Battery discharge component boundary offset rate test method, risk identification method and system

Abstract

This invention proposes a method for testing the boundary offset rate of battery discharge components, a risk identification method, and a system, relating to the field of battery component screening technology. The method includes determining multiple sets of test conditions; conducting actual tests on multiple sets of battery discharge components, calculating the measured mean and standard deviation of self-discharge data for each set; selecting multiple standard points from other components with test conditions similar to the target component based on the similarity of test conditions; assigning weights to the standard points, and calculating the theoretical mean and standard deviation of the target component by combining the measured mean and standard deviation of the standard points; calculating the deviation rate of the target component; and identifying the boundary offset rate between risky and normal battery discharge components, using the boundary offset rate as a standard for identifying thermal imbalance in battery discharge components. This invention selects multiple parameter data for multidimensional processing, which can solve the problem of data interference caused by using a single factor to identify discharge components.

Description

Technical Field

[0001] This invention belongs to the field of battery component screening technology, and particularly relates to a method for testing the boundary offset rate of battery discharge components, a risk identification method and system. Background Technology

[0002] Thermal imbalance is an extremely serious problem in the power supply system of electric vehicles, whether for battery components or battery modules. Due to exothermic side reactions, internal heat is not suppressed or released, leading to heat accumulation and ultimately thermal imbalance.

[0003] For pure electric vehicles, thermal runaway is prone to occur under common conditions (static placement, static charging, dynamic driving). If the power battery is not properly managed or is disconnected from monitoring, safety accidents are likely to occur. The above reasons are also the most common factors leading to thermal runaway of the power battery in pure electric vehicles, and most battery fires caused by thermal runaway are due to this reason.

[0004] Batteries undergo testing before leaving the factory, and manufacturers test their thermal runaway performance. The inventors discovered that manufacturers often use voltage drop as a single reference value for judgment, but relying on only a single reference value to determine thermal imbalance can easily lead to misjudgments or omissions. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention provides a battery discharge component boundary offset rate testing method, risk identification method, and system. It changes the traditional prediction method of a single reference value for thermal imbalance, and manages the risk of thermal runaway of lithium-ion batteries in a multidimensional way. It selects multiple parameter data for multidimensional processing, and determines the actual manifestation of the influence of each dimension under the influence of multidimensional data. It determines a relatively accurate boundary offset rate for identifying risky cells and normal cells, and then uses the boundary offset rate to identify risky cells.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0007] The first aspect of the present invention provides a method for testing the boundary offset rate of a battery discharge component.

[0008] The battery discharge component boundary offset rate test method includes the following steps:

[0009] Step 1: Determine multiple sets of test conditions based on the types of multidimensional data;

[0010] Step 2: Based on multiple sets of test conditions, conduct actual tests on multiple sets of battery discharge components, calculate the measured mean and standard deviation of the self-discharge data of each set of battery discharge components, and construct the overall dataset;

[0011] Step 3: Select a set of battery discharge components in the overall dataset as the target component. Based on the similarity of test conditions, select a set number of standard points from other components that are most similar to the test conditions of the target component.

[0012] Step 4: Based on the similarity of the test conditions, assign weights to each standard point, and combine the measured mean and standard deviation of the standard points to calculate the theoretical mean and standard deviation of the target component;

[0013] Step 5: Calculate the deviation rate of the target component based on the measured mean, theoretical mean, and standard deviation of the target component;

[0014] Step Six: Repeat steps three through five to obtain the deviation rate for each group of battery discharge components. Verify the deviation rate and find the boundary offset rate between the risky battery discharge components and the normal battery discharge components.

[0015] Optionally, in step one, the types of multidimensional data are determined, the interval test interval for each data type is defined, and multiple sets of test conditions are obtained by combining the types of multidimensional data and their corresponding interval test intervals, specifically including:

[0016] The diaphragm ventilation, component SOC status, logistics time, and test temperature are identified as multi-dimensional data types.

[0017] Determine the upper and lower limits and the mean value for each data type to obtain the interval range corresponding to each data type;

[0018] By combining the intervals of various data types, multiple sets of test conditions are obtained.

[0019] Optionally, in step three, the distance between the target component and other components in the test conditions is used to calculate the similarity of the test conditions:

[0020] ρi=[(V p -V i )*(V p -V i )+(S p -S i )*(S p -S i )+(W p -W i )*(W p -W i )

[0021] +(T p -T i )*(T p -T i )] 1 / 2

[0022] Where ρi is the similarity of test conditions between the target component and the i-th standard point, i = 1, 2, 3…n; V represents the membrane ventilation degree, S represents the component SOC state, W represents the logistics time, T represents the test temperature; p represents the target component, and i represents the i-th standard point.

[0023] Optionally, in step four, a weight is assigned to each standard point, specifically as follows:

[0024] Qi=(1 / ρi) / (1 / ρ1+1 / ρ2+1 / ρ3+1 / ρ(n-1)+1 / ρn)

[0025] Where Qi is the weight of the i-th standard point.

[0026] Optionally, in step four, the theoretical mean and standard deviation of the target component are calculated by combining the measured mean and standard deviation of the standard points, specifically as follows:

[0027] M p = (M1*Q1+M2*Q2…Mn*Qn)

[0028] S p = (S1*Q1 + S2*Q2…Sn*Qn)

[0029] Among them, M p S is the theoretical average of the target component. p The standard deviation of the target component.

[0030] Optionally, in step five, the deviation rate of the target component is specifically:

[0031] C p =(D p -M p ) / S p

[0032] Among them, C p D represents the deviation rate of the target component. p This is the measured average of the target component.

[0033] Optionally, in step six, the deviation rate is verified by discharging or disassembling the battery discharge assembly, and the boundary offset rate is used as the standard for identifying thermal imbalance of the battery discharge assembly.

[0034] A second aspect of the present invention provides a method for identifying risks in battery discharge components.

[0035] A battery discharge component risk identification method based on the battery discharge component boundary offset rate test method described in the first aspect includes the following steps:

[0036] Identify the battery discharge component to be identified and obtain the deviation rate of the battery discharge component to be identified;

[0037] The deviation rate of the battery discharge component to be identified is compared with the boundary offset rate. When the deviation rate of the battery discharge component to be identified is greater than the boundary offset rate, it is determined that the battery discharge component to be identified has a risk of thermal imbalance.

[0038] Optionally, obtain the deviation rate of the battery discharge component to be identified, specifically:

[0039] Determine one set of test conditions from multiple sets of test conditions as the selected test conditions, and obtain the measured mean and standard deviation of the battery discharge component to be identified corresponding to the selected test conditions;

[0040] Based on the selected test conditions, a set number of standard points that are most similar to the battery discharge component to be identified are selected from the overall dataset, and the theoretical mean and standard deviation of the battery discharge component to be identified are calculated.

[0041] The deviation rate of the battery discharge component to be identified is calculated based on the measured average, theoretical average and standard deviation of the battery discharge component to be identified.

[0042] A third aspect of the present invention provides a battery discharge component risk identification system.

[0043] Battery discharge component risk identification system, including:

[0044] The deviation rate calculation module is configured to: identify the battery discharge component to be identified and obtain the deviation rate of the battery discharge component to be identified;

[0045] The comparison module is configured to compare the deviation rate of the battery discharge component to be identified with the boundary offset rate. When the deviation rate of the battery discharge component to be identified is greater than the boundary offset rate, it is determined that the battery discharge component to be identified has a risk of thermal imbalance.

[0046] The above one or more technical solutions have the following beneficial effects:

[0047] This invention provides a method for testing the boundary offset rate of battery discharge components, a risk identification method, and a system. By comprehensively considering data from four dimensions—diaphragm air permeability, component SOC status, logistics time, and test temperature—it prevents a single factor from having an excessive impact on the overall data, thus avoiding data interference. This solves the problem of data interference caused by using a single factor to identify discharge components in existing technologies.

[0048] This invention selects data from multiple points for fitting based on the similarity of test conditions, and calculates the theoretical value of the self-discharge data of the battery discharge component based on a weighted and multidimensional distributed prediction method, which ensures high robustness of the data, greatly reduces the risk of distortion in the fitting process, and effectively solves the problem of fitting distortion.

[0049] This invention uses the measured average, theoretical average and standard deviation of the target component to calculate the deviation rate of the target component and verifies the deviation rate. It finds the boundary offset rate between the risky battery discharge component and the normal battery discharge component and uses the boundary offset rate as the identification standard for determining the battery discharge component with thermal imbalance caused by internal short circuit, which can ensure high identification accuracy.

[0050] This invention solves the problem of narrow test condition windows and reduces testing costs. Traditional testing processes have high requirements for materials and testing environments. This invention only requires a large window for the standard points in the early stage, and the solution can be used in the later production process. The requirements for materials and testing environments can be reduced, thus reducing testing costs to a certain extent.

[0051] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0052] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0053] Figure 1 This is a flowchart of the method in the first embodiment.

[0054] Figure 2 This is a schematic diagram of the divided interval test zones.

[0055] Figure 3 This is a schematic diagram of multiple test conditions.

[0056] Figure 4 This is a schematic diagram of the measured mean and standard deviation. Detailed Implementation

[0057] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0058] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0059] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0060] Example 1

[0061] This embodiment discloses a method for testing the boundary offset rate of a battery discharge module. Overall, it changes the existing method of predicting thermal imbalance using a single reference value. The inventors found in actual testing that many factors influence the determination of thermal imbalance in a battery discharge module, including: the operation of the material flow line, the consistency of the module's initial state of charge (SOC), and the temperature conditions during the testing process. These factors all have a significant impact on the sampled values. Under the influence of multiple factors, the existing technology, which only considers a single reference value to determine thermal imbalance, is prone to misjudgment or omission.

[0062] To address this technical challenge, this embodiment processes the predicted data using multidimensional data to manage the risk of thermal runaway in lithium-ion batteries in a multidimensional manner. Based on this, separator ventilation, component SOC status, logistics time, and test temperature are identified as multidimensional data types. By calculating the similarity of test conditions, the focus of management is placed on the correlation and selection ratio of multidimensional data. A fitting method is used to theoretically assign values ​​to the battery discharge components, thereby calculating the deviation rate of the battery discharge components. Actual verification is then used to find the boundary offset rate between risky and normal battery discharge components. This boundary offset rate is used as the standard for identifying thermal imbalance in battery discharge components, thus improving the overall accuracy of the thermal imbalance judgment standard for battery discharge components.

[0063] Furthermore, this embodiment first calculates the measured mean and standard deviation of the self-discharge data of the battery discharge component obtained through actual testing. Then, by selecting a set number of standard points similar to the test conditions of the target component, and based on the weight of the standard points, combined with the measured mean and standard deviation of the standard points, the theoretical mean and standard deviation of the target component are calculated through fitting. Next, based on the measured mean, theoretical mean, and standard deviation of the target component, the deviation rate of the target component is calculated. Finally, the deviation rate is verified, and the boundary offset rate between risky battery discharge components and normal battery discharge components is found. The boundary offset rate is used as the standard for identifying thermal imbalance of the battery discharge component. Through the above methods, the actual manifestation of the influence of each dimension under the influence of multi-dimensional data is determined, and the actual influencing factors of the self-discharge of the component under different operating conditions are identified.

[0064] In addition, traditional testing processes have high requirements for materials and testing environment. This embodiment solves the problem of narrow testing condition window in traditional testing processes, reducing testing costs. This embodiment only requires selecting a large window for determining the standard point in the early stage to ensure that a relatively accurate boundary offset rate is found. This solution can be used in the later production process, and the requirements for materials and testing environment can be reduced, thus reducing testing costs to a certain extent.

[0065] In this embodiment, the battery discharge component is a battery cell; the logistics time is the actual time of the battery self-discharge test in the workshop.

[0066] like Figure 1 As shown, the battery discharge component boundary offset rate test method includes the following steps:

[0067] Step 1: Determine multiple sets of test conditions based on the types of multidimensional data;

[0068] The specific process for determining multiple sets of test conditions is as follows: determine the types of multidimensional data, define the interval test interval for each data type, and combine the types of multidimensional data and their corresponding interval test intervals to obtain multiple sets of test conditions.

[0069] More specifically:

[0070] The diaphragm ventilation, component SOC status, logistics time, and test temperature are identified as multi-dimensional data types.

[0071] Determine the upper and lower limits and the mean value for each data type to obtain the interval range corresponding to each data type;

[0072] By combining the intervals of various data types, multiple sets of test conditions are obtained.

[0073] 1A. In this embodiment, the sampling data for the determined battery discharge component are: diaphragm venting / component SOC state / material transport time / test temperature. For ease of description, diaphragm venting / component SOC state / material transport time / test temperature are respectively named V (venting) / S (SOC) / W (wander) / T (temperature).

[0074] 1B. Next, in order to facilitate the division of multiple test conditions, it is necessary to confirm the sampling limit value of the sampled data (based on the actual production process and specification requirements), formulate step points, divide multiple interval test intervals between the limit values, and use multiple interval test intervals to form multiple test conditions.

[0075] For example, such as Figure 2 As shown, the stepwise scheme for diaphragm ventilation is as follows:

[0076] The upper and lower limits of ventilation are designated as V1 and V4, respectively. The interval between the upper and lower limits is then divided into three equal parts, and the values ​​at 1 / 3 and 2 / 3 are taken as V2 and V3.

[0077] Following this example, perform operations on S / W / T respectively to extract S1 / S2 / S3 / S4 W1 / W2 / W3 / W4 T1 / T2 / T3 / T4.

[0078] 1C. After that, set the sampling values ​​in order and arrange 4*4*4*4=256 sets of test conditions.

[0079] Step 2: Based on multiple sets of test conditions, conduct actual tests on multiple sets of battery discharge components, calculate the measured mean and standard deviation of the self-discharge data of each set of battery discharge components, and construct the overall dataset;

[0080] 2A. It should be noted that in this embodiment, each of the 256 test conditions corresponds to one battery discharge assembly. From each of the 256 qualified battery discharge assemblies, 32 single samples are taken. The test groups are then tested according to the planned process conditions of the battery discharge assemblies.

[0081] 2B. Collect data, construct an overall data set, and read the self-discharge data for each test group;

[0082] 2C. Calculate the mean M and standard deviation S of the self-discharge data to obtain the mean M and standard deviation S of 256 standard points.

[0083] In this embodiment, the data in the overall data set is as follows:

[0084] The battery discharge components, corresponding test condition data, and the calculated average M and standard deviation S of the self-discharge data, such as... Figure 4 As shown.

[0085] The battery discharge component boundary offset rate test method proposed in this embodiment comprehensively considers four dimensions of data: diaphragm air permeability, component SOC status, logistics time, and test temperature. This prevents a single factor from having an excessive impact on the overall data, thus avoiding data interference. It can solve the problem of data interference caused by using a single factor to identify discharge components in the prior art.

[0086] Step 3: Select a set of battery discharge components in the overall dataset as the target component. Based on the similarity of test conditions, select a set number of standard points from other components that are most similar to the test conditions of the target component.

[0087] 3A. After obtaining the overall dataset, obtain the membrane ventilation rate, component SOC status, logistics time, and test temperature of the target component, and obtain the V of the component P at the end of the test. p (venting) / Sp (SOC) / W p (wander) / T p (tepperature) and self-discharge D data;

[0088] 3B. Select 5 to 10 target points that are closest to the target component in terms of V / S / W / T;

[0089] In this embodiment, five standard points closest to the target component (based on distance) are selected.

[0090] 3C. Measure the distance ρ between the test component and the nearest target point:

[0091] ρ1=[(V p -V1)*(V p -V1)+(S p -S1)*(S p -S1)+(W p -W1)*(W p -W1)

[0092] +(T p -T1)*(T p -T1)] 1 / 2

[0093] And so on, we get ρ2, ρ3, ρ4, and ρ5.

[0094] Step 4: Based on the similarity of the test conditions, assign weights to each standard point, and combine the measured mean and standard deviation of the standard points to calculate the theoretical mean and standard deviation of the target component;

[0095] This embodiment implements the theoretical assignment of values ​​to the target components, taking 5 target points as an example:

[0096] 4A. Confirm component data weights

[0097] Target point 1:

[0098] Q1=(1 / ρ1) / (1 / ρ1+1 / ρ2+1 / ρ3+1 / ρ4+1 / ρ5)

[0099] Target Point 2:

[0100] Q2=(1 / ρ2) / (1 / ρ1+1 / ρ2+1 / ρ3+1 / ρ4+1 / ρ5)

[0101] By analogy, the weight of each target point is determined.

[0102] 4B. Obtain the theoretical assignment

[0103] Based on the weight distribution, calculate the theoretical M and S values ​​of the target component.

[0104] The M and S values ​​of the tested component P:

[0105] M p = (M1*Q1+M2*Q2…M5*Q5)

[0106] S p = (S1*Q1 + S2*Q2…S5*Q5)

[0107] This embodiment selects data from multiple points for fitting based on the similarity of test conditions. It calculates the theoretical value of the self-discharge data of the battery discharge component based on a weighted and multidimensional distributed prediction method, which ensures high robustness of the data and greatly reduces the risk of distortion in the fitting process, effectively solving the problem of fitting distortion.

[0108] Step 5: Calculate the deviation rate of the target component based on the measured mean, theoretical mean, and standard deviation of the target component;

[0109] The specific calculation method is to calculate the difference between the measured value of the component and its theoretical M value, and then divide it by its theoretical standard deviation to calculate the offset rate.

[0110] Find M p and S q Then, the deviation rate C of the tested component P was measured:

[0111] C p =(D p -M p ) / S p

[0112] Step Six: Repeat steps three through five to obtain the deviation rate for each group of battery discharge components. Verify the deviation rate and find the boundary offset rate between the risky battery discharge components and the normal battery discharge components.

[0113] 6A. Collect the deviation rate C value of each component;

[0114] Repeat the above steps to collect a large amount of target component data and calculate the corresponding offset rate;

[0115] 6B. The deviation rate C value is verified in a stepwise manner, and the verification method is discharge recovery / disassembly of the battery cell;

[0116] Specifically, this involves conducting discharge tests and disassembling battery cells with different offset rates.

[0117] 6C. Based on the verification results, find the offset rate of the boundary between the risky battery cell and the normal battery cell, and use it as the offset rate of the boundary between the risky battery discharge assembly and the normal battery discharge assembly. Use this offset rate as the standard for judging the risk of thermal imbalance.

[0118] By using the measured average, theoretical average and standard deviation of the target components, the deviation rate of the target components is calculated and verified. The boundary offset rate between risky battery discharge components and normal battery discharge components is found. The boundary offset rate is used as the identification standard for battery discharge components with thermal imbalance caused by internal short circuit, which can ensure high identification accuracy.

[0119] Example 2

[0120] This embodiment discloses a method for identifying risks in battery discharge components.

[0121] A method for identifying risks associated with battery discharge components includes the following steps:

[0122] Identify the battery discharge component to be identified and obtain the deviation rate of the battery discharge component to be identified;

[0123] The deviation rate of the battery discharge component to be identified is compared with the boundary offset rate. When the deviation rate of the battery discharge component to be identified is greater than the boundary offset rate, it is determined that the battery discharge component to be identified has a risk of thermal imbalance.

[0124] Furthermore, the deviation rate of the battery discharge component to be identified is obtained, specifically:

[0125] Determine one set of test conditions from multiple sets of test conditions as the selected test conditions, and obtain the measured mean and standard deviation of the battery discharge component to be identified corresponding to the selected test conditions;

[0126] Based on the selected test conditions, a set number of standard points that are most similar to the battery discharge component to be identified are selected from the overall dataset, and the theoretical mean and standard deviation of the battery discharge component to be identified are calculated.

[0127] The deviation rate of the battery discharge component to be identified is calculated based on the measured average, theoretical average and standard deviation of the battery discharge component to be identified.

[0128] Since the overall dataset has been pre-built in Example 1, in this example, it is only necessary to select a set of test conditions to actually test the battery discharge component to be identified, calculate the similarity between multiple sets of test conditions in the overall dataset and the selected test conditions, and select multiple standard points corresponding to the battery discharge component to be identified. Then, following the steps in Example 1, the deviation rate of the battery discharge component to be identified can be calculated.

[0129] After obtaining the deviation rate of the battery discharge component to be identified, it is only necessary to compare it with the boundary offset rate determined in Example 1 to determine whether the current battery discharge component to be identified has a risk of thermal imbalance.

[0130] Therefore, in the later stages of risk identification for battery discharge components, the overall testing cost was reduced, making it easier to efficiently identify risks in battery discharge components.

[0131] Example 3

[0132] This embodiment provides a battery discharge component risk identification system.

[0133] Battery discharge component risk identification system, including:

[0134] The deviation rate calculation module is configured to: identify the battery discharge component to be identified and obtain the deviation rate of the battery discharge component to be identified;

[0135] The comparison module is configured to compare the deviation rate of the battery discharge component to be identified with the boundary offset rate. When the deviation rate of the battery discharge component to be identified is greater than the boundary offset rate, it is determined that the battery discharge component to be identified has a risk of thermal imbalance.

[0136] The steps and methods involved in the apparatus of Embodiment 3 above correspond to those in Embodiment 2. For detailed implementation methods, please refer to the relevant description section of Embodiment 2. Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.

[0137] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for testing the boundary offset rate of a battery discharge component, characterized in that, Includes the following steps: Step 1: Determine multiple sets of test conditions based on the types of multidimensional data; Step 2: Based on multiple sets of test conditions, conduct actual tests on multiple sets of battery discharge components, calculate the measured mean and standard deviation of the self-discharge data of each set of battery discharge components, and construct the overall dataset; Step 3: Select a set of battery discharge components in the overall dataset as the target component. Based on the similarity of test conditions, select a set number of standard points from other components that are most similar to the test conditions of the target component. Step 4: Based on the similarity of the test conditions, assign weights to each standard point, and combine the measured mean and standard deviation of the standard points to calculate the theoretical mean and standard deviation of the target component; Step 5: Calculate the deviation rate of the target component based on the measured mean, theoretical mean, and standard deviation of the target component; Step 6: Repeat steps 3 to 5 to obtain the deviation rate corresponding to each group of battery discharge components, verify the deviation rate, and find the boundary offset rate between risky battery discharge components and normal battery discharge components. In step three, the distance between the target component and other components in the test conditions is used to calculate the similarity of the test conditions: ρi=[(V p -V i )*(V p -V i )+(S p -S i )*(S p -S i )+(W p -W i )*(W p -W i ) +(T p -T i )*(T p -T i )] 1 / 2 Where ρi is the similarity of test conditions between the target component and the i-th standard point, i=1,2,3…n; V represents the membrane ventilation degree, S represents the component SOC state, W represents the logistics time, T represents the test temperature; p represents the target component, and i represents the i-th standard point. In step four, a weight is assigned to each standard point, specifically as follows: Qi=(1 / ρi) / (1 / ρ1+1 / ρ2+1 / ρ3+1 / ρ(n-1)+1 / ρn) Where Qi is the weight of the i-th standard point; In step four, the theoretical mean and standard deviation of the target component are calculated by combining the measured mean and standard deviation of the standard points, specifically as follows: M p =(M1*Q1+M2*Q2…Mn*Qn) S p =(S1*Q1+S2*Q2…Sn*Qn) Among them, M p S is the theoretical average of the target component. p The standard deviation of the target component; In step five, the deviation rate of the target component is specifically as follows: C p =(D p - M p ) / S p Among them, C p D represents the deviation rate of the target component. p This is the measured average of the target component.

2. The battery discharge component boundary offset rate test method as described in claim 1, characterized in that, In step one, the types of multidimensional data are determined, and the interval test range for each data type is defined. Combining the types of multidimensional data and their corresponding interval test ranges, multiple sets of test conditions are obtained, specifically including: The diaphragm ventilation, component SOC status, logistics time, and test temperature are identified as multi-dimensional data types. Determine the upper and lower limits and the mean value for each data type to obtain the interval corresponding to each data type; By combining the intervals of various data types, multiple sets of test conditions are obtained.

3. The battery discharge component boundary offset rate test method as described in claim 1, characterized in that, In step six, the deviation rate is verified by discharging or disassembling the battery discharge component, and the boundary offset rate is used as the standard for identifying thermal imbalance of the battery discharge component.

4. A method for identifying the risk of a battery discharge component based on the battery discharge component boundary offset rate test method according to any one of claims 1-3, characterized in that, Includes the following steps: Identify the battery discharge component to be identified and obtain the deviation rate of the battery discharge component to be identified; The deviation rate of the battery discharge component to be identified is compared with the boundary offset rate. When the deviation rate of the battery discharge component to be identified is greater than the boundary offset rate, it is determined that the battery discharge component to be identified has a risk of thermal imbalance.

5. The battery discharge component risk identification method as described in claim 4, characterized in that, Obtain the deviation rate of the battery discharge component to be identified, specifically: Determine one set of test conditions from multiple sets of test conditions as the selected test conditions, and obtain the measured mean and standard deviation of the battery discharge component to be identified corresponding to the selected test conditions; Based on the selected test conditions, a set number of standard points that are most similar to the battery discharge component to be identified are selected from the overall dataset, and the theoretical mean and standard deviation of the battery discharge component to be identified are calculated. The deviation rate of the battery discharge component to be identified is calculated based on the measured average, theoretical average and standard deviation of the battery discharge component to be identified.

6. A battery discharge component risk identification system based on the battery discharge component boundary offset rate test method according to any one of claims 1-3, characterized in that, include: The deviation rate calculation module is configured to: identify the battery discharge component to be identified and obtain the deviation rate of the battery discharge component to be identified; The comparison module is configured to compare the deviation rate of the battery discharge component to be identified with the boundary offset rate. When the deviation rate of the battery discharge component to be identified is greater than the boundary offset rate, it is determined that the battery discharge component to be identified has a risk of thermal imbalance.

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