A lithium ion battery screening and analyzing method based on formation curve

Abstract

The application discloses a screening method for lithium ion battery cell abnormality based on formation curve, differentiates the formation curve of the lithium ion battery cell to obtain a differential curve, determines a side reaction platform voltage and a demarcation point voltage based on the differential curve, further determines a peak capacity and a platform capacity, and determines the abnormality of the cell, so that the screening method improves the screening effect of the abnormal cell, strengthens the potential risk identification capability of the cell trial production, improves the production and manufacturing reliability, and reduces the production and product risks. The abnormality of the cell and the trial production is identified at the initial stage of the formation, the risks are identified in advance, the risk cells are prevented from flowing into the next working step, and the cell manufacturing cost is saved.

Description

Technical Field

[0001] This application relates to the field of lithium-ion batteries, specifically to a method for screening and analyzing lithium-ion batteries based on formation curves. Background Technology

[0002] In traditional lithium-ion battery manufacturing, battery screening primarily involves consistency standards for electrothermal performance parameters such as capacity, voltage, internal resistance, self-discharge voltage drop / K value, and temperature, as well as physical parameters such as quality, liquid retention, and dimensions. However, this screening mechanism still misses some potentially risky cells, such as those with poor health or those prone to rapid degradation. In other words, the traditional lithium-ion battery manufacturing screening system has certain loopholes, necessitating the addition of more practical and feasible screening parameters and a more robust screening network to further improve battery manufacturing quality and reliability.

[0003] Lithium-ion battery formation refers to the first charge-discharge process after the cell is packaged and filled with electrolyte. The formation process involves complex physical, chemical, and electrochemical processes, including activation of positive and negative electrode materials, SEI film formation, side reactions of electrolyte and electrode impurities, gas generation from various electro / chemical reactions, electrode material expansion, and particle interface reconstruction. Therefore, formation is crucial to the final performance and health status of lithium-ion batteries. The lithium-ion battery formation curve (usually a capacity-voltage curve or voltage-time curve) records the charge-discharge characteristics of the cell during the formation process. It is a comprehensive reflection of various complex electrochemical processes during formation and also contains complex characteristics of the cell's health status. However, traditional lithium-ion battery manufacturing systems have failed to fully utilize the characteristics of the cell formation curve in cell screening. They rely solely on simple statistical analysis of parameters such as starting voltage, ending voltage, stage voltage, charge-discharge capacity, and time to screen outlier cells based on certain threshold standards.

[0004] Current lithium-ion battery screening methods based on formation curves primarily rely on process data points directly collected from the formation charge-discharge equipment. These data points may include: open-circuit voltage before charging, charging termination voltage, open-circuit voltage, current, step time, capacity, charge level, average voltage, median voltage, and temperature. By statistically analyzing the distribution of one or more of these parameters, a threshold standard for qualified products is established to screen outlier cells.

[0005] Patent CN110496799B discloses a method for identifying abnormal battery cells using formation curves. This method involves plotting an I-MR control chart by detecting the post-filling voltage, plotting the QV second derivative curve in real-time during formation, and detecting the cell voltage, internal resistance, and capacity after formation. These factors are used to determine cell abnormalities. Specifically, when identifying abnormal cells using the QV second derivative curve, the criterion is that the value of a point on the plotted d(dQ / dV) / dV-V curve is greater than or less than 5% of the average value of the d(dQ / dV) / dV-V curve. However, existing technologies fail to fully utilize the characteristic information contained in the formation curves, potentially missing some abnormal and risky cells, leaving product risks and production hazards. Furthermore, the screening parameters used in existing technologies are mainly sampling data at a single time point, which may be subject to sampling bias due to equipment and environmental factors, leading to misjudgments.

[0006] Patent CN114200329A discloses a method for identifying battery abnormalities using the characteristics of the formation curve. During the formation and charging stage, the cell voltage and polarization characteristics, i.e., the voltage difference, are detected and recorded to identify and screen out abnormal cells.

[0007] Patent CN113640683A discloses a method for identifying abnormal batteries during the formation and capacity testing stage. Based on distinguishing battery types, the method uses a neural network to identify abnormal batteries by detecting their characteristic quantities (voltage, current, capacity, voltage change rate, temperature, etc.).

[0008] Patents CN113884908A and CN109164146A respectively disclose methods for evaluating battery water content using the formation dQ / dV-V curve;

[0009] Patent CN112415402B discloses a method for collecting battery charging data and predicting cell abnormalities. Summary of the Invention

[0010] This application provides a lithium-ion battery screening and analysis method based on formation curves. By extracting key characteristic parameters from the formation curves through numerical processing, it identifies potential risks to the health status of lithium-ion batteries and can be used for screening abnormal cells and identifying risks in lithium-ion battery research and development, trial production and mass production.

[0011] Specifically, this application adopts the following technical solution:

[0012] This application discloses a method for screening anomalies in lithium-ion battery cells based on formation curves, including,

[0013] The lithium-ion battery cell was formed, data was collected, and the capacity (Q)-voltage (V) formation curve was obtained;

[0014] The capacitance (Q)-voltage (V) conversion curve is differentiated for the first time to obtain the first differential curve dQ / dV-V;

[0015] Taking the second derivative of the first differential curve dQ / dV-V, we obtain the second differential curve d(dQ / dV) / dV-V.

[0016] The side reaction plateau voltage V is determined based on the first or second differential curve. peak and the dividing point voltage V side ;

[0017] According to voltage V peak and V side Determine peak capacity Q peak Platform capacity Q side According to Q peak and Q side Identify any abnormalities in the battery cells;

[0018] The V peak This refers to the voltage at which the characteristic peak value of dQ / dV first appears in the first differential curve dQ / dV-V.

[0019] The V side This refers to the voltage at which the characteristic valley of dQ / dV first appears in the first differential curve dQ / dV-V.

[0020] The characteristic peaks and valleys refer to the intrinsic voltage changes formed by the battery formation reaction, rather than numerical peaks and valleys caused by numerical fluctuations or noise due to factors such as data acquisition, testing, and data processing.

[0021] The Q peak This refers to the appearance of V peak The capacity corresponding to the time,

[0022] The Q side This refers to the appearance of V side The capacity corresponding to the time.

[0023] In a preferred embodiment, the determination of the side reaction plateau voltage V peak and the dividing point voltage V side This refers to determining V by reading the voltage at the first peak value in the first differential curve dQ / dV-V. peak V is determined by the voltage at the first occurrence of the valley value. side ;or

[0024] The voltage V is determined at the first intersection point (d(dQ / dV) / dV=0) of the second differential curve d(dQ / dV) / dV-V with the X-axis. peak The voltage V is determined by the voltage at the second intersection point with the X-axis (d(dQ / dV) / dV=0).side .

[0025] In a preferred embodiment, the determination of the peak capacity Q of the side reaction platform... peak Platform capacity Q side This refers to directly reading Q by converting the capacitance (Q) to voltage (V) curve. peak and Q side ;or

[0026] Q is obtained by integrating the first differential curve dQ / dV-V. peak and Q side .

[0027] In a preferred embodiment, the Q peak This refers to the dQ / dV-V curve, dQ / dV = 0, and V = V. peak The area enclosed by V and V0;

[0028] The Q side This refers to the dQ / dV-V curve, dQ / dV = 0, and V = V. side The area enclosed by V and V0, where V0 is the starting point of the conversion charging voltage.

[0029] In a preferred embodiment, the step according to Q peak and Q side Determining the abnormal condition of the battery cell refers to Q. peak and Q side The larger the value, the greater the possibility of cell malfunction.

[0030] In a preferred embodiment, the method further includes,

[0031] Confirm Q peak Standard threshold K peak and Q side Standard threshold K side via Q side and Q peak Used as a characteristic parameter for online or offline monitoring to screen for abnormal battery cells.

[0032] In a preferred embodiment, Q side >K side and / or Q peak >K peak This indicates an abnormality in the battery cell and a potential risk.

[0033] In a preferred embodiment, the determination of Q peak Standard threshold K peak and Q side Standard threshold K side This refers to: statistically analyzing the Q of a certain number of known normal battery cells. peak and Qside Get Q peak and Q side Numerical distribution range [Q] peak_min ~Q peak_max ] and [Q side_min ~Q side_max Determine the standard threshold K for a normal battery cell. peak =Q peak_max and K side =Q side_max .

[0034] This application further discloses a system for identifying abnormalities in lithium-ion battery cells based on formation curves, comprising:

[0035] A conversion curve acquisition module is used to acquire the capacity (Q)-voltage (V) conversion curve;

[0036] The calculation module is used to perform the first differentiation on the capacitance (Q)-voltage (V) conversion curve to obtain the first differential curve dQ / dV-V; and to perform the second differentiation on the first differential curve dQ / dV-V to obtain the second differential curve d(dQ / dV) / dV-V.

[0037] The judgment module is used to determine the side reaction plateau voltage V based on the first or second differential curve. peak and the dividing point voltage V side According to voltage V peak and V side Determine peak capacity Q peak Platform capacity Q side According to Q peak and Q side Identify any abnormalities in the battery cells;

[0038] Wherein, the V peak This refers to the voltage at which the characteristic peak value of dQ / dV first appears in the first differential curve dQ / dV-V.

[0039] The V side This refers to the voltage at which the characteristic valley of dQ / dV first appears in the first differential curve dQ / dV-V.

[0040] The characteristic peaks and valleys refer to the intrinsic voltage changes formed by the battery formation reaction, rather than numerical peaks and valleys caused by numerical fluctuations or noise due to factors such as data acquisition, testing, and data processing.

[0041] The Q peak This refers to the appearance of V peak The capacity corresponding to the time,

[0042] The Q side This refers to the appearance of V sideThe capacity corresponding to the time.

[0043] In a preferred embodiment, the system for screening lithium-ion battery cell anomalies based on formation curves performs any of the lithium-ion battery cell anomaly screening methods based on formation curves as described above.

[0044] Invention Effects

[0045] 1. This invention provides a lithium-ion battery screening and analysis method based on formation curves, which can effectively analyze and identify cell abnormal risks through cell formation data, and quickly and non-destructively diagnose and screen abnormal cells.

[0046] 2. Compared to traditional cell screening, this method adds a risk identification parameter at the initial formation stage, improving the screening effect for abnormal cells, strengthening the ability to identify potential risks in cell trial production, enhancing manufacturing reliability, and reducing production and product risks. Identifying cell and trial production anomalies in the early stages of formation allows for early risk assessment, preventing risky cells from entering the next stage and saving on cell manufacturing costs. Attached Figure Description

[0047] The accompanying drawings are provided to better understand this application and do not constitute an undue limitation thereof. Wherein:

[0048] Figure 1 Example diagram of the initial formation curve of a lithium-ion battery;

[0049] Figure 2a and Figure 2b To transform the initial segment of the capacity (Q)-voltage (V) curve into the first derivative curve dQ / dV-V (Figure 2a shows the overall diagram), Figure 2b (This is a magnified view of a portion of the image);

[0050] Figure 3a and Figure 3b To transform the initial segment of the capacity (Q)-voltage (V) curve into the second derivative curve d(dQ / dV) / dV-V (Figure 3a shows the overall diagram), Figure 3b (This is a magnified view of a portion of the image);

[0051] Figure 4 This is the cell anomaly screening method of Example 1;

[0052] Figure 5 This is the cell anomaly screening method of Example 2;

[0053] Figure 6 The area diagram enclosed by the dQ / dV-V curve, dQ / dV = 0, V = Vpeak (or V = Vside), and V = V0. Detailed Implementation

[0054] The following description provides exemplary embodiments of this application, including various details to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0055] This application provides a method for screening anomalies in lithium-ion battery cells based on formation curves, including,

[0056] The lithium-ion battery cell was formed, data was collected, and the capacity (Q)-voltage (V) formation curve was obtained;

[0057] The capacitance (Q)-voltage (V) conversion curve is differentiated for the first time to obtain the first differential curve dQ / dV-V;

[0058] Taking the second derivative of the first differential curve dQ / dV-V, we obtain the second differential curve d(dQ / dV) / dV-V.

[0059] The side reaction plateau voltage V is determined based on the first or second differential curve. peak and the dividing point voltage V side ;

[0060] According to voltage V peak and V side Determine peak capacity Q peak Platform capacity Q side According to Q peak and Q side Identify any abnormalities in the battery cells.

[0061] In this application, "formation" refers to the first charge-discharge process after the lithium-ion battery cell is packaged and filled with electrolyte. The "formation curve" refers to the charge-discharge curve of the lithium-ion battery cell during the formation process, typically a capacity (Q)-voltage (V) formation curve or a voltage (V)-time (t) formation curve. Specifically, it refers to recording parameters such as capacity, voltage, current, time, and temperature of the cell at certain time intervals using a formation charge-discharge device, and taking the capacity (Q)-voltage (V) or voltage (V)-time (t) data as the formation curve used in this method. In one embodiment of this application, the lithium-ion battery cell anomaly screening method based on the formation curve refers to a lithium-ion battery cell anomaly screening method based on the capacity (Q)-voltage (V) formation curve.

[0062] The differential conforms to the general definition in mathematics, that is, given the function B = f(A), we obtain two sets of numbers A and B. In A, when dx approaches itself, the limit of the function at dx is called the differential of the function at dx. The central idea of ​​differential is infinite division. In this application, the function is capacitance (Q) minus voltage (V). After the first differential, we obtain the first differential curve dQ / dV-V. In this application, a second differential can also be performed to obtain the second differential curve d(dQ / dV) / dV-V.

[0063] This application identifies the side reaction plateau in the initial charging stage by observing the side reaction peaks appearing in the differential curve dQ / dV-V during the initial charging stage, as shown in Figures 2a and 2b (Figure 2a is an overall illustration). Figure 2b As shown in the enlarged view, the voltage V corresponding to the peak (peak value) is... peak That is, the corresponding side reaction plateau voltage. In this application, the V... peak This refers to the voltage at which the characteristic peak value of dQ / dV first appears in the first differential curve dQ / dV-V, which is the voltage V corresponding to the first characteristic peak value in the formation curve of normal cell 1. peak1 The voltage V corresponding to the first characteristic peak in the formation curve of abnormal cell 2 peak2 .

[0064] In one embodiment of this application, the side reaction plateau voltage V can also be determined by the second differential curve d(dQ / dV) / dV-V. peak As shown in Figures 3a and 3b (Figure 3a is an overall illustration), Figure 3b As shown in the enlarged illustration, the voltage at the first intersection point (d(dQ / dV) / dV=0) of the second differential curve d(dQ / dV) / dV-V with the X-axis is used to determine the plateau voltage V of the side reaction. peak Specific value.

[0065] In this application, as shown in Figures 2a and 2b (Figure 2a is an overall illustration), Figure 2b As shown in the enlarged view, the voltage V corresponding to the valley point (valley value) is... side That is, the corresponding dividing point voltage. In this application, the V... side This refers to the voltage at which the characteristic valley of dQ / dV first appears in the first differential curve dQ / dV-V, i.e., the voltage V corresponding to the first characteristic valley appearing in the formation curve of normal cell 1. side1 The voltage V corresponding to the first characteristic valley value in the formation curve of abnormal cell 2 side2 .

[0066] The characteristic peaks and valleys refer to the intrinsic voltage changes formed by the battery formation reaction, rather than the numerical peaks and valleys caused by numerical fluctuations or noise due to factors such as data acquisition, testing, and data processing. In one embodiment of this application, the boundary voltage V can also be determined by the second differential curve d(dQ / dV) / dV-V. side As shown in Figures 3a and 3b (Figure 3a is an overall illustration), Figure 3b As shown in the enlarged illustration, determine the voltage V at the second intersection point (d(dQ / dV) / dV=0) of the second differential curve d(dQ / dV) / dV-V with the X-axis. side Specific value.

[0067] Based on the side reaction plateau voltage and the boundary point voltage V peak and V side Determine the peak capacity Q peak Platform capacity Q side In one embodiment of this application, the capacity Q of the side reaction platform is directly read using the QV curve. side (V side (corresponding Q value) and peak capacity Q peak (V peak (corresponding Q value).

[0068] In another embodiment of this application, the side reaction platform capacity Q is obtained by integrating the dQ / dV-V curve. side (dQ / dV-V curve, dQ / dV = 0, V = V) side (and the area enclosed by V = V0) and peak capacity Q peak (dQ / dV-V curve, dQ / dV = 0, V = V) peak The area enclosed by V and V0 is the value of the area bounded by V and V0, where V0 is the starting point of the conversion charging voltage.

[0069] The integral is the inverse operation of differentiation, that is, knowing the derivative of a function, one finds the antiderivative. In this application, the integral conforms to the general concept in mathematics, and its geometric meaning is the area enclosed by the integrand and the coordinate axes, with the portion above the x-axis being positive and the portion below the x-axis being negative.

[0070] In one specific implementation, Q of this application peak This refers to the dQ / dV-V curve, dQ / dV = 0, and V = V. peak The area enclosed by V and V0.

[0071] In one specific implementation, Q of this application side This refers to the dQ / dV-V curve, dQ / dV = 0, and V = V. side The area enclosed by V and V0, where V0 is the starting point of the conversion charging voltage.

[0072] This application is based on the finally determined side reaction platform capacity Q. side and peak capacity Q peak This is used to conduct a qualitative analysis of abnormal conditions in lithium-ion battery cells.

[0073] In one specific analytical approach, this application bases its analysis on the side reaction platform capacity Q. side and peak capacity Q peak The magnitude of the value is used to determine whether there is an abnormality in the battery cell; for example, the platform capacity Q. side and peak capacity Q peak The larger the value, the greater the likelihood of cell abnormalities. The flowchart of the analysis method is as follows: Figure 4 As shown, the abnormal condition of the battery cell is determined, i.e., the platform capacity Q. side and peak capacity Q peak The larger the value, the greater the possibility of abnormalities in the battery cell.

[0074] In another specific analytical approach, this application bases its analysis on the side reaction platform capacity Q. side and peak capacity Q peak The value is compared with relevant numerical values ​​to determine whether there is an abnormality in the battery cell:

[0075] For example, platform capacity Q side and peak capacity Q peak The greater the deviation from the normal experience reference value for battery cells, the greater the possibility of risk to the battery cell.

[0076] For example, setting the platform capacity Q side and peak capacity Q peak Standard threshold K side and K peak via Q side and Q peak Used as a characteristic parameter for online or offline monitoring, screening for abnormal cells, and providing early warning of production risks; specifically, when Q... side >K side If Q is positive, it indicates a cell malfunction and a risk; when Q... peak >K peak If Q is positive, it indicates a cell malfunction and a risk; when Q... side >K side And Q peak >K peak This indicates a cell malfunction and a potential risk. The flowchart of the analysis method is as follows: Figure 5 As shown, the abnormal condition of the battery cell is determined when Q... side >K side If Q is positive, it indicates a cell malfunction and a risk; when Q... peak >K peak If Q is positive, it indicates a cell malfunction and a risk; when Q...side >K side And Q peak >K peak This indicates an abnormality in the battery cell and a potential risk.

[0077] The threshold, also called the critical value, refers to the lowest or highest value at which an effect can occur. In this application, the standard threshold refers to a standard for judging whether a lithium-ion battery cell is normal or not. Its numerical range is related to actual factors such as the battery system and size. The standard threshold of this application can be any standard value in the art that can judge whether the lithium-ion battery cell is normal or not. In a specific embodiment, the determination of Q... peak Standard threshold K peak and Q side Standard threshold K side This refers to: statistically analyzing the Q of a certain number of known normal battery cells. peak and Q side Get Q peak and Q side Numerical distribution range [Q] peak_min ~Q peak_max ] and [Q side_min ~Q side_max Determine the standard threshold K for a normal battery cell. peak =Q peak_max and K side =Q side_max .

[0078] For example, based on a batch of existing normal and abnormal battery cells, the peak capacity Q of each cell can be determined using the method of this application. peak Platform capacity Q side Then, based on modeling normal and abnormal battery cells, the boundary point, i.e., the threshold, between normal and abnormal battery cells is determined. This application does not limit the mathematical modeling method; for example, it can be analogy, bisection, difference, variational method, graph theory, analytic hierarchy process, data fitting, regression analysis, mathematical programming (linear programming, nonlinear programming, integer programming, dynamic programming, goal programming), mechanism analysis, queuing methods, game theory, decision-making methods, fuzzy evaluation methods, time series methods, grey theory methods, modern optimization algorithms (tabu search algorithm, simulated annealing algorithm, genetic algorithm, neural network), etc.

[0079] This application provides a screening method for lithium-ion battery cell anomalies based on formation curves. It can also screen battery cell anomalies based on the voltage (V)-time (t) of the battery cell formation curve. When the formation curve is a voltage (V)-time (t) curve, the screening method is the same as when the formation curve is a capacity (Q)-voltage (V) curve. That is, t is used to replace Q for screening in the same way, and equivalent conversions of various parameters are used, such as Q = I (current) × t (time), etc. This screening method also belongs to the screening method provided in this application.

[0080] This application presents a lithium-ion battery screening and analysis method based on formation curves. This method extracts characteristic parameters from the formation curves of lithium-ion batteries to identify potential risks to their health status. It is used for screening abnormal cells and identifying risks during lithium-ion battery R&D, trial production, and mass production. However, existing technologies primarily use sampling data from a single time point, which may be subject to sampling biases due to equipment and environmental factors, leading to misjudgments. Furthermore, existing lithium-ion battery screening methods based on formation curves mainly rely on process data points directly collected from the formation charging and discharging equipment. These data points may include: open-circuit voltage before charging, charging termination voltage, open-circuit voltage, current, step time, capacity, charge, average voltage, median voltage, and temperature. By statistically distributing one or more of these parameters to establish a qualified product threshold standard and screen outlier cells, the method fails to fully utilize the characteristic information contained in the formation curves. The screening results may miss some abnormal and risky cells, leaving product risks and production hazards unaddressed.

[0081] For example, in the prior art of patent CN110496799B, there is a method for identifying abnormal battery cells through formation curves. This method involves plotting an I-MR control chart by detecting the post-filling voltage, plotting the QV second derivative curve in real time during formation, and detecting the cell voltage, internal resistance, and capacity after formation. The abnormality of the cell is determined by considering these multiple factors. Specifically, when identifying abnormalities through the QV second derivative curve, the criterion is that the value of a point on the plotted d(dQ / dV) / dV-V curve is greater than or less than 5% of the average value of the d(dQ / dV) / dV-V curve. The drawback of this method is that it uses the value of the midpoint of the entire formation curve d(dQ / dV) / dV-V to be greater or less than 5% of the average value of the curve. This may mask the weak characteristics of the curve, especially when the side reaction plateau is relatively weak (the difference between the curve and the average curve due to the difference in the side reaction plateau is <5%) or when the test curve data points have large fluctuations and noise. Furthermore, this method judges outliers based on the degree of deviation of the curve from the average value, which is not the true standard value for normal cells. When the sample data is small, the average value may deviate from the normal range overall, leading to misjudgment. In contrast, the lithium-ion battery screening method based on the formation curve provided in this application directly locates the side reaction plateau in the initial segment of the formation curve and accurately extracts the side reaction characteristic parameter V. side Q side V peak Q peak Furthermore, the threshold standard used for analysis and judgment is a reasonable threshold for normal battery cells, rather than the degree of outlier relative to the average value of the analyzed samples. This method clarifies the characteristic parameters associated with battery cell anomalies, adopts a more reasonable judgment standard, and makes the identification of product risks more accurate.

[0082] Example

[0083] Example 1

[0084] Taking a lithium-ion battery with a lithium iron phosphate cathode and a graphite anode as an example, this paper describes a screening and analysis method for lithium-ion batteries based on formation curves, including the following steps:

[0085] S1: Connect the battery cell to be formed (without charging or discharging) to the formation equipment. At 25°C, allow it to stand for 5 minutes, then charge it at a constant current of 0.05C for 3 hours, then at a constant current of 0.15C for 1 hour, and finally at a constant current of 0.25C for 1 hour, followed by a 5-minute rest. Record formation data (including time, voltage, capacity, current, temperature, etc.) at intervals of ≤30 seconds to obtain the battery cell's formation curve (capacity (Q) - voltage (V)). Figure 1 As shown;

[0086] S2: Using the initial segment capacity (Q)-voltage (V) curve of the formation curve, the initial segment capacity (Q)-voltage (V) curve of the formation curve is differentiated through numerical processing (including curve smoothing, differentiation, etc.) to obtain the differential curve dQ / dV-V, as shown in Figure 2. The side reaction peaks appearing on the differential curve dQ / dV-V in the initial charging segment are used to identify the side reaction plateaus in the initial charging segment. As shown in Figure 2, the voltage V corresponding to the apex of the first characteristic peak appearing in normal cell 1 and abnormal cell 2 are respectively... peak1 and V peak2 These are the corresponding side reaction plateau voltages, and the voltage V corresponding to the valley point (valley value) of the first characteristic valley that appears. side1 and V side2 These are the corresponding side reaction plateau voltages. Through numerical processing (including curve smoothing and differentiation), the first differential curve dQ / dV-V is differentiated to obtain the second differential curve d(dQ / dV) / dV-V, as shown in Figure 3. The intersection of the second differential curve d(dQ / dV) / dV-V with the X-axis (d(dQ / dV) / dV=0) determines the side reaction plateau voltage and the boundary voltage V. peak and V side Specific values. Based on the side reaction plateau voltage and the boundary point voltage V. peak and V side The plateau capacity Q of the side reaction can be directly read from the QV curve. side (V side (corresponding Q value) and peak capacity Q peak (V peak The corresponding Q value; the plateau capacity Q of the side reaction can also be obtained by integrating the dQ / dV-V curve. side (dQ / dV-V curve, dQ / dV = 0, V = V) side (and the area enclosed by V = V0) and peak capacity Q peak (dQ / dV-V curve, dQ / dV = 0, V = V) peak The area enclosed by V and V0), where V0 is the starting point of the conversion charging voltage, such as Figure 6 As shown; in this case, the V of normal cell 1 side1 =2032mV, V peak1 =1924mV, Q side1 =245mAh and peak capacity Q peak1 =168mAh; V of abnormal cell 2 peak2 =2165mV, V side2 =2012mV, Q side2 =820mAh and peak capacity Q peak2 =353mAh.

[0087] S3: Define the platform capacity Qside and peak capacity Q peak Standard threshold K side and K peak via Q side and Q peak Used as a characteristic parameter for online or offline monitoring to screen abnormal cells and provide early warning of production risks; Q side >K side or (and) Q peak >K peak This indicates a cell malfunction and a potential risk. In this case, the platform capacity Q of the normal cell 1 is... side1 (245mAh) and peak capacity Q peak1 (168mAh) are all below the standard threshold, indicating normal battery cells; the platform capacity Q of abnormal battery cell 2 is... side2 (820mAh) and peak capacity Q peak2 (353mAh) is significantly greater than the standard threshold, indicating an abnormal cell and posing a risk.

[0088] S4: Qualitative Analysis: Direct Comparison of Side Reaction Platform Capacity Q side and peak capacity Q peak Platform capacity Q side and peak capacity Q peak The larger the capacity, the greater the potential risk to the battery cell; platform capacity Q side and peak capacity Q peak The greater the deviation from the empirical reference value for normal battery cells, the greater the possibility of risk associated with the cell; in this case, the platform capacity Q of abnormal cell 2... side2 (820mAh) and peak capacity Q peak2 (353mAh) is significantly larger than the platform capacity Q of normal cell 1. side1 (245mAh) and peak capacity Q peak1 (168mAh) is an abnormal battery cell and poses a risk.

[0089] Example 2

[0090] Taking a lithium-ion battery with a lithium iron phosphate cathode and a graphite anode as an example, this paper describes a screening and analysis method for lithium-ion batteries based on formation curves, including the following steps:

[0091] S1-S3 are the same as in Example 1.

[0092] S4: Qualitative Analysis:

[0093] Confirm Q peak Standard threshold K peak and Q side Standard threshold K side By statistically analyzing the Q of a certain number of known normal battery cellspeak and Q side Get Q peak and Q side Numerical distribution range

[0094] [Q peak_min ~Q peak_max ] and [Q side_min ~Q side_max Determine the standard threshold K for a normal battery cell. peak =

[0095] Q peak_max and K side =Q side_max via Q side and Q peak As a feature parameter, it can be monitored online or offline when Q side >K side and / or Q peak >K peak This indicates an abnormality in the battery cell and a potential risk.

[0096] Although the embodiments of this application have been described above in conjunction with the specific embodiments described, this application is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the teachings of this specification and without departing from the scope of protection of the claims of this application, and these are all within the scope of protection of this application.

Claims

1. A method for screening anomalies in lithium-ion battery cells based on formation curves, comprising: The lithium-ion battery cell was formed, data was collected, and the capacity (Q)-voltage (V) formation curve was obtained; The capacitance (Q)-voltage (V) conversion curve is differentiated for the first time to obtain the first differential curve dQ / dV-V; Taking the second derivative of the first differential curve dQ / dV-V, we obtain the second differential curve d(dQ / dV) / dV-V. The side reaction plateau voltage V is determined based on the first or second differential curve. peak and the dividing point voltage V side ; According to voltage V peak and V side Determine peak capacity Q peak Platform capacity Q side According to Q peak and Q side Identify any abnormalities in the battery cells; The V peak This refers to the voltage at which the first peak value of dQ / dV appears in the first differential curve dQ / dV-V. The V side This refers to the voltage at which the first valley of dQ / dV appears in the first differential curve dQ / dV-V. The Q peak This refers to the appearance of V peak The capacity corresponding to the time, The Q side This refers to the appearance of V side The capacity corresponding to the time; The screening method further includes: determining Q peak Standard threshold K peak and Q side Standard threshold K side via Q side and Q peak Used as a characteristic parameter for online or offline monitoring to screen abnormal battery cells; Q side >K side and / or Q peak >K peak This indicates an abnormality in the battery cell and a potential risk. The determination of Q peak Standard threshold K peak and Q side Standard threshold K side This refers to: statistically analyzing the Q of normal battery cells. peak and Q side Get Q peak and Q side Numerical distribution range [Q] peak_min ~Q peak_max ] and [Q side_min ~Q side_max Determine the standard threshold K for a normal battery cell. peak = Q peak_max and K side = Q side_max .

2. The method according to claim 1, characterized in that, The determination of the side reaction platform voltage V peak and the dividing point voltage V side This refers to determining V by reading the voltage at the first peak value in the first differential curve dQ / dV-V. peak V is determined by the voltage at the first occurrence of the valley value. side ;or The voltage V is determined by the first intersection point (d(dQ / dV) / dV=0) of the second differential curve d(dQ / dV) / dV-V with the X-axis. peak The voltage V is determined by the voltage at the second intersection point with the X-axis (d(dQ / dV) / dV=0). side .

3. The method according to claim 1 or 2, characterized in that, The determination of the peak capacity Q of the side reaction platform peak Platform capacity Q side This refers to directly reading Q by converting the capacitance (Q) to voltage (V) curve. peak and Q side .

4. The method according to claim 1, characterized in that, According to Q peak and Q side Determining the abnormal condition of the battery cell refers to Q. peak and Q side The larger the value, the greater the possibility of cell malfunction.

5. A system for detecting anomalies in lithium-ion battery cells based on formation curves, comprising: A conversion curve acquisition module is used to acquire the capacity (Q)-voltage (V) conversion curve; The calculation module is used to perform the first differentiation on the capacitance (Q)-voltage (V) conversion curve to obtain the first differential curve dQ / dV-V; and to perform the second differentiation on the first differential curve dQ / dV-V to obtain the second differential curve d(dQ / dV) / dV-V. The judgment module is used to determine the side reaction plateau voltage V based on the first or second differential curve. peak and the dividing point voltage V side According to voltage V peak and V side Determine peak capacity Q peak Platform capacity Q side According to Q peak and Q side Identify any abnormalities in the battery cells; Wherein, the V peak This refers to the voltage at which the first peak value of dQ / dV appears in the first differential curve dQ / dV-V. The V side This refers to the voltage at which the first valley of dQ / dV appears in the first differential curve dQ / dV-V. The Q peak This refers to the appearance of V peak The capacity corresponding to the time, The Q side This refers to the appearance of V side The capacity corresponding to the time.

6. The system according to claim 5, characterized in that, Perform the method according to any one of claims 1 to 4.

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