of safety accidents of lithium ion batteries. In the use and charge-discharge cycle process of the energy storage battery, the falling and migration of the electrode material can form micro short circuit between the anode and the cathode, thereby causing overheat and burning of the battery.
The existing traditional electrochemical detection methods, such as a constant current charging method and an alternating current impedance method, are simple to operate, but require a long time; the alternating current impedance method has fast response, but requires special equipment, but because the lithium ion battery has two obvious platform periods for self charging, the sensitivity and the efficiency for detecting the micro short circuit by using the method are lower. Recent research begins to develop non-electrochemical rapid detection techniques such as acoustic emission methods, pyroelectric imaging, and the like. These methods are all currently in the theoretical stage and all require specialized equipment support.
Prior art related to the invention:
prior art one
The Chinese patent application with publication number CN116299038A discloses a combined estimation method of SOC internal short circuit based on battery model parameter identification, which utilizes the current transformation process of converting from a pre-charging stage to a heavy current charging stage when an electric automobile is charged, establishes a battery internal short circuit fault equivalent circuit model for a battery monomer with voltage outlier in a small current pre-charging stage according to collected voltage and current information, adopts a least square method with forgetting factors to jointly estimate the battery SOC and the battery internal short circuit resistance value by adopting extended Kalman filtering, thereby judging whether the battery monomer has internal short circuit fault or not, and further realizing early diagnosis of the internal micro short circuit fault of the battery. The invention is suitable for electric vehicles, energy storage systems, battery monomers such as electric tools and the like and grouping application occasions.
This technique relies on specific phases of charging of the electric vehicle (pre-charge phase and high current charge phase), and the applicability of the method is limited if there is no current change in these two typical phases. And only can carry out model establishment and fault judgment on the battery monomer with voltage outlier in the pre-charging stage, and cannot handle the situation that the voltage is normal but internal short circuit exists. The algorithm has higher requirements on voltage and current data precision in the battery charging process, and the battery aging and the environmental temperature change can influence the test result.
Two prior art
The Chinese patent application with publication number of CN114910795A discloses a method and a system for judging micro-short circuit of a battery in constant current charging, a storage medium and a terminal. The invention provides a method and a system for judging micro short circuit of a battery in constant current charging, a storage medium and a terminal, wherein the method comprises the following steps: acquiring voltage data of the battery core without and with the battery micro-short circuit phenomenon in the constant current charging process; performing data cleaning on the voltage data; calculating a voltage difference and a slope under a preset time window based on the cleaned voltage data; adding a label to the cleaned voltage data, wherein the label is free of battery micro-short circuit phenomenon or has battery micro-short circuit phenomenon; training a battery micro-short circuit judgment model based on the voltage difference, the slope and the tag; and judging whether the battery micro-short circuit occurs or not based on the trained battery micro-short circuit judging model. The method and the system for judging the micro-short circuit of the battery in the constant current charging, the storage medium and the terminal can realize the rapid judgment of the micro-short circuit of the battery based on the charging voltage and the machine learning algorithm, and effectively avoid the risk caused by the micro-short circuit of the battery.
The method needs to calculate the voltage difference and the slope under the window as the characteristics, and may miss the characteristics under some micro-short circuit conditions, so as to lead to misjudgment. The addition of the label relies on manual experience, subjectivity is introduced, and micro-short circuit characteristics under different charge and discharge conditions can be different. Using only voltage data, the effect of other factors such as current, soc, etc. is not considered, and thus there may be a limit to the robustness of the result.
Three of the prior art
The Chinese patent application publication No. CN115079007A discloses a lithium ion battery micro-short circuit identification method, an identification system and a battery management system. The application provides a lithium ion battery micro-short circuit identification method, an identification system and a battery management system. The identification method is used for identifying the micro short circuit of the lithium ion battery under the constant current working condition and comprises the following steps: in the charging process of the lithium ion battery, a normal charging curve is obtained, and a micro-short circuit is simulated by an experimental method to obtain a micro-short circuit charging curve; randomly dividing an original data set into a training set and a verification set; building a one-dimensional convolutional neural network training model; training by using a training set, and verifying and obtaining an optimal model with highest accuracy by using a verification set; and identifying the micro short circuit of the lithium ion battery to be evaluated by using the actual charging curve and the optimal model of the lithium ion battery. Carrying out normalization processing and data expansion processing on the charging curve before training; and the data expansion processing superimposes random normal disturbance on the charging curve to generate an additional expansion data curve. The identification system and the battery management system are based on the above identification method.
The lithium ion battery micro-short circuit identification method based on the convolutional neural network. Using the charging curve alone as a model input, the true noise distribution cannot be reflected, possibly resulting in an overfitting. The data is simulation data, is not an actual operation verse, and does not consider the individual difference of the battery and the influence of battery aging, and the generalization capability may not be enough in robustness.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and based on the technology of big data and machine learning, the device for detecting the estimation of the micro-short-circuit resistance and the abnormality identification of the energy storage battery by analyzing the historical data and the electrochemical parameters of the power station meets the online early warning requirement of the power station, and can realize the real-time detection and identification of the abnormal state of the battery monomer of the power station battery by only arranging the device on an online data server or platform of the power station without increasing additional detection cost, thereby increasing the safe operation and maintenance of the power station.
In order to achieve the above purpose, the present invention is realized by the following technical scheme.
The invention provides a method for detecting micro short circuit of a battery in an energy storage power station container, which comprises the following steps:
respectively acquiring and detecting battery cluster data of the container of the energy storage power station in the process of charging and discharging for a plurality of times, preprocessing the battery cluster data, and then cutting according to a module;
finding out the cell which charges the fastest in each module;
calculating average voltages of the fastest-charging single batteries in each module in two platform stages in the charging process, and recording time differences between other single batteries in each module and the fastest-charging single batteries in the charging process;
calculating leakage current in each module by using the charge quantity difference between other single batteries in each module and the single battery which charges fastest and the time difference reaching the average voltage; calculating the resistance values of all the single batteries in each module by utilizing the leakage current;
and judging whether the single battery has micro short circuit or not according to the calculated resistance value and the recorded time difference.
As one of the improvements of the above technical solutions, the collected battery cluster data includes: time, total current, SOC value, and voltage of each unit cell.
As one of the improvements of the above technical solutions, the preprocessing of the battery cluster data includes:
step 1, separating a plurality of files from the collected battery cluster data according to a battery module;
step 2, checking whether the data in the file comprises time, total current, SOC value and voltage of each single battery, if not, discarding the file; if so, entering a step 3;
step 3, resampling each file according to the set time, and taking a certain value; if a value of the sampled file is missing or wrong, forward filling is performed.
As one of the improvements of the above technical solution, the calculating the leakage current in each module using the charge difference between the other cells in each module and the cell that charges the fastest and the time difference to reach the average voltage includes:
step A1: calculating a voltage difference of a set duration based on the fastest charged single battery in a plurality of modules of each battery cluster, and searching two platform stages of the battery according to the voltage difference and the SOC; the judgment standard of the platform period is that the voltage change is extremely small in the platform period time within a certain SOC range;
step A2: calculating average voltage U of each single battery voltage in two platform stages in one charging process R ：
Wherein mean () represents averaging, U P1 Representing the voltage of the first plateau of the first monomer voltage, U P2 The voltage of the second stage of the first single voltage is represented, P1 represents the first stage of the second stage of the first stage of the second stage, S represents the S-th single voltage, s=1, 2.
Step A3: calculating a charge amount difference EQD of the i-th battery and the fastest battery:
EQD＝I*Δt
wherein I is charging current, delta t is the time difference between the ith battery and the fastest battery reaching the limit of charging and discharging voltage, I epsilon { I }, and I represents a set of single batteries except the fastest battery in each module;
step A4: calculating leakage current I of ith cell i ：
Wherein EQD i,n And EQD i,n-1 Is the difference in charge between the ith cell and the reference cell during the nth and (n-1) th charges, T Fastest monomer, n And T Fastest monomer, n-1 Is the time required for the fastest charged cell in the current module to reach the voltage reference line during the nth and (n-1) th charges.
As one of the improvements of the above technical solution, calculating the resistance values of all the unit cells in each module using the leakage current includes:
calculating the average voltage Um of the fastest single battery charged for a plurality of times in each module;
calculating average voltage Um and leakage current I i The resistance Ri of each battery cell is obtained.
As one of the improvements of the above technical solution, in the step A1, searching for two platform phases of the battery according to the voltage difference and the SOC includes: setting an SOC range and window time respectively for the first platform stage and the second platform stage, traversing and calculating a constant value of voltage difference of each window time in the set SOC range, and finding a section with the minimum difference as the first platform stage and the second platform stage; in the traversal calculation process, if the condition that the platform period is continuous occurs, the longest time period is selected as the platform period.
As one of the improvements of the above technical solution, the determining whether the micro-short circuit phenomenon occurs in the single battery according to the calculated resistance value and the recorded time difference includes:
step B1: performing DBSCAN algorithm density clustering on a plurality of batteries of each module, and setting proper clustering parameters eps and min_samples;
step B2: and judging whether the resistance value of each single battery is smaller than a preset threshold value alpha, whether clusters only containing the battery exist in the DBSCAN clustering result, whether the time difference that the battery reaches the lowest voltage Um twice is smaller than a preset threshold value beta, checking whether the single battery starts an equalization strategy, and early warning the battery for micro short circuit or not according to the judging result.
As one of the improvements of the above technical solution, the step B2 specifically includes:
step B2-1: b2-2 is entered if the resistance Ri is less than the preset threshold value alpha, if not, early warning is not carried out, and if yes;
step B2-2: judging whether a cluster only containing the battery per se exists in the DBSCAN clustering result, and if not, not performing early warning; if yes, enter step B2-3;
step B2-3: calculating the time difference of the battery reaching the lowest voltage Um twice, judging whether the time difference is smaller than a preset threshold value beta, and if yes, not performing early warning; if not, go to step B2-4.
Step B2-4: checking whether the battery monomer starts an equalization strategy or not, if so, not performing early warning; if not, the battery is warned of micro short circuit.
As one of the improvements of the above technical solution, before preprocessing the battery cluster data, the method further includes: judging whether the battery cluster data is available or not, specifically: judging whether the charge and discharge times meet the proper times, if the charge of the SOC battery reaches the full charge, namely, the SOC is more than a set percentage, and if the current fluctuation is less than a set current value, the battery cluster data are reserved and then the pretreatment is carried out; if any one of the judging results is negative, discarding the battery cluster data and re-collecting.
The invention also provides a battery micro-short circuit detection system in the energy storage power station container, which is characterized by comprising the following steps:
the data processing module is used for respectively acquiring and detecting battery cluster data of the container of the energy storage power station in the multiple charging and discharging processes, preprocessing the battery cluster data and then cutting the battery cluster data according to the modules; finding out the cell which charges the fastest in each module; calculating average voltages of the fastest-charging single batteries in each module in two platform stages in the charging process, and recording time differences between other single batteries in each module and the fastest-charging single batteries in the charging process; calculating leakage current in each module by using the charge quantity difference between other single batteries in each module and the single battery which charges fastest and the time difference reaching the average voltage; calculating the resistance values of all the single batteries in each module by utilizing the leakage current; and
and the micro short circuit judging module is used for judging whether the single battery generates the micro short circuit phenomenon or not according to the calculated resistance value and the recorded time difference.
Compared with the prior art, the invention has the advantages that:
1. the maintenance cost of equipment is reduced, and the power generation loss is reduced: one of the advantages of the method is that on the premise of not increasing the hardware cost of the equipment, the maintenance cost of the equipment is effectively reduced by detecting the safety problem of the battery in advance; the occurrence of micro short circuit may lead to continuous charge and discharge of the battery, the battery may be damaged in a slight case, and dangerous events such as thermal runaway or explosion may be caused in a serious case; therefore, the safety monitoring of the battery is particularly important, so that the normal operation of the equipment is ensured, and the loss of the generated energy and potential safety hazards are avoided;
2. and (3) monitoring the state of the battery, predicting and maintaining in advance: the method allows the running state of the battery to be monitored in a refined mode, and the situation of the internal resistance of the battery can be predicted by setting different thresholds. Based on the estimated internal resistance value, the most suitable time can be selected for maintenance or battery replacement, and necessary operation is implemented with the lowest cost; the refined prediction and maintenance strategy helps to prolong the service life of the battery to the greatest extent, and reduces the maintenance cost and the risk of production interruption;
3. according to the method, measuring point data are acquired at corresponding sampling frequency through actual operation mass data of the energy storage power station, and analysis and calculation are carried out by combining ohm law, electric quantity and voltage basic principles in circuit knowledge so as to detect possible short circuit conditions in single batteries and estimate the resistance value of the single batteries.
4. The method integrates big data technology and various algorithms, wherein the algorithms comprise linear interpolation, difference, rolling data duty ratio analysis, density clustering and the like; by using the method, the micro-short circuit problem of the battery in the container power station can be identified and maintained, so that measures are taken in advance to ensure the safety of the battery.
Drawings
FIG. 1 is a schematic diagram of a container data preprocessing flow;
FIG. 2 is a schematic diagram of a calculation flow of the internal resistance of a container battery;
FIG. 3 is a schematic diagram of a container battery micro-short circuit judgment flow;
FIG. 4 is a schematic diagram illustrating the platform definition according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is described in detail below with reference to the accompanying drawings and examples.
Example 1
In order to facilitate the understanding of the present technical solution by those skilled in the art, the following describes the method according to the present invention in connection with specific embodiments: in the embodiment, 16 battery clusters are arranged in one container, and 380 batteries are arranged in each battery cluster; every 10 batteries are used as a module, and the total number of the modules is 38. It should be clear to those skilled in the art that the present embodiment is for convenience of understanding the technical solution of the present application, and is not limited to the solution of the present application.
The whole prediction process of the method comprises three parts of data preprocessing, container battery internal resistance calculation and container battery micro-short circuit judgment.
1. Data processing and finding the fastest battery cell to charge according to the module
FIG. 1 is a schematic diagram of a container data preprocessing flow; the data preprocessing part comprises a step of judging whether the data Charge and discharge times meet the proper times, a step of judging whether the SOC (State of Charge) battery is charged fully, namely, whether the SOC data is more than 95 percent, a step of constant current charging and current fluctuation is less than 10A, and if the data are met, the data are usable; after the preliminary requirement of calculation is met, reading data of 3 days, wherein the data fields comprise time, total current, an SOC value, single voltage 1, single voltage 2, and the number of single batteries is equal to the number of single batteries;
the data are divided into different files according to time and battery clusters, one day of data of one battery cluster in the container is one file, one battery cluster comprises a plurality of modules, the algorithm is based on the modules, the module segmentation is carried out on the battery cluster files, and the data preprocessing is continued on the module data:
pretreatment 1 of data:
(1) file no header data (header data fields include time, total current, SOC value, cell voltage 1, cell voltage 2,..once., cell voltage N), discard;
(2) each file is resampled for 5 seconds, and a first value is taken;
(3) NaN (Not a number) appears, NULL, and forward filling is performed.
Data preprocessing 2:
and (3) carrying out linear increment filling on all the single battery voltage data including the single voltage 1, the single voltage 2 and the single voltage N, so as to ensure that the voltage data is linearly incremented in the charging process, and finding out the single battery which charges fastest in each module.
2. Calculation of internal resistance of single battery of power station container
The method has the judgment standard on the platform period that the voltage change is extremely small (diff is minimum) within a certain SOC range within a period of time. As shown in fig. 2, a flow chart of calculation of the internal resistance of the container battery includes the following steps:
step 1: and calculating the voltage difference of three minutes based on the fastest charged single battery in 38 modules of the 1 battery cluster, and searching two platform stages of the battery according to the voltage difference and the SOC.
The lithium battery charging process has the characteristics of two charging platform periods and a middle rapid rising period. Taking a lithium battery with capacity of 280 milliamperes as an example, the voltage difference map can be obtained after differentiating the data of the charging voltage curve, as shown in fig. 4. Two charging plateau phases and an intermediate fast growth phase can be clearly observed from fig. 4. The actual voltage variation range varies due to the different lithium battery characteristics of different models, capacities, attenuation degrees and operating environment temperatures. Therefore, in practical application, voltage change intervals of two charging platform periods and a rapid increase period need to be determined according to parameters of a specific battery, so as to accurately define a battery charging process. The plateau period of this case is defined as follows: (in practice, the SOC ranges and time windows of the two stages can be set according to the actual conditions)
For the first plateau, the SOC range was selected to be 0.3-0.55, with a time window of 20 minutes (240 data points). It is necessary to calculate the constant value of the voltage difference every 20 minutes in the SOC range, and find a segment with the smallest difference as the first plateau.
For the second plateau, the SOC range was greater than 0.7, and the time window was 15 minutes (180 data points). The same traversal finds the segment with the smallest difference as the second plateau.
In the calculation process, if the condition that the platform period is continuous occurs, the longest time period is selected as the platform period.
By the above analysis steps, the start times and durations of the two plateau phases can be obtained.
Step 2: average voltage calculation, U, is performed according to equation 1 R Is the average voltage of the monomer voltage N in two platform phases in one charging process.
Wherein U is P1 Representing the voltage of the first plateau of the first monomer voltage, U P2 Voltage representing the first cell voltage for the second plateau, P1 representing the first plateau, P2 for the second plateau, S representing the S-th cell voltage, s=1, 2. Because one container has multiple cell voltages, assuming 380 cell voltages, there are 380U' s R 。
Step 3: EQD represents the difference in charge between the I-th battery and the fastest battery, I is the charge current (constant current charge, so constant), and Δt is the time difference between the two reaching the charge-discharge voltage limit (Upper Regulation Voltage, UR). That is, assuming t1 is the time at which the fastest battery reaches UR, Δt is the time difference between the time at which the i-th battery reaches UR and t 1.
Eqd=i×Δt (formula 2)
Step 4: calculation of leakage current I by variation of EQD i ，I i Is the leakage current of the ith battery, EQD i,n And EQD i,n-1 Is the difference in charge between the ith cell and the reference cell during the nth and (n-1) th charges, T Fastest monomer, n And T Fastest monomer, n-1 Is the time required for the fastest charged cell in the current module to reach the voltage reference line during the nth and (n-1) th charges.
Step 5: calculating the short-circuit resistance using the calculated average voltage Um of the two charges (i.e. U of the two charges R Average value of (2): u for first charging calculation R And U for the second charge calculation R Average value of (2). ) Dividing the current of formula 3 to obtain a short-circuit resistance value R of each battery cell, recording two times of reaching Um of each battery cell, and calculating 380 time differences for each battery cluster.
3. Early warning judgment of internal resistance of power station container battery
As shown in fig. 3, a flow chart of the micro-short circuit judgment of the container battery is shown, which comprises the following steps:
step 1: performing density clustering on 10 batteries of each module by using a DBSCAN algorithm, setting proper clustering parameters eps=30 and min_samples=2,
step 2: judging for each battery:
(1) If the resistance Ri of the ith battery is smaller than the preset threshold value alpha, entering the next step of judgment; otherwise, the early warning is not carried out.
(2) Judging whether clusters only containing the battery per se exist in the DBSCAN clustering result, and if not, not giving an early warning; if so, the next step is carried out.
(3) Calculating the time difference of the battery reaching the lowest voltage Um twice, and if the time difference is smaller than a preset threshold value beta, not giving an early warning; if the value is larger than beta, the next step of judgment is carried out.
(4) Checking whether the battery monomer starts an equalization strategy or not, and if so, not giving an early warning; if not, the battery is warned of micro-short circuit.
Step 3: and (3) repeating the judgment of the step (2) on each battery in the module to find out all the batteries needing early warning.
Step 4: and outputting an early warning result.
Example 2
The invention discloses a battery micro-short circuit detection system in an energy storage power station container, which comprises the following components:
the data processing module is used for respectively acquiring and detecting battery cluster data of the container of the energy storage power station in the multiple charging and discharging processes, preprocessing the battery cluster data and then cutting the battery cluster data according to the modules; finding out the cell which charges the fastest in each module; calculating average voltages of the fastest-charging single batteries in each module in two platform stages in the charging process, and recording time differences between other single batteries in each module and the fastest-charging single batteries in the charging process; calculating leakage current in each module by using the charge quantity difference between other single batteries in each module and the single battery which charges fastest and the time difference reaching the average voltage; calculating the resistance values of all the single batteries in each module by utilizing the leakage current; and
and the micro short circuit judging module is used for judging whether the single battery generates the micro short circuit phenomenon or not according to the calculated resistance value and the recorded time difference.
The invention is based on actual power station data and applied to an actual energy storage power station, can rapidly and accurately detect whether the micro short circuit exists in the single battery in the power station container, and can quantitatively calculate the resistance value of the micro short circuit. The invention provides an innovative method based on big data technology, which calculates a resistance value by analyzing parameters such as electric quantity difference, voltage trend, leakage current and the like of 380 single batteries in each battery cluster, and judges whether the single batteries have micro short circuit phenomenon or not according to the resistance value. The detection method not only can efficiently identify the micro-short circuit problem, but also can provide comprehensive assessment for the health condition of the battery system. The innovative method provides important support for battery management and maintenance, and protects the driving and aviation for safe and stable operation of the power station container.
In a word, the method not only can reduce equipment maintenance cost and power generation loss, but also can effectively ensure the safety and operation stability of the battery through refined battery state monitoring and advanced prediction. This is an effective method and device for the reliability and safety of the operation of the energy storage container.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and are not limiting. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the appended claims.
Claims (10)
1. A method for detecting a micro short circuit of a battery in a container of an energy storage power station, the method comprising:
respectively acquiring and detecting battery cluster data of the container of the energy storage power station in the process of charging and discharging for a plurality of times, preprocessing the battery cluster data, and then cutting according to a module;
finding out the cell which charges the fastest in each module;
calculating average voltages of the fastest-charging single batteries in each module in two platform stages in the charging process, and recording time differences between other single batteries in each module and the fastest-charging single batteries in the charging process;
calculating leakage current in each module by using the charge quantity difference between other single batteries in each module and the single battery which charges fastest and the time difference reaching the average voltage; calculating the resistance values of all the single batteries in each module by utilizing the leakage current;
and judging whether the single battery has micro short circuit or not according to the calculated resistance value and the recorded time difference.
2. The method for detecting micro-shorting of cells in an energy storage power station container of claim 1, wherein the collected cluster data comprises: time, total current, SOC value, and voltage of each unit cell.
3. The method for detecting micro-shorting of cells in an energy storage power station container according to claim 1, wherein the preprocessing of the cell cluster data comprises:
step 1, separating a plurality of files from the collected battery cluster data according to a battery module;
step 2, checking whether the data in the file comprises time, total current, SOC value and voltage of each single battery, if not, discarding the file; if so, entering a step 3;
step 3, resampling each file according to the set time, and taking a certain value; if a value of the sampled file is missing or wrong, forward filling is performed.
4. The method for detecting micro-short circuit of battery in container of energy storage power station according to claim 1, wherein calculating the leakage current in each module by using the charge difference between other single battery in each module and the single battery charged most rapidly and the time difference reaching the average voltage comprises:
step A1: calculating a voltage difference of a set duration based on the fastest charged single battery in a plurality of modules of each battery cluster, and searching two platform stages of the battery according to the voltage difference and the SOC; the judgment standard of the platform period is that the voltage change is extremely small in the platform period time within a certain SOC range;
step A2: calculating average voltage U of each single battery voltage in two platform stages in one charging process R ：
Wherein mean () represents averaging, U P1 Representing the voltage of the first plateau of the first cell voltage,U P2 the voltage of the second stage of the first single voltage is represented, P1 represents the first stage of the second stage of the first stage of the second stage, S represents the S-th single voltage, s=1, 2.
Step A3: calculating a charge amount difference EQD of the i-th battery and the fastest battery:
EQD＝I*Δt
wherein I is charging current, delta t is the time difference between the ith battery and the fastest battery reaching the limit of charging and discharging voltage, I epsilon { I }, and I represents a set of single batteries except the fastest battery in each module;
step A4: calculating leakage current I of ith cell i ：
Wherein EQD i,n And EQD i,n-1 Is the difference in charge between the ith cell and the reference cell during the nth and (n-1) th charges, T Fastest monomer, n And T Fastest monomer, n-1 Is the time required for the fastest charged cell in the current module to reach the voltage reference line during the nth and (n-1) th charges.
5. The method for detecting micro-short circuit of battery in container of energy storage power station according to claim 4, wherein calculating resistance values of all single battery in each module by using leakage current comprises:
calculating the average voltage Um of the fastest single battery charged for a plurality of times in each module;
calculating average voltage Um and leakage current I i The resistance Ri of each battery cell is obtained.
6. The method for detecting micro-short circuit of battery in container of energy storage power station according to claim 5, wherein in step A1, searching for two platform phases of battery according to voltage difference and SOC comprises: setting an SOC range and window time respectively for the first platform stage and the second platform stage, traversing and calculating a constant value of voltage difference of each window time in the set SOC range, and finding a section with the minimum difference as the first platform stage and the second platform stage; in the traversal calculation process, if the condition that the platform period is continuous occurs, the longest time period is selected as the platform period.
7. The method for detecting micro-short circuit of battery in container of energy storage power station according to claim 4, wherein the step of judging whether micro-short circuit occurs in single battery according to the calculated resistance value and the recorded time difference comprises the following steps:
step B1: performing DBSCAN algorithm density clustering on a plurality of batteries of each module, and setting proper clustering parameters eps and min_samples;
step B2: and judging whether the resistance value of each single battery is smaller than a preset threshold value alpha, whether clusters only containing the battery exist in the DBSCAN clustering result, whether the time difference that the battery reaches the lowest voltage Um twice is smaller than a preset threshold value beta, checking whether the single battery starts an equalization strategy, and early warning the battery for micro short circuit or not according to the judging result.
8. The method for detecting micro-short circuit of battery in container of energy storage power station according to claim 7, wherein the step B2 specifically comprises:
step B2-1: b2-2 is entered if the resistance Ri is less than the preset threshold value alpha, if not, early warning is not carried out, and if yes;
step B2-2: judging whether a cluster only containing the battery per se exists in the DBSCAN clustering result, and if not, not performing early warning; if yes, enter step B2-3;
step B2-3: calculating the time difference of the battery reaching the lowest voltage Um twice, judging whether the time difference is smaller than a preset threshold value beta, and if yes, not performing early warning; if not, entering the step B2-4;
step B2-4: checking whether the battery monomer starts an equalization strategy or not, if so, not performing early warning; if not, the battery is warned of micro short circuit.
9. The method for detecting micro-shorting of cells in an energy storage power station container according to any one of claims 1 to 8, further comprising, prior to preprocessing the cluster data: judging whether the battery cluster data is available or not, specifically: judging whether the charge and discharge times meet the proper times, if the charge of the SOC battery reaches the full charge, namely, the SOC is more than a set percentage, and if the current fluctuation is less than a set current value, the battery cluster data are reserved and then the pretreatment is carried out; if any one of the judging results is negative, discarding the battery cluster data and re-collecting.
10. A battery micro-short detection system in an energy storage power station container, the system comprising:
the data processing module is used for respectively acquiring and detecting battery cluster data of the container of the energy storage power station in the multiple charging and discharging processes, preprocessing the battery cluster data and then cutting the battery cluster data according to the modules; finding out the cell which charges the fastest in each module; calculating average voltages of the fastest-charging single batteries in each module in two platform stages in the charging process, and recording time differences between other single batteries in each module and the fastest-charging single batteries in the charging process; calculating leakage current in each module by using the charge quantity difference between other single batteries in each module and the single battery which charges fastest and the time difference reaching the average voltage; calculating the resistance values of all the single batteries in each module by utilizing the leakage current; and
and the micro short circuit judging module is used for judging whether the single battery generates the micro short circuit phenomenon or not according to the calculated resistance value and the recorded time difference.
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