Battery cell rapid screening method and system

Abstract

The invention discloses a rapid screening method for battery cells. The method comprises the following steps: 1) constructing an OCV attenuation database under different battery cell SOC and warehouse temperature T; 2) establishing a dynamic compensation model by using the attenuation database; 3) calculating a theoretical attenuation value delta OCVthetical of the battery cell through a dynamic compensation model, and comparing the theoretical attenuation value delta OCVthetical with an actually measured attenuation value delta OCVactual of the battery cell to judge abnormality; the invention further discloses a rapid screening system for the battery cells, the rapid screening system is used for achieving the method, and the rapid screening system comprises a temperature sensor, a voltage sensor, an SOC estimation module, an edge calculation terminal and a database server. According to the invention, the inventory cell detection cost can be significantly reduced, and reliable data support is provided for cell echelon utilization.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery cell detection, and particularly relates to a method and system for rapid screening of battery cells. Background Art

[0002] Before the battery is shipped and used, it is necessary to conduct a self-discharge test. The traditional battery self-discharge detection method has obvious defects:

[0003] Long testing cycle: It needs to be left standing for more than 7 days, which cannot meet the demand for fast shipment;

[0004] Large environmental interference: Warehouse temperature fluctuations make voltage attenuation data unreliable;

[0005] The influence of SOC is ignored: the nonlinear change of self-discharge rate under different SOC is not considered.

[0006] In addition, the existing technology only uses single temperature or SOC detection and lacks multi-variable coupling correction, which cannot ensure the reliability of the detection data. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for rapid screening of battery cells, which can significantly reduce the cost of testing inventory battery cells and provide reliable data support for the cascade utilization of battery cells.

[0008] To achieve the above object, the present invention provides a method for rapid screening of battery cells, comprising the following steps:

[0009] 1) Construct an OCV attenuation database under different battery cell SOC and warehouse temperature T;

[0010] 2) Real-time collection of warehouse temperature T and battery cell SOC data to establish a dynamic compensation model;

[0011] 3) Calculate the theoretical cell attenuation value ΔOCV_theoretical using a dynamic compensation model and compare it with the actual cell attenuation value ΔOCV_actual to determine if there is an abnormality;

[0012] 4) Gradual screening.

[0013] As a further solution of the present invention: the calculation function of the theoretical attenuation value ΔOCV_theoretical is expressed as:

[0014] ΔOCV_theoretical=a·SOC^b·e^(-c / T)·t+d;

[0015] Where a, b, c, and d are material fitting coefficients, SOC is the incoming battery cell state of charge (%, T is the warehouse temperature in °C), t is the battery cell storage time in h, and e is the base of the natural logarithm, which is approximately equal to 2.71828.

[0016] As a further solution of the present invention: abnormality determination condition:

[0017] |ΔOCV_actual-ΔOCV_theoretical|>k·σ;

[0018] Among them, σ is the standard deviation of historical data, and k is the confidence factor.

[0019] Identified as an abnormal battery cell.

[0020] As a further solution of the present invention: the hierarchical screening includes:

[0021] Initial screening: Perform short-term OCV monitoring on the inventory battery cells, and compare the theoretical attenuation value ΔOCV_theoretical with the measured attenuation value ΔOCV_actual to quickly eliminate significantly abnormal battery cells;

[0022] Fine screening: Combine EIS testing with suspicious battery cells to verify whether there are micro-short circuits or SEI defects.

[0023] To achieve the above objectives, the present invention also provides a battery cell rapid screening system for implementing the above battery cell rapid screening method, including a temperature sensor, a voltage sensor, an SOC estimation module, an edge computing terminal and a database server.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] By establishing a database on the relationship between OCV attenuation and battery cell SOC, warehouse temperature, and battery cell storage time, combined with real-time warehouse temperature-battery cell SOC-battery cell storage time data correction, and using theoretical attenuation values ​​and actual attenuation values ​​for comparison and judgment, the efficiency and accuracy of self-discharge detection of inventory battery cells can be improved, the cost of inventory battery cell detection can be reduced, and reliable data support can be provided for the cascade utilization of battery cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flow chart of the rapid screening method for battery cells of the present invention.

[0027] Figure 2 It is a structural schematic diagram of the battery cell rapid screening system of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described below by way of examples.

[0029] like Figure 1 As shown, a method for rapid screening of battery cells comprises the following steps:

[0030] 1) Construct an OCV attenuation database under different battery cell SOC and warehouse temperature T;

[0031] SCO gradient test: select at least five nodes within the 20% to 100% SOC range (e.g., 20%, 40%, 50%, 80%, 100%);

[0032] Temperature gradient test: Set at least three temperature points (such as 25°C, 35°C, and 45°C) within the range of 10°C to 45°C.

[0033] 2) Use IoT terminals to collect real-time warehouse temperature T and battery cell SOC data and establish a dynamic compensation model;

[0034] 3) Calculate the theoretical cell attenuation value ΔOCV_theoretical through the dynamic compensation model and compare it with the actual cell attenuation value ΔOCV_actual to determine abnormalities.

[0035] In order to record the attenuation change of the OCV of the battery cell with time t under each SOC-temperature combination, the calculation function of the theoretical attenuation value ΔOCV_theoretical is further expressed as:

[0036] ΔOCV_theoretical=a·SOC^b·e^(-c / T)·t+d;

[0037] Where a, b, c, and d are material fitting coefficients, SOC is the incoming battery cell state of charge (%, T is the warehouse temperature in °C), t is the battery cell storage time in h, and e is the base of the natural logarithm, which is approximately equal to 2.71828.

[0038] Furthermore, the abnormality determination conditions are:

[0039] |ΔOCV_actual-ΔOCV_theoretical|>k·σ;

[0040] Among them, σ is the standard deviation of historical data, and k is the confidence factor, which is usually 2 to 3.

[0041] Identified as an abnormal battery cell.

[0042] 4) Gradual screening.

[0043] Initial screening (24 hours): Perform short-term OCV monitoring on the inventory battery cells, and compare the theoretical attenuation value ΔOCV_theoretical with the measured attenuation value ΔOCV_actual to quickly eliminate significantly abnormal battery cells (ΔOCV deviation >15%).

[0044] Fine screening (72 hours): Combine EIS testing on suspicious cells to verify whether there are micro-short circuits or SEI defects.

[0045] A battery cell rapid screening system is used to implement the above-mentioned battery cell rapid screening method, including a temperature sensor, a voltage sensor, a SOC estimation module, an edge computing terminal and a database server.

[0046] Temperature sensor: used to detect battery cell storage temperature data;

[0047] Voltage sensor: used to detect battery cell voltage data;

[0048] Database server: stores data such as battery cell incoming SOC, battery cell storage temperature, and battery cell voltage changes over time;

[0049] Edge computing terminal: Uses a dynamic compensation model to calculate the theoretical attenuation value of battery cells, which is used to quickly screen battery cells and eliminate abnormal cells.

[0050] Take NCM811 battery cell as a specific example:

[0051] Database construction:

[0052] Store at SOC=19%, 25%, 40%, 50%, 80%, 100% and 25℃, 35℃, 45℃ respectively, and record the OCV decay database;

[0053] The fitting parameters are: a=0.121, b=1.23, c=1201, d=0.051 (R 2 >0.99), where R2 is an important indicator used in statistics to evaluate the degree of fit of the regression model to the data. 2 >0.99, indicating that the model fits the data very well and explains more than 99% of the variation in the dependent variable;

[0054] Construct the theoretical attenuation value ΔOCV_theoretical with an error of <±3mV.

[0055] Warehouse screening:

[0056] A warehouse with fluctuating ambient temperature (20°C to 30°C) tested 1,000 battery cells.

[0057] After model correction, 13 abnormal cells (ΔOCV deviation > 20%) were screened out within 24 hours;

[0058] EIS verification showed that 10 cells had micro-short circuits and 3 cells had SOC calibration errors.

Claims

1. A method for rapid screening of battery cells, characterized in that: The following steps are involved: 1) Construct an OCV attenuation database under different battery cell SOC and warehouse temperature T; 2) Real-time collection of warehouse temperature T and battery cell SOC data to establish a dynamic compensation model; 3) Calculate the theoretical cell attenuation value ΔOCV_theoretical using a dynamic compensation model and compare it with the actual cell attenuation value ΔOCV_actual to determine if there is an abnormality; 4) Gradual screening.

2. A method for rapid screening of battery cells according to claim 1, characterized in that: The calculation function of the theoretical attenuation value ΔOCV_theoretical is expressed as: ΔOCV_theoretical=a·SOC^b·e^(-c / T)·t+d; Where a, b, c, and d are material fitting coefficients, SOC is the state of charge (%) of the incoming battery cells, T is the warehouse temperature (°C), t is the battery cell storage time (h), and e is the base of the natural logarithm.

3. A method for rapid screening of battery cells according to claim 2, characterized in that: Abnormal judgment conditions: |ΔOCV_actual-ΔOCV_theoretical|>k·σ; Among them, σ is the standard deviation of historical data, k is the confidence factor; Identified as an abnormal battery cell.

4. A method for rapid screening of battery cells according to claim 3, characterized in that: Tiered screening includes: Initial screening: Perform short-term OCV monitoring on the inventory battery cells, and compare the theoretical attenuation value ΔOCV_theoretical with the measured attenuation value ΔOCV_actual to quickly eliminate significantly abnormal battery cells; Fine screening: Combine EIS testing with suspicious battery cells to verify whether there are micro-short circuits or SEI defects.

5. A rapid screening system for battery cells, characterized in that: Used to implement the battery cell rapid screening method described in any one of claims 1-4, including a temperature sensor, a voltage sensor, an SOC estimation module, an edge computing terminal and a database server.

Images