Self-discharge screening standard determination method and system

Abstract

The invention relates to the technical field of lithium battery manufacturing, in particular to a lithium ion battery self-discharge screening standard judgment method which comprises the following steps: acquiring an initial voltage V1 and an end voltage V2 of the same batch of lithium ion batteries in a standing time period; based on an Arrhenius equation, performing temperature compensation on the original self-discharge rate Kraw, and normalizing the original self-discharge rate Kraw to a standard self-discharge rate Knorm at a standard reference temperature; converting the standard self-discharge speed Knorm into a Z value conforming to standard normal distribution by using a Johnson conversion function; calculating a mean value mu and a standard deviation sigma according to the data set of the Z values, and setting a dynamic judgment threshold value; and comparing the Z value of each battery with a dynamic judgment threshold value, and executing two-stage judgment. According to the method, the temperature compensation model based on the Arrhenius equation is introduced to cooperate with the Johnson distribution conversion function, so that the influence of environment temperature fluctuation is effectively eliminated from a data source.

Description

Technical Field

[0001] This invention belongs to the field of lithium battery technology, specifically relating to a method and system for determining self-discharge screening criteria. Background Technology

[0002] During the production of lithium-ion batteries, factors such as manufacturing processes, material purity, and internal microstructure can lead to excessive self-discharge in some batteries, severely impacting their storage performance, lifespan, and safety. Therefore, self-discharge screening before batteries leave the factory is a crucial step in ensuring battery consistency and safety.

[0003] Currently, the K-value method is widely used in the industry as the primary means of self-discharge screening. This method is based on the rate of voltage change of the battery over a period of time, and the specific formula is as follows:

[0004] K = (V1 - V2) / t

[0005] Where V1 and V2 are the voltages at the beginning and end of the resting period, respectively, and t is the resting time. Typically, a fixed K-value threshold is set (e.g., K-value mean ± 3σ). Batteries exceeding this threshold are considered to have abnormal self-discharge and are discarded.

[0006] However, the traditional K-value screening method has the following significant drawbacks in practical applications:

[0007] The K-value data distribution is not normal: In actual production, K-value data often exhibits a right-skewed distribution, meaning that the vast majority of batteries have small K-values, while a few defective products have extremely large K-values. Under these circumstances, the "mean ± 3σ" threshold setting method based on the normal distribution assumption will lead to threshold deviation, which in turn will cause problems such as missed screening (misplacement of defective products) and incorrect screening (misrejection of good products).

[0008] Temperature sensitivity was not effectively compensated: The self-discharge rate is highly dependent on ambient temperature, following the Arrhenius equation, with the K value approximately doubling for every 10°C increase in temperature. Due to fluctuations in the production line ambient temperature (typically ±2°C), the uncompensated K value cannot accurately reflect the battery's self-discharge characteristics, further exacerbating the uncertainty of the screening results.

[0009] Threshold setting lacks dynamic adaptability: Traditional methods use fixed thresholds, which cannot be adaptively adjusted according to process fluctuations, changes in battery models, or differences in production batches, resulting in rigid screening criteria and a high misjudgment rate.

[0010] To address the aforementioned problems, several improvements have emerged in the existing technology, such as:

[0011] Publication number CN119758092A proposes a secondary screening method by fitting the K2 value through nonlinear regression;

[0012] Publication number CN120381995A employs a combination of the overall differential pressure method and the single-panel standard deviation method for multi-level screening;

[0013] Publication No. CN119064810A analyzes the self-discharge current and its causes through open-circuit voltage-capacity curves.

[0014] However, these methods have not fundamentally solved the problem of insufficient screening accuracy caused by temperature interference and distribution skewness, nor have they formed a complete technical system that combines temperature compensation, distribution correction and dynamic threshold determination. Summary of the Invention

[0015] The purpose of this invention is to provide a method and system for determining self-discharge screening criteria, which can effectively improve the accuracy of self-discharge screening and reduce the false judgment rate without modifying the existing production line hardware.

[0016] To achieve the above objectives, the present invention provides the following technical solution:

[0017] This invention provides a method for determining the self-discharge screening criteria of lithium-ion batteries, comprising the following steps:

[0018] Step S1: Obtain the initial voltage V1 and the final voltage V2 of the same batch of lithium-ion batteries during the resting period, and calculate the voltage according to formula K. raw = (V1-V2) / t Calculate the initial self-discharge rate K raw , where t is the settling time;

[0019] Step S2: Based on the Arrhenius equation, the original self-discharge rate K is... raw Temperature compensation is applied, and the self-discharge rate K is normalized to the standard reference temperature. norm ;

[0020] Step S3: Using the Johnson transformation function, convert the standard self-discharge rate K... norm Convert to Z-values ​​that conform to a standard normal distribution;

[0021] Step S4: Based on the dataset of Z values, calculate its mean μ and standard deviation σ, and set dynamic judgment thresholds, including upper limit threshold Z1 and lower limit threshold Z2;

[0022] Step S5: Compare the Z value of each battery with the dynamic determination threshold, and perform a two-level determination:

[0023] If the Z value is greater than Z1, the battery is determined to have an abnormal self-discharge and is rejected.

[0024] If the Z value ∈ [μ+3σ,Z1], the battery is determined to be suspected of being abnormal, and the retesting process is initiated.

[0025] If the Z value < Z2, it is determined that the battery has abnormal self-discharge and is excluded.

[0026] If the Z value ∈ [Z2, μ + 3σ], it is determined that the battery passes the self-discharge screening.

[0027] Preferably, in the step S2, the specific formula for temperature compensation is:

[0028]

[0029] where E a is the activation energy of the battery material, R is the gas constant, with a value of 0.008314 KJ / mol·K, T ref : the reference temperature, with a value of 298.15 K, and T is the average static temperature measured actually.

[0030] Preferably, the method for obtaining the activation energy E a includes:

[0031] Measure the K value of the same batch of batteries at different temperature gradients, and according to the formula

[0032]

[0033] perform linear fitting, and calculate the E a value through the slope of the fitting line.

[0034] Preferably, the value range of the activation energy E a is 40 KJ / mol to 60 KJ / mol.

[0035] Preferably, in the step S4, the setting method of the dynamic determination threshold is:

[0036] The upper threshold Z1 = μ + A×σ, where A is a constant within the range of 3.0 to 4.0;

[0037] The lower threshold Z2 = μ - B×σ, where B is a constant within the range of 1.5 to 2.5.

[0038] Preferably, the value of A is 3.5 and the value of B is 2.0.

[0039] Preferably, in the step S5, starting the retest process specifically includes step S6:

[0040] Perform secondary static placement on the suspected abnormal battery, and the static placement time t1 is 48 hours to 72 hours;

[0041] Obtain the voltages V3 and V4 during the secondary static placement period, and calculate the retest Z3 value = (V3 - V4) / t1;

[0042] If the Z3 value is greater than μ+3σ upon retesting, the battery is determined to have an abnormal self-discharge and is rejected.

[0043] If the Z3 value is ≤ μ+3σ upon retesting, the battery is deemed to have passed the self-discharge screening.

[0044] Preferably, the parameters of the Johnson transformation function in step S3, and the activation energy E according to claim 2, are... a It can perform online fitting and updating based on the data of the current production batch to achieve process adaptation.

[0045] A lithium-ion battery self-discharge screening system for implementing the method includes:

[0046] The data acquisition module is used to acquire battery voltage data and calculate the original self-discharge rate K. raw ;

[0047] The temperature compensation module is connected to the data acquisition module and is used to receive the K... raw Based on the Arrhenius equation, temperature compensation was applied to obtain the standard self-discharge rate K. norm ;

[0048] The distribution conversion module, connected to the temperature compensation module, is used to receive the K... norm And use the Johnson transformation function to convert it into a Z value that conforms to a standard normal distribution;

[0049] The dynamic judgment module, connected to the distribution transformation module, is used to receive the Z value, calculate its mean μ and standard deviation σ, set a dynamic judgment threshold, execute a two-level judgment logic based on the threshold, and output the judgment result.

[0050] The retest management module is connected to the dynamic judgment module and is used to initiate and manage the retest process for suspected abnormal batteries output by the dynamic judgment module based on the judgment result.

[0051] Preferably, the dynamic determination module is configured to set the upper limit threshold Z1 to μ+3.5σ and the lower limit threshold Z2 to μ-2σ.

[0052] The beneficial effects of this invention are as follows: By introducing the synergistic effect of a temperature compensation model based on the Arrhenius equation and the Johnson distribution transformation function, this invention effectively eliminates the influence of environmental temperature fluctuations at the data source and corrects the right-skewed distribution of the measured K-value data, transforming it into a Z-value that conforms to a standard normal distribution. This fundamentally solves the problem of inaccurate threshold determination caused by temperature sensitivity and distribution distortion in traditional methods, significantly reducing the overall misjudgment rate (the sum of missed and incorrect screenings) of lithium-ion battery self-discharge screening from 15%-25% in traditional methods to below 2%, greatly improving screening accuracy and result reliability.

[0053] Furthermore, this invention creatively employs a dynamic dual-level threshold determination mechanism. By setting differentiated upper and lower thresholds and initiating a retesting process for batteries in the critical range, it achieves accurate capture of slow self-discharge defective batteries while avoiding excessive rejection of good products. Attached Figure Description

[0054] Figure 1 This is a flowchart of the present invention;

[0055] Figure 2 K is the K in this invention raw Self-discharge screening results diagram;

[0056] Figure 3 K is the K in this invention norm Value Johnson Transformation Graph;

[0057] Figure 4 This is the Z-value self-discharge screening diagram in this invention;

[0058] Figure 5 This is the self-discharge screening and retesting diagram in this invention. Detailed Implementation

[0059] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that the following embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0060] Example: A method for determining the self-discharge screening criteria of lithium-ion batteries

[0061] This embodiment uses a batch of lithium iron phosphate (LFP) system lithium-ion batteries as an example to apply the self-discharge screening method of the present invention. Figure 1 The diagram shown is an overall flowchart of the method of the present invention, and the specific implementation steps are as follows:

[0062] S1: Data Acquisition and K-value Calculation

[0063] A large batch (405 batteries in this example) of lithium iron phosphate batteries of the same model were adjusted to the same fixed state of charge (SOC, e.g., 50%) after capacity testing. The batteries were then placed in a room temperature environment (the measured average settling temperature in this example was T = 22°C, i.e., 295.15K) for 72 hours. At the start of the settling period, the open-circuit voltage V1 of each battery was measured and recorded; after the settling period, the open-circuit voltage V2 of each battery was measured and recorded again.

[0064] Calculate the initial self-discharge rate K of each battery using the formula. raw :

[0065] K raw = (V1-V2) / 72(mV / h)

[0066] At this point, 405 K's have been obtained. raw The data points are distributed as follows: Figure 2 As shown, it exhibits a typical right-skewed distribution.

[0067] S2: Temperature compensation, obtaining standard K value.

[0068] To eliminate the influence of temperature fluctuations in the testing environment, the above K... raw The value is normalized to the standard reference temperature (T) based on the Arrhenius equation. ref The standard K value K at 25℃ (i.e., 298.15K) norm .

[0069] The temperature compensation formula is:

[0070] in:

[0071] R is the gas constant, taken as 0.008314 kJ / mol·K.

[0072] E a The activation energy of the battery material needs to be calibrated in advance.

[0073] Activation energy E a The calibration process is as follows:

[0074] Sufficient additional samples were taken from the same batch of batteries and placed in precision constant temperature chambers at 20℃, 25℃, and 30℃ for 8 hours respectively. The average K value of the batteries at each temperature point was calculated. The measured data are shown in the table below:

[0075] Standing temperature T (°C) Standing temperature T (K) <![CDATA[1 / T(K -1 )]]> Average value K (mV / h) Ln(K-means) 20 293.15 0.003411 0.110 -2.207 25 298.15 0.003354 0.180 -1.715 30 303.15 0.003299 0.285 -1.255 .

[0076] A linear regression was performed with 1 / T as the x-axis and Ln(K) as the y-axis to obtain the fitted equation:

[0077] Ln(K)=13.12-(5500 / T)

[0078] The slope of the line is m = -5500. According to formula E... a = -m×R, calculated as follows:

[0079] E a =-(-5500)×0.008314≈45.73KJ / mol

[0080] For ease of application, this embodiment rounds E to the nearest integer. a =46KJ / mol is used for temperature compensation calculations for all subsequent batteries.

[0081] Given Ea = 46 kJ / mol, R = 0.008314 kJ / mol·K, and T... ref Substituting K = 298.15K and T = 295.15K into the temperature compensation formula, we can obtain 405K. raw The values ​​are converted one by one to the corresponding K. norm value.

[0082] S3: Distribution transformation, calculate the standard normal Z-value.

[0083] Because of K norm The value data still maintains a right-skewed distribution (e.g.) Figure 3 As shown, directly using the sigma rule based on the normal distribution is still inaccurate. Therefore, this step utilizes the Johnson transformation function (a statistical tool known in the field) to transform K... norm The value data is transformed into new data that conforms to a standard normal distribution, namely the Z value.

[0084] After transformation, the Z-value dataset exhibits an ideal normal distribution with a mean of 0 and a standard deviation of 1, laying the foundation for subsequent accurate determination based on sigma.

[0085] S4: Set dynamic judgment threshold

[0086] Calculate the mean μ and standard deviation σ of the Z-value dataset. In this example, μ≈0 and σ≈1 are calculated.

[0087] Set dynamic judgment threshold:

[0088] The upper threshold Z1 = μ + 3.5σ = 0 + 3.5 × 1 = 3.5

[0089] Lower threshold Z2=μ-2σ=0-2×1=-2

[0090] S5: Two-level threshold determination

[0091] like Figure 4 As shown, the Z-value of each battery is compared with the above threshold, and a determination is made:

[0092] If the Z value is greater than 3.5 (Z1), the battery is judged to be an abnormal self-discharge battery and should be removed from the production line immediately.

[0093] If the Z value ∈ [3.0 (i.e., μ+3σ), 3.5 (Z1)], it is determined to be a suspected abnormal battery, and the retesting process is initiated (see S106).

[0094] If the Z value is less than -2 (Z2), it is judged as an abnormal self-discharge battery (abnormally low K value, which may indicate other defects) and is immediately rejected.

[0095] If the Z value ∈ [-2(Z2), 3.0 (i.e., μ+3σ)], it is determined to be a qualified battery, and after screening, it flows to the next process.

[0096] S6: Retesting Process

[0097] like Figure 5 As shown, for batteries identified as potentially abnormal in S105, a second resting and retesting process is performed. In this embodiment, the resting time t1 for the retest is set to 48 hours. The retesting procedure is as follows:

[0098] Measure the voltage V3 at the beginning of the retest and the voltage V4 at the end of the retest.

[0099] Calculate the Z3 value for retesting: Z3 value = (V3 - V4) / 48.

[0100] Judgment: If the Z3 value is >3.0 (i.e., μ+3σ), the battery is confirmed to have an abnormal self-discharge and is rejected; if the Z3 value is ≤3.0, the battery is considered to have performed normally in the retest and can be released to the qualified product channel.

[0101] Comparison of Implementation Results

[0102] In this embodiment, the method of the present invention is applied to 405 batteries:

[0103] Using the traditional fixed threshold method (mean K value ± 3σ), a total of 10 defective products were screened out, with a defect rate of 2.4% and a miss rate of 0.98%.

[0104] By adopting the method of this invention, the accuracy is improved from the data source through temperature compensation and distribution conversion. Combined with the two-level dynamic threshold judgment, the defect rate is reduced to 0.4%, the missed screening rate is reduced to 0.2%, and the overall misjudgment rate is far lower than that of traditional methods, which significantly improves the safety and consistency of the products.

[0105] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the self-discharge screening criteria of lithium-ion batteries, characterized in that, It includes the following steps: Step S1: Obtain the initial voltage V1 and the final voltage V2 of the same batch of lithium-ion batteries during the resting period, and calculate the voltage according to formula K. raw = (V1-V2) / t Calculate the initial self-discharge rate K raw , where t is the settling time; Step S2: Based on the Arrhenius equation, the original self-discharge rate K is... raw Temperature compensation is applied, and the self-discharge rate K is normalized to the standard reference temperature. norm ; Step S3: Using the Johnson transformation function, convert the standard self-discharge rate K... norm Convert to Z-values ​​that conform to a standard normal distribution; Step S4: Calculate the mean μ and standard deviation σ of the dataset of the Z values, and set dynamic determination thresholds, including an upper threshold Z1 and a lower threshold Z2; Step S5: Compare the Z value of each battery with the dynamic determination thresholds and perform a two-level determination: If the Z value > Z1, determine that the battery has abnormal self-discharge and reject it; If the Z value ∈ [μ + 3σ, Z1], determine that the battery is suspected of being abnormal and initiate a retest process; If the Z value < Z2, determine that the battery has abnormal self-discharge and reject it; If the Z value ∈ [Z2, μ + 3σ], determine that the battery passes the self-discharge screening.

2. The method according to claim 1, characterized in that, In step S2, the specific formula for temperature compensation is as follows: Among them, E a Let T be the activation energy of the battery material, R be the gas constant with a value of 0.008314 kJ / mol·K, and T be the activation energy of the battery material. ref Reference temperature, taken as 298.15K, and T is the actual measured average static temperature.

3. The method according to claim 2, characterized in that, The activation energy E a The methods for obtaining it include: Measure the K values of the same batch of batteries at different temperature gradients. According to the formula Perform a linear fit and calculate E using the slope of the fitted line. a value.

4. The method according to claim 3, characterized in that, The activation energy E a The value ranges from 40 KJ / mol to 60 KJ / mol.

5. The method according to claim 1, characterized in that, In the said Step S4, the way of setting the dynamic determination thresholds is: The upper threshold Z1 = μ + A×σ, where A is a constant within the range of 3.0 to 4.0; The lower threshold Z2 = μ - B×σ, where B is a constant within the range of 1.5 to 2.

5.

6. The method according to claim 5, characterized in that, The value of A is 3.5, and the value of B is 2.

0.

7. The method according to claim 1, characterized in that, In the said Step S5, the specific steps for initiating the retest process include Step S6: Perform secondary static placement on the batteries suspected of being abnormal. The static placement time t1 is 48 hours to 72 hours; Obtain the voltages V3 and V4 during the secondary static placement period, and calculate the retest Z3 value = (V3 - V4) / t1; If the retest Z3 value > μ + 3σ, determine that the battery has abnormal self-discharge and reject it; If the retest Z3 value ≤ μ + 3σ, determine that the battery passes the self-discharge screening.

8. The method according to claim 1, characterized in that, The parameters of the Johnson transformation function in step S3, and the activation energy E according to claim 2. a It can perform online fitting and updating based on the data of the current production batch to achieve process adaptation.

9. A lithium-ion battery self-discharge screening system, used to implement the method according to any one of claims 1 to 8, characterized in that, It includes: The data acquisition module is used to acquire battery voltage data and calculate the original self-discharge rate K. raw ; The temperature compensation module is connected to the data acquisition module and is used to receive the K... raw Based on the Arrhenius equation, temperature compensation was applied to obtain the standard self-discharge rate K. norm ; The distribution conversion module, connected to the temperature compensation module, is used to receive the K... norm And use the Johnson transformation function to convert it into a Z value that conforms to a standard normal distribution; A dynamic determination module, connected to the distribution conversion module, for receiving the Z value, calculating its mean μ and standard deviation σ, setting dynamic determination thresholds, and performing a two-level determination logic based on the thresholds, and outputting a determination result; A retest management module, connected to the dynamic determination module, for initiating and managing the retest process for the batteries suspected of being abnormal output by the dynamic determination module according to the determination result.

10. The system according to claim 9, characterized in that, The dynamic determination module is configured to: set the upper threshold Z1 as μ + 3.5σ and the lower threshold Zz as μ - 2σ.

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