Method for judging abnormal single cell of lithium iron phosphate battery

Abstract

This application relates to a method for identifying abnormal cells in lithium iron phosphate batteries, comprising the following steps: collecting the voltage values ​​of n individual cells after charge and discharge; performing basic statistical calculations, including the mean, standard deviation, median, and quartiles; determining abnormalities using the standard deviation method, the interquartile range method, and the median absolute deviation method, respectively; performing a comprehensive determination: if at least two abnormality determination methods identify an abnormality, the individual cell is confirmed as an abnormal cell; if only one abnormality determination method identifies an abnormality, further analysis is performed; quantifying the degree of abnormality by calculating the abnormality index for each abnormal cell; and classifying the abnormal cells based on their voltage data. This method for identifying abnormal cells in lithium iron phosphate batteries can more accurately screen out cells that affect the overall battery capacity, facilitating targeted maintenance of abnormal cells and thus overcoming the impact of abnormal cells on battery capacity.

Description

Technical Field

[0001] This application relates to the field of energy storage battery testing technology, specifically to a method for judging abnormal cells in lithium iron phosphate batteries. Background Technology

[0002] Currently, the temperature control systems of lithium iron phosphate batteries in energy storage power stations or electric vehicles all use liquid cooling systems. The ambient temperature of the batteries is generally consistent, and the main data affecting the overall battery capacity is the consistency of the battery itself. Battery consistency includes individual cell voltage consistency and individual cell temperature consistency. Individual cell voltage consistency is a key data affecting capacity. The normal end of charging and discharging is marked by the overall voltage or voltage reaching a specified value. If one or more individual cells have abnormal voltages, reaching the end limit prematurely, the overall battery capacity will decrease. During charging, the individual cell voltage continuously increases, and during discharging, the individual cell voltage continuously decreases. In related technologies, battery consistency testing is usually performed using methods such as internal resistance and conductivity values. However, such testing methods cannot predict polarization at the end of the operating condition and have the risk of missed detection, failing to accurately screen out batteries that affect the overall battery capacity. Summary of the Invention

[0003] This application provides a method for identifying abnormal cells in lithium iron phosphate batteries. This method can more accurately screen out cells that affect the overall battery capacity, making it easier to perform targeted maintenance on abnormal cells and thus overcome the impact of abnormal cells on battery capacity.

[0004] The method for determining abnormal cells in lithium iron phosphate batteries provided in this application includes the following steps: S1. Collect the voltage values ​​of n individual cells after the charging and discharging process. The array of voltage values ​​of the n individual cells after the charging and discharging process is: V=[v1,v2,...,v...]. n ]; S2. Perform basic statistical calculations, including mean, standard deviation, median, and quartiles; S3. Anomalies are determined using the standard deviation method, interquartile range method, and median absolute deviation method, respectively. S4. Make a comprehensive judgment. If at least two anomaly judgment methods determine that the cell is abnormal, then the cell is confirmed to be an abnormal cell. If only one anomaly judgment method determines that the cell is abnormal, then further analysis is required. S5. Quantify the degree of abnormality and calculate the abnormality index for each abnormal entity; S6. Classify abnormal cells based on their voltage data.

[0005] In one alternative approach, in step S2, the formula for calculating the average value is: μ=(Σvᵢ) / n; The formula for calculating the standard deviation is: σ = √[Σ(vᵢ-μ)² / (n-1)]; The median is the middle value V_median after sorting the voltage value array; The quartiles include Q1, Q3 and IQR, where Q1 is the 25th percentile, Q3 is the 75th percentile, and IQR = Q3 - Q1.

[0006] In one alternative approach, in step S3, the formula for calculating the standard deviation method is: |vᵢ-μ|>k×σ; Where 2≤k≤3, it is judged as mild abnormality at a 95% confidence interval, with k being 2; and it is judged as significant abnormality at a 99.7% confidence interval, with k being 3.

[0007] In one alternative scheme, in step S3, the determination method of the interquartile range method is as follows: a lower bound and an upper bound are preset, wherein the lower bound is Q1-1.5×IQR, and the upper bound is Q3+1.5×IQR; when vᵢ<lower bound or vᵢ>upper bound, it is determined to be abnormal.

[0008] In one alternative approach, in step S3, the method for determining the median absolute deviation is as follows: Calculate the absolute deviation: ADᵢ=|vᵢ-V_median|; Calculate MAD = median(ADᵢ); The corrected Z-score is: Mᵢ = 0.6745 × (vᵢ - V_median) / MAD; When |Mᵢ|>3.5, it is judged as abnormal.

[0009] In one alternative approach, when using the standard deviation method for anomaly detection, the value of k can be adaptively adjusted based on the battery pack state: if the battery pack is in a quiescent state, a stricter threshold is used, i.e., k=2; if the battery pack is in a charging / discharging state, a slightly looser threshold is used, i.e., k=2.5; if the voltage distribution itself is discrete, the threshold is appropriately relaxed.

[0010] In one alternative approach, in step S5, the anomaly index is calculated as follows: Anomaly index = (|vᵢ - μ| / σ + |vᵢ - V_median| / MAD) / 2.

[0011] In one alternative approach, in step S6, the voltage data for classifying abnormal cells is statistically analyzed once at the end of charging and once at the end of discharging. The abnormal values ​​classified at the end of charging include abnormally high V values. chand abnormally low V cl The abnormal value classification at the end of discharge includes abnormally high V. fh and abnormally low V fl .

[0012] In one alternative approach, when classifying abnormal cells, if a battery matches V... ch A set, but not in V. fh or V fl If the set is found to be too high, the battery level is determined to be too high and needs to be discharged appropriately to achieve consistency; if a battery matches V... cl A set, but not in V. fh or V fl If the set is found, the battery is considered to be within the normal range and requires no attention; if a battery matches V... fh A set, but not in V. ch or V cl If the set is found, the battery is considered to be within the normal range and requires no attention; if a battery matches V... fl A set, but not in V. ch or V cl If the battery level is low, it is determined that the battery needs to be recharged to ensure compatibility.

[0013] In one alternative approach, when classifying abnormal cells, if a battery simultaneously matches V... ch Sets and V fh If the set is found, it is determined that the battery charge is too high and needs to be discharged appropriately to achieve consistency; if a battery is simultaneously matched to V cl Sets and V fl If the set is matched, it is determined that the battery's power is too low and needs to be replenished appropriately to ensure consistency; if a battery is matched with V at the same time... ch Sets and V fl If a set of batteries is matched, it indicates that the battery capacity is too low and replacement should be considered; if a battery is matched with V at the same time... cl Sets and V fh If the battery capacity is too high, it may be a newly replaced battery and requires no further attention.

[0014] The beneficial effects of this application are as follows: The method for identifying abnormal cells in lithium iron phosphate batteries in this application improves detection reliability by analyzing battery data at specified time points, using multiple methods to determine abnormalities, and combining this with the method of selecting abnormal cells. This allows for more accurate screening of batteries that affect the overall battery capacity, facilitating targeted maintenance of abnormal cells and thus overcoming the impact of abnormal cells on battery capacity.

[0015] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Detailed Implementation

[0016] To better understand the technical solution of this application, the embodiments of this application are described in detail below.

[0017] It should be understood that the described embodiments are merely some embodiments of this application, and not all embodiments. All other technical solutions obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0018] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0019] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0020] This application provides a method for identifying abnormal cells in lithium iron phosphate batteries. This method improves detection reliability by identifying "outliers" that significantly deviate from the overall distribution and combining multiple statistical methods to ultimately identify the abnormal cells and the degree of deviation. In this method, all voltage and temperature data for the battery are collected by the battery management system (BMS), and the BMS already possesses universal voltage and temperature protection thresholds.

[0021] Specifically, the method for determining abnormal cells in lithium iron phosphate batteries provided in this application mainly includes the following steps: S1. Collect the voltage values ​​of n individual cells after the charging and discharging process. The array of voltage values ​​of the n individual cells after the charging and discharging process is: V=[v1,v2,...,v...]. n ].

[0022] S2. Perform basic statistical calculations, including mean, standard deviation, median, and quartiles; the formula for calculating the mean is: μ=(Σvᵢ) / n; The formula for calculating the standard deviation is: σ = √[Σ(vᵢ-μ)² / (n-1)]; The median is the middle value V_median after sorting the voltage value array; The quartiles include Q1, Q3, and IQR, where Q1 is the 25th percentile, Q3 is the 75th percentile, and IQR = Q3 - Q1.

[0023] S3. Anomaly detection is performed using the standard deviation method, interquartile range method, and median absolute deviation method, respectively. The standard deviation method is suitable for normal distributions, while the interquartile range method is more robust. Specifically, when using the standard deviation method, the judgment criteria / calculation formula is as follows: |vᵢ-μ|>k×σ; Where 2 ≤ k ≤ 3, a value of k is 2 when the battery is in a 95% confidence interval and 3 when the battery is in a 99.7% confidence interval. Furthermore, the value of k can be adaptively adjusted based on the battery pack's state: a stricter threshold (k=2) is used if the battery pack is in a static state; a slightly more lenient threshold (k=2.5) is used if the battery pack is in a charging / discharging state; and the threshold is appropriately relaxed if the voltage distribution is discrete.

[0024] The interquartile range (IQR) determination method is as follows: a lower bound and an upper bound are preset, where the lower bound is Q1 - 1.5 × IQR and the upper bound is Q3 + 1.5 × IQR; when vᵢ < the lower bound or vᵢ > the upper bound, it is determined to be abnormal.

[0025] The method for determining the absolute deviation of the median is as follows: Calculate the absolute deviation: ADᵢ=|vᵢ-V_median|; Calculate MAD = median(ADᵢ); The corrected Z-score is: Mᵢ = 0.6745 × (vᵢ - V_median) / MAD; When |Mᵢ|>3.5, it is judged as abnormal.

[0026] S4. A voting mechanism is used for comprehensive judgment. If at least two anomaly judgment methods determine that the cell is abnormal, then the cell is confirmed as an abnormal cell. If only one anomaly judgment method determines that the cell is abnormal, then further analysis is performed. This judgment method can greatly improve the accuracy of the judgment results.

[0027] S5. Quantify the degree of abnormality. Calculate the abnormality index for each abnormal entity. The calculation method for the abnormality index is: Abnormality index = (|vᵢ - μ| / σ + |vᵢ - V_median| / MAD) / 2.

[0028] S6. Classify abnormal cells based on their voltage data. Specifically, in this step, statistics are collected when charging or discharging reaches the limit, i.e., when the overall SOC reaches 95% or 100%, or when the overall voltage reaches the set value. This may vary depending on the battery used in the power station or electric vehicle. Statistics are also collected once at the end of charging and once at the end of discharging. The abnormal values ​​collected at the end of charging are classified into categories including abnormally high V values. ch and abnormally low V cl The abnormal value classification at the end of discharge includes abnormally high V. fh and abnormally low V fl The classification results include: normal, high battery level, low battery level, low capacity, and high capacity. Reports are required for high battery level, low battery level, and low capacity.

[0029] Then, a single or double match is performed on the four sets from the above two judgments. A single match includes: if a battery matches V... ch A set, but not in V. fh or V fl If the set is found to be too high, the battery level is determined to be too high and needs to be discharged appropriately to achieve consistency; if a battery matches V... cl A set, but not in V. fh or V fl If the set is found, the battery is considered to be within the normal range and requires no attention; if a battery matches V... fh A set, but not in V. ch or V cl If the set is found, the battery is considered to be within the normal range and requires no attention; if a battery matches V... fl A set, but not in V. ch or V cl If the battery level is low, it is determined that the battery needs to be recharged to ensure compatibility.

[0030] Dual matching includes: if a battery matches V simultaneously ch Sets and V fh If the set is found, it is determined that the battery charge is too high and needs to be discharged appropriately to achieve consistency; if a battery is simultaneously matched to V cl Sets and V fl If the set is matched, it is determined that the battery's power is too low and needs to be replenished appropriately to ensure consistency; if a battery is matched with V at the same time... ch Sets and V fl If a set of batteries is matched, it indicates that the battery capacity is too low and replacement should be considered; if a battery is matched with V at the same time... cl Sets and V fh If the battery capacity is too high, it may be a newly replaced battery and requires no further attention.

[0031] In addition, the steps for performing consistency analysis on the temperature of individual cells can refer to steps S1 to S5 for voltage analysis. The analysis time is periodic analysis during the charging and discharging process (the cycle can be 10 seconds, which can be adjusted according to the actual situation). If the temperature of a certain cell deviates significantly and exceeds a certain threshold (the threshold can be set according to different batteries), an alarm will be triggered to eliminate potential safety hazards. This will not be elaborated on in this article.

[0032] In summary, the method for determining abnormal cells in lithium iron phosphate batteries in this application improves detection reliability by analyzing battery data at specified time points, using multiple methods to determine abnormalities, and combining this with the method of selecting abnormal cells. This allows for more accurate screening of batteries that affect the overall battery capacity, facilitating targeted maintenance of abnormal cells and thus overcoming the impact of abnormal cells on battery capacity.

[0033] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining abnormal cells in a lithium iron phosphate battery, characterized in that, Includes the following steps: S1. Collect the voltage values ​​of n individual cells after the charging and discharging process. The array of voltage values ​​of the n individual cells after the charging and discharging process is: V=[v1,v2,...,v...]. n ]; S2. Perform basic statistical calculations, including mean, standard deviation, median, and quartiles; S3. Anomalies are determined using the standard deviation method, interquartile range method, and median absolute deviation method, respectively. S4. Make a comprehensive judgment. If at least two anomaly judgment methods determine that the cell is abnormal, then the cell is confirmed to be an abnormal cell. If only one anomaly judgment method determines that the cell is abnormal, then further analysis is required. S5. Quantify the degree of abnormality and calculate the abnormality index for each abnormal entity; S6. Classify abnormal cells based on their voltage data.

2. The method for determining abnormal cells in a lithium iron phosphate battery according to claim 1, characterized in that, In step S2, the formula for calculating the average value is: μ=(Σvᵢ) / n; The formula for calculating the standard deviation is: σ = √[Σ(vᵢ-μ)² / (n-1)]; The median is the middle value V_median after sorting the voltage value array; The quartiles include Q1, Q3 and IQR, where Q1 is the 25th percentile, Q3 is the 75th percentile, and IQR = Q3 - Q1.

3. The method for determining abnormal cells in a lithium iron phosphate battery according to claim 2, characterized in that, In step S3, the formula for calculating the standard deviation method is as follows: |vᵢ-μ|>k×σ; Where 2≤k≤3, it is judged as mild abnormality at a 95% confidence interval, with k being 2; and it is judged as significant abnormality at a 99.7% confidence interval, with k being 3.

4. The method for determining abnormal cells in a lithium iron phosphate battery according to claim 2, characterized in that, In step S3, the determination method of the interquartile range method is as follows: a lower bound and an upper bound are preset, wherein the lower bound is Q1-1.5×IQR and the upper bound is Q3+1.5×IQR; when vᵢ<lower bound or vᵢ>upper bound, it is determined to be abnormal.

5. The method for determining abnormal cells in a lithium iron phosphate battery according to claim 2, characterized in that, In step S3, the method for determining the absolute deviation of the median is as follows: Calculate the absolute deviation: ADᵢ=|vᵢ-V_median|; Calculate MAD = median(ADᵢ); The corrected Z-score is: Mᵢ = 0.6745 × (vᵢ - V_median) / MAD; When |Mᵢ|>3.5, it is judged as abnormal.

6. The method for determining abnormal cells in a lithium iron phosphate battery according to claim 3, characterized in that, When using the standard deviation method for anomaly detection, the value of k can be adaptively adjusted according to the battery pack state: if the battery pack is in a static state, a stricter threshold is used, i.e., k=2; if the battery pack is in a charging / discharging state, a slightly looser threshold is used, i.e., k=2.5; if the voltage distribution itself is discrete, the threshold is appropriately relaxed.

7. The method for determining abnormal cells in lithium iron phosphate batteries according to any one of claims 2-6, characterized in that, In step S5, the abnormality index is calculated as follows: Anomaly index = (|vᵢ - μ| / σ + |vᵢ - V_median| / MAD) / 2.

8. The method for determining abnormal cells in lithium iron phosphate batteries according to any one of claims 2-6, characterized in that, In step S6, the voltage data for classifying abnormal cells is statistically analyzed once at the end of charging and once at the end of discharging. The abnormal value classification at the end of charging includes abnormally high V. ch and abnormally low V cl The abnormal value classification at the end of discharge includes abnormally high V. fh and abnormally low V fl .

9. The method for determining abnormal cells in a lithium iron phosphate battery according to claim 8, characterized in that, When classifying abnormal cells, if a certain battery matches V ch A set, but not in V. fh or V fl If the set is found to be too high, the battery level is determined to be too high and needs to be discharged appropriately to achieve consistency; if a battery matches V... cl A set, but not in V. fh or V fl If the set is found, the battery is considered to be within the normal range and requires no attention; if a battery matches V... fh A set, but not in V. ch or V cl If the set is found, the battery is considered to be within the normal range and requires no attention; if a battery matches V... fl A set, but not in V. ch or V cl If the battery level is low, it is determined that the battery needs to be recharged to ensure compatibility.

10. The method for determining abnormal cells in a lithium iron phosphate battery according to claim 8, characterized in that, When classifying abnormal cells, if a certain battery simultaneously matches V ch Sets and V fh If the set is found, it is determined that the battery charge is too high and needs to be discharged appropriately to achieve consistency; if a battery is simultaneously matched to V cl Sets and V fl If the set is matched, it is determined that the battery's power is too low and needs to be replenished appropriately to ensure consistency; if a battery is matched with V at the same time... ch Sets and V fl If a set of batteries is matched, it indicates that the battery capacity is too low and replacement should be considered; if a battery is matched with V at the same time... cl Sets and V fh If the battery capacity is too high, it may be a newly replaced battery and requires no further attention.