Method for detecting internal short circuit of power battery, electronic device and storage medium

Abstract

The application discloses a power battery internal short circuit detection method, an electronic device and a storage medium, and comprises the following steps: obtaining first data by intercepting at least one piece of data from battery multiple charging cycle data; obtaining estimated data and actual data at different time according to the first data; correcting the estimated data and the actual data to obtain second data at different time; grouping the second data obtained at different time; jointly calculating the second data in different groups to obtain a charging deviation coefficient; and judging the internal short circuit condition of the power battery according to the charging deviation coefficient. The application detects and judges the internal short circuit condition of the battery by obtaining the trend of the charging deviation coefficient from the charging cycle data of the battery. Moreover, the method disclosed by the application requires less data, the test process takes a short period of time, and the labor cost and material cost are lower.

Description

Technical Field

[0001] The embodiments of this application relate to the field of battery testing technology, and in particular to a method for detecting internal short circuits in a power battery, an electronic device, and a storage medium. Background Technology

[0002] With the popularization and development of new energy electric vehicles, high-energy-density electrochemical systems are gradually being applied to meet market demands for driving range, and corresponding safety issues are receiving more attention. Among the more serious safety issues, the thermal runaway of power batteries is of utmost concern. Direct contact between the positive and negative electrodes within the power battery, leading to an internal short circuit, is an inevitable step in the occurrence of thermal runaway.

[0003] Internal short circuits in batteries can be categorized into four types: positive-negative, positive-copper, aluminum-negative, and aluminum-negative. It is generally believed that positive-negative and positive-copper internal short circuits evolve gradually from slight self-discharge, eventually leading to thermal runaway. In contrast, positive-aluminum-negative and aluminum-copper internal short circuits rapidly progress to thermal runaway after their occurrence. Therefore, identifying early-stage internal short circuits is the most effective way to prevent thermal runaway.

[0004] Existing technologies for detecting and warning of thermal runaway mainly fall into two categories: one is based on electrochemical models, but this method requires testing to obtain model parameters, involves numerous tests, and has a long cycle. Parameters need to be identified separately for different battery models and systems, resulting in high costs and a lengthy process. The other category is data-driven methods, which typically build a model of the relationship between characteristic factors in cloud data and thermal runaway. However, this method uses internal short-circuit warnings, usually identifying anomalies in heat generation, voltage, and current, which requires high-quality data. Summary of the Invention

[0005] The embodiments of this application provide a method for detecting internal short circuits in a power battery, an electronic device, and a storage medium to solve the technical problems of high cost, long testing cycle, and high data requirements in existing internal short circuit testing methods.

[0006] To address the aforementioned technical problems, embodiments of this application disclose the following technical solutions:

[0007] Firstly, a method for detecting internal short circuits in a power battery is provided, including:

[0008] The data acquisition module extracts at least one segment of data from the battery's multiple charge cycle data to obtain the first data.

[0009] The first calculation module obtains estimated data and actual data at different times from the first data;

[0010] The estimated data and the actual data are corrected by the data correction module to obtain second data at different times;

[0011] The second data obtained at different times is grouped using the data grouping module;

[0012] The charging deviation coefficient is obtained by jointly calculating the second data from different groups through the second calculation module.

[0013] The data analysis module determines the internal short circuit status of the power battery based on the charging deviation coefficient.

[0014] In conjunction with the first aspect, the method for grouping the second data obtained at different times using the data grouping module includes:

[0015] The second data at different times is compared with a pre-set threshold input into the data grouping module;

[0016] If the second data is greater than the threshold, then the second data corresponding to that moment is assigned to the first group;

[0017] If the second data is less than the threshold, then the second data corresponding to that moment is assigned to the second group.

[0018] In conjunction with the first aspect, the method for obtaining the first data by extracting at least one segment of data from multiple battery charging cycle data through a data acquisition module includes:

[0019] The data acquisition module acquires historical data of multiple charging cycles of the battery;

[0020] The data acquisition module extracts the segment of each charging cycle data that is between 20% and 80% of the total value as the first data.

[0021] In conjunction with the first aspect, the method for obtaining estimated data and actual data at different times from the first data through the first calculation module includes:

[0022] The first data is divided into multiple intervals to obtain interval data;

[0023] The estimation reference line is obtained by plotting the endpoint values ​​from the interval data.

[0024] The first calculation module obtains the estimated data for the corresponding time based on the estimation reference line;

[0025] The first calculation module obtains the actual data at the corresponding time based on the first data.

[0026] In conjunction with the first aspect, the method for obtaining the charging deviation coefficient by jointly calculating the second data from different groups using the second calculation module includes:

[0027] The second calculation module obtains the first time difference between the second data in each group and the previous second data, and the second time difference between the second data and the next second data;

[0028] The second calculation module adds the first time difference and the second time difference to obtain the time;

[0029] The second calculation module multiplies each of the second data in the first group with its corresponding time and then sums them to obtain a first accumulated sum;

[0030] The second calculation module multiplies each of the second data in the second group with its corresponding time and then sums them to obtain a second cumulative sum;

[0031] The second calculation module divides the second sum by the first sum to obtain the segment coefficient;

[0032] The second calculation module adds the segment coefficients of each segment to obtain the charging deviation coefficient.

[0033] In conjunction with the first aspect, the method for correcting the estimated data and the actual data using a data correction module to obtain second data at different times includes:

[0034] The first difference is obtained by subtracting the estimated data from the actual data;

[0035] The second difference is obtained by subtracting the current battery temperature from the standard temperature.

[0036] The data correction module multiplies the second difference by a correction coefficient to obtain a correction value;

[0037] The data correction module adds 1 to the correction value and multiplies it by the first difference to obtain the second data.

[0038] In conjunction with the first aspect, the charging cycle data includes one or more of the following: charging time, charging current, charging voltage, SOC, battery temperature, and state of charge.

[0039] In conjunction with the first aspect, the method for determining the internal short circuit status of the power battery based on the charging deviation coefficient using a data analysis module includes:

[0040] The slope is obtained by linearly fitting the charging deviation coefficient with the number of charging cycles.

[0041] The data analysis module compares the slope with a preset slope threshold and determines the internal short circuit status of the battery based on the comparison result.

[0042] In a second aspect, an electronic device is provided, including a memory and a processor; the memory is used to store a computer program; the processor is used to implement the power battery internal short circuit detection method as described in the first aspect when the computer program is executed.

[0043] Thirdly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power battery internal short-circuit detection method as described in the first aspect.

[0044] One of the above technical solutions has the following advantages or beneficial effects:

[0045] Compared with existing technologies, this application provides a method for detecting internal short circuits in a power battery, comprising: extracting at least one segment of data from multiple charging cycle data of the battery to obtain first data; obtaining estimated data and actual data at different times based on the first data; correcting the estimated data and actual data to obtain second data at different times; grouping the second data obtained at different times; jointly calculating the second data from different groups to obtain a charging deviation coefficient; and determining the internal short circuit condition of the power battery based on the charging deviation coefficient. This application detects and determines the internal short circuit condition of the battery by obtaining the trend of the charging deviation coefficient from the battery's charging cycle data. Furthermore, the method of this application requires less data, has a shorter testing cycle, and incurs lower labor and material costs. Attached Figure Description

[0046] The technical solution and other beneficial effects of this application will become apparent from the following detailed description of specific embodiments in conjunction with the accompanying drawings.

[0047] Figure 1 This is a schematic diagram illustrating the steps of the method provided in the embodiments of this application;

[0048] Figure 2 A schematic diagram of the structure of the estimated data and actual data of battery #1 provided in the embodiments of this application;

[0049] Figure 3 This is a schematic diagram of the structure of the estimated and actual data for battery #2 provided in the embodiments of this application;

[0050] Figure 4 This is a schematic diagram of the method flow provided in the embodiments of this application. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0052] The applicant notes that existing technologies for detecting and warning of thermal runaway mainly fall into two categories: one is based on electrochemical models, which construct models by identifying electrochemical model parameters through experimental testing. This method requires long-term experimental testing and even battery disassembly to obtain the model parameters. The other category is data-driven methods, which currently typically achieve this by constructing a model of the relationship between characteristic factors of cloud data and thermal runaway.

[0053] Developing an electrochemical model-based early warning system requires testing to obtain model parameters. This involves numerous tests and a lengthy process, necessitating separate parameter identification for different battery models and systems, resulting in high cost and extended development time. Data-driven methods for internal short-circuit early warning typically identify anomalies in heat generation, voltage, and current. However, this method demands high-quality data. Furthermore, the batteries identified by this method must exhibit continuous internal short-circuit phenomena, potentially indicating that the short circuit has progressed to its later stages, leaving insufficient time for intervention.

[0054] This application proposes a method for detecting internal short circuits in power batteries. It detects and determines internal short circuits by observing the trend of the battery's voltage deviation coefficient during charging. As internal short circuits occur and evolve, the battery's deviation coefficient exhibits a significant changing trend. This method can rapidly detect internal short circuits in their early to mid-stages, thereby solving one of the aforementioned technical problems.

[0055] The specific implementation methods of this application are illustrated below through examples:

[0056] like Figure 1As shown in the figure, this application provides a method for detecting internal short circuits in a power battery, including:

[0057] S1: Extract at least one segment of data from the battery's multiple charge cycle data using the data acquisition module to obtain the first data;

[0058] The specific steps are as follows:

[0059] The data acquisition module acquires historical data of multiple charging cycles of the battery. The historical data of multiple charging cycles includes charging time, charging current, charging voltage, SOC, battery temperature and charging status. A charging cycle is one charging process of the battery. Whether it is a full charge from 0% to 100% or a charge from 40% to 80%, it is considered one charging cycle.

[0060] The historical data obtained from multiple charging cycles is cleaned to retain valid data. The cleaning methods include:

[0061] Charging data where the battery's charging start and end points fall within the 20%-80% SOC range are omitted;

[0062] The remaining qualified charging data is retained, and the segment between 20% and 80% of the total value in each charging cycle is selected as the first data. That is, only the data corresponding to the 20-80% range is extracted from each charging cycle, including the battery's charging time, charging current, charging voltage, SOC, battery temperature, and state of charge at that time. Since the battery's charging voltage and state of charge are more stable within the 20-80% range, it is more conducive to studying the relationship between battery SOC and voltage, and avoids data obtained under unstable battery conditions affecting the judgment of internal short circuits.

[0063] S2: Obtain estimated and actual data at different times from the first data through the first calculation module;

[0064] S201: Divide the first data into multiple intervals according to the interval to obtain interval data;

[0065] Divide the SOC data in the first dataset into 20-200 equal intervals, and take the data from one SOC interval each time.

[0066] S202: Draw the estimation reference line by taking the endpoint values ​​from the interval data;

[0067] Obtain the voltage values ​​at the start and end points of the SOC interval, and plot the voltage-time curve, which serves as the estimation reference line. In this application, the range of SOC intervals is relatively small; therefore, the relationship between voltage and time can be considered to be linear.

[0068] Perform the same operation on the other SOC intervals to obtain the same number of estimation reference lines as the number of SOC intervals.

[0069] Therefore, the voltage value at the starting point of each SOC interval is denoted as: V ikc1 The voltage value at the termination point is denoted as: V ikc2 Where i is the cell number, k is the number of cycles, and c is the corresponding SOC range;

[0070] S203: The first calculation module obtains the estimated data for the corresponding time based on the estimation reference line;

[0071] Select a time point within the obtained SOC range and obtain the corresponding estimated voltage value at that time point from the estimation reference line. This estimated voltage value is the estimated data.

[0072] S204: The first calculation module obtains the actual data at the corresponding time based on the first data;

[0073] Obtain the time point selected in step S203, and obtain the actual voltage value corresponding to that time point from the obtained historical data. This actual voltage value is the actual data.

[0074] Perform the same steps for the other SOC intervals to obtain the estimated and actual data for each SOC interval; denot the estimated voltage value as: RV ikcn The actual voltage value is recorded as: V ikcn The estimated voltage value at a given time point is obtained by estimating the reference line. The estimated voltage value is a theoretical value. By comparing the theoretical value with the actual voltage value, the difference between the actual and theoretical values ​​can be obtained.

[0075] S3: The estimated data and actual data are corrected by the data correction module to obtain the second data at different times;

[0076] S301: Subtract the estimated data from the actual data to obtain the first difference;

[0077] Subtract the estimated voltage value from the actual voltage value for each SOC interval to obtain the first difference, denoted as V. ikcn -RV ikcn ;

[0078] S302: Subtract the current battery temperature from the standard temperature to obtain the second difference;

[0079] Since temperature has a significant impact on battery voltage changes, temperature normalization is necessary. 25℃ is set as the standard temperature. The temperature corresponding to the selected time point in each SOC range is subtracted from the standard temperature to obtain a second difference, denoted as T. ik -25, where T ik The current temperature of the battery is denoted by i, the cell number is denoted by k, and the number of cycles is denoted by k.

[0080] S303: The data correction module multiplies the second difference by the correction coefficient to obtain the correction value;

[0081] Multiply the second difference by the correction coefficient to obtain the correction value, denoted as T. coe (T ik -25); where T coe T is the correction factor. coe The range of values ​​is 0 < T coe <1;

[0082] S304: The data correction module adds 1 to the correction value and multiplies it by the first difference to obtain the second data;

[0083] The correction value T coe (T ik Adding 1 to -25), we get: (1+T) coe (T ik -25);

[0084] The first difference V ikcn -RV ikcn Multiplying this by the correction value (after adding 1) yields the normalized data, which is the second data; the calculation formula is denoted as:

[0085] (V ikcn -RV ikcn ) 25℃ =(V ikcn -RV ikcn )*(1+T coe (T ik -25)).

[0086] S4: Group the second data obtained at different times using the data grouping module;

[0087] S401: Compare the second data at different times with a preset threshold input into the data grouping module;

[0088] The normalized data (V) in each SOC interval ikcn -RV ikcn ) 25℃ Compare with a set threshold, where the threshold is 0;

[0089] S402: If the second data is greater than the threshold, then the second data corresponding to that moment is assigned to the first group;

[0090] If the second data (V) ikcn -RV ikcn ) 25℃ If the value is greater than 0, then the second data (V) will be... ikcn -RV ikcn ) 25℃ Place them into the first group, and renumber each data point. Simultaneously, calculate the value of each second data point (V). ikcn -RV ikcn ) 25℃ Compared with the previous second data (V) ikcn -RV ikcn ) 25℃ The difference between the time points is denoted as Tspan. ikcp This will be combined with the second data (V) ikcn -RV ikcn ) 25℃ The difference between the time points is denoted as Tspan. ikcp+1 , where p is the number ordered chronologically;

[0091] S403: If the second data is less than the threshold, then the second data corresponding to that moment is assigned to the second group;

[0092] If the second data (V) ikcn -RV ikcn ) 25℃ If it is less than 0, then the second data (V) ikcn -RV ikcn ) 25℃ Place them into the second group, renumber each data point, and calculate the value of each second data point (V). ikcn -RV ikcn ) 25℃ Compared with the previous second data (V) ikcn -RV ikcn ) 25℃ The difference between the time points is denoted as Tspan. ikce This will be combined with the second data (V) ikcn -RV ikcn ) 25℃ The difference between the time points is denoted as Tspan. ikce+1 ,

[0093] Where e is the number ordered chronologically;

[0094] S5: The charging deviation coefficient is obtained by jointly calculating the second data from different groups through the second calculation module;

[0095] S501: The second calculation module obtains the first time difference between the second data and the previous second data in each group, and the second time difference between the second data and the next second data;

[0096] The first time difference in the first group is: Tspan ikcp The second time difference is: Tspan ikcp+1 ;

[0097] The first time difference in the second group is: Tspan ikce The second time difference is: Tspan ikce+1 ;

[0098] S502: The second calculation module adds the first time difference and the second time difference to obtain the time;

[0099] Adding the first time difference and the second time difference in the first group gives: (Tspan) ikcp +Tspan ikcp+1 );

[0100] Adding the first and second time differences in the second group yields: (Tspan) ikce +Tspan ikce+1 );

[0101] S503: The second calculation module multiplies each second data in the first group with its corresponding time and then sums them to obtain the first accumulated sum;

[0102] The second data (V) of the first group ikcn -RV ikcn ) 25℃ With its time and (Tspan ikcp +Tspan ikcp+1 Multiplying them together, we get: (V) ikcn -RV ikcn ) 25℃ *(Tspan ikcp +Tspan ikcp+1 );

[0103] The data are then summed to obtain: Σ|(V ikcn -RV ikcn ) 25℃ *(Tspan ikcp +Tspan ikcp+1 )|;

[0104] S504: The second calculation module multiplies each second data in the second group with its corresponding time and then sums them to obtain the second cumulative sum;

[0105] The second data (V) of the second group ikcn -RVikcn ) 25℃ With its time and for (Tspan) ikce +Tspan ikce+1 Multiplying them together, we get: (V) ikcn -RV ikcn ) 25℃ *(Tspan ikce +Tspan ikce+1 );

[0106] The data are then summed to obtain: Σ|(V ikcn -RV ikcn ) 25℃ *(Tspan ikce +Tspan ikce+1 )|;

[0107] S505: The second calculation module divides the second cumulative sum by the first cumulative sum to obtain the segment coefficient;

[0108] The formula for dividing the second sum by the first sum is:

[0109]

[0110] S506: The second calculation module adds up the section coefficients of each section to obtain the charging deviation coefficient;

[0111] The charging deviation coefficients of each SOC range are summed to obtain the charging deviation coefficient for that range, specifically the charging deviation coefficient within the 20-80% range.

[0112] ζ ik， =Σζ ikc ;

[0113] ζ for each cycle ik The charging deviation coefficient series of the battery under multiple cycles is obtained by calculation. The charging deviation coefficient is mainly the voltage deviation coefficient. The stability of the battery is judged by the voltage deviation, thereby judging the situation of internal short circuit in the battery.

[0114] S6: The data analysis module determines the internal short circuit status of the power battery based on the charging deviation coefficient;

[0115] S601: Linearly fit the charging deviation coefficient with the number of charging cycles to obtain the slope;

[0116] A linear fit was performed between the charging deviation coefficient and the number of charging cycles over multiple cycles, with the number of charging cycles as the x-axis and the charging deviation coefficient as the y-axis, to obtain a coefficient-cycle curve. The slope was then calculated from the coefficient-cycle curve. i ;

[0117] S602: The data analysis module compares the slope with a preset slope threshold and determines the internal short circuit status of the battery based on the comparison result.

[0118] Determine the slope i Does it exceed the preset slope threshold? If the slope... i If the slope exceeds the preset threshold for each level, a short circuit warning for the corresponding single-cell battery will be issued, and the battery number i and the slope will be reported. i The slope threshold ranges from 0.002 to 2 mV per cycle. That is, when the obtained slope... i When the slope is between 0.002-2mV / cycle, it indicates that the battery has not experienced an internal short circuit.

[0119] And when the slope i When the value is greater than or equal to 2, it needs to be determined based on the slope. i The numerical value needs to be further determined;

[0120] If 2 ≤ slope i If the value is less than 6, it is considered that the battery has an internal short circuit, and the internal short circuit is in its initial stage.

[0121] If 6 ≤ slope i If the value is less than 10, the battery is considered to have an internal short circuit, and the internal short circuit is in the middle stage.

[0122] If the slope i If the value is ≥10, the battery is considered to have an internal short circuit, and the internal short circuit is in its final stage. The above threshold range is statistically confirmed after multiple tests based on the method of this application.

[0123] Example

[0124] Taking a specific vehicle model that was flagged in the warning as an example, the technical solution is explained. The battery pack for this vehicle has been returned to the factory, and disassembly verified that the battery cells have internal short circuits. This technical solution is effective.

[0125] (1) Extract historical charging data of the vehicle, including charging time, charging current, charging voltage, battery SOC, charging temperature, charging status and other information, and perform data cleaning to retain valid data;

[0126] (2) Screen the starting SOC and ending SOC of the charging process. For the charging segment data that meets the requirements of starting SOC ≤ 20% and ending SOC ≥ 80%, extract the data in the 20%-80% SOC range and perform the following steps.

[0127] (3) Here, cell #1 is an internally short-circuited cell, and cell #2 is a normal cell. In the 200th cycle of both cells, select the data within the 79-80% SOC range, and record the voltage as V. 1,200,80,n The reference voltage is denoted as RV. 1,200,80,n The time interval between any two frames is 1 second.

[0128] On the voltage-time curve, take the starting point V of this interval. ikc1 and termination point V ikc2 Connect V ikc1 and V ikc2 This forms a linear voltage reference line, where i is the cell number, k is the number of cycles, and c is the corresponding SOC range.

[0129]

[0130]

[0131] (4) The voltage corresponding to the time of each frame on the voltage reference line is denoted as RV. ikcn n is the frame number ordered chronologically, and the actual voltage corresponding to each frame is denoted as V. ikcn ;like Figure 2 and Figure 3 The diagram shows the actual voltage and reference voltage of cell #1 with an internal short circuit and cell #2 in normal condition.

[0132] (5) Temperature correction: The above data were all collected at a temperature of 25℃, so no additional correction is required and they can be used directly.

[0133] (6) The table below shows the data obtained after comparing the internally short-circuited cell #1 with the threshold 0 and numbering it:

[0134]

[0135]

[0136] The diagonal bars in the table above indicate that there is no data.

[0137] (7) Based on the voltage deviation coefficient ζ ikc Definition, calculation of ζ 1，200，80 With ζ 2，200，80 :

[0138] ζ 1，200，80 =8.80;

[0139] ζ 2，200，80 =0.48;

[0140] For each individual cell, calculate the ζ for every 1% SOC within the 20%-80% SOC range.ikc And calculate the voltage deviation coefficient ζ of the battery cell during this charging. ik , where ζ ik =Σζ ikc ;

[0141] coefficient 1# 2# <![CDATA[ζ 1，200，21 ]]> 3.21 0.30 <![CDATA[ζ 1，200，22 ]]> 3.56 0.37 <![CDATA[ζ 1，200，23 ]]> 5.09 0.23 <![CDATA[ζ 1，200，24 ]]> 4.52 0.10 <![CDATA[ζ 1，200，25 ]]> 2.12 0.23 … … … <![CDATA[ζ 1，200，76 ]]> 6.94 0.74 <![CDATA[ζ 1，200，77 ]]> 4.80 0.35 <![CDATA[ζ 1，200，78 ]]> 7.96 0.66 <![CDATA[ζ 1，200，79 ]]> 8.22 0.88 <![CDATA[ζ 1，200，80 ]]> 8.80 0.48

[0142] (8) Calculate the ζ of each individual cell in different cycles. ik And for each individual cell, ζ ik A linear fit was performed with the number of iterations k over a range of approximately 100 iterations to obtain the slope. i Determine whether the slope value exceeds the preset slope threshold;

[0143]

[0144]

[0145] The fitting yielded:

[0146] Slope1 = 3.7;

[0147] Slope2 = 0.04;

[0148] Through big data statistics (see table below for data examples), the slope of normal battery cells of similar design and battery cells verified to have internal short circuits was analyzed. i The distribution yields the slope of a normal cell of this model. i Range is Slope i <2, Slope of internally short-circuited cell i Range is Slope i ≥2, therefore we can conclude that when the slope is... i When the slope is less than 2, all cells are within a safe range and there is no internal short circuit; furthermore, the slope of batteries 1, 2, 13, 14, and 17 is... i During the analysis, the slope of battery number 1 was... i The slope of battery #2 is 5.33. i The value is 5.20. Batteries 1 and 2 are in the initial stage of internal short circuit. The slope of battery 13 is... i The slope of battery #14 is 6.60. i The slope of battery #17 is 6.33. i The slope is 6.60, while batteries 13, 14, and 17 are in the middle stage of an internal short circuit. Therefore, the slope can be calculated. i When the value is greater than 6, the internal short circuit of the battery is in the intermediate stage;

[0149] Meanwhile, the slope was measured for size 8, 15, 18, and 24 batteries. i During the analysis, the slope of battery number 8 was... i The slope is 10.2 for battery #15. i The value is 9.30. Battery numbers 8 and 15 are both in the middle stage of an internal short circuit, while battery number 18 has a slope. i The slope is 11.55 for battery #24. i 12.65, while batteries 18 and 24 are in the final stage of an internal short circuit. Therefore, it can be deduced that the slope is... i When the value is greater than 10, the internal short circuit of the battery is in its final stage. Based on the above conclusion, we can conclude that:

[0150] 0 < Slope i <2, the battery does not have an internal short circuit;

[0151] 2≤Slope i <6, The battery has an internal short circuit, which is in its initial stage;

[0152] 6≤Slope i <10 indicates that the battery has an internal short circuit, which is in the intermediate stage.

[0153] Slope i ≥10 indicates that the battery has an internal short circuit, which is in its final stage.

[0154] Therefore, for the two cells mentioned above, if cell #1 exceeds the preset slope threshold, a corresponding level of internal short circuit warning will be issued and the cell number 1 and slope 3.7 will be reported. Based on the slope 3.7, it can be concluded that cell #1 is in the initial stage of internal short circuit. Cell #2 has not exceeded the threshold and no warning will be triggered.

[0155]

[0156]

[0157] This application provides an electronic device, including a memory and a processor; the memory is used to store a computer program; the processor is used to implement the above-described method for detecting internal short circuits in a power battery when the computer program is executed.

[0158] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-described method for detecting internal short circuits in a power battery.

[0159] The present application provides a detailed description of a power battery internal short circuit detection method, electronic device, and storage medium. Specific examples have been used to illustrate the principles and implementation methods of the present application. The descriptions of the above embodiments are only for the purpose of helping to understand the technical solutions and core ideas of the present application. Those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting internal short circuits in a power battery, characterized in that, include: The data acquisition module extracts at least one segment of data from the battery's multiple charge cycle data to obtain the first data. The first calculation module obtains estimated and actual data at different times from the first data, including: The first data is divided into multiple intervals to obtain interval data; The estimation reference line is obtained by plotting the endpoint values ​​from the interval data. The first calculation module obtains the estimated data for the corresponding time based on the estimation reference line; The first calculation module obtains the actual data at the corresponding time based on the first data; The estimated data and the actual data are corrected by a data correction module to obtain second data at different times, including: The first difference is obtained by subtracting the estimated data from the actual data; The second difference is obtained by subtracting the current battery temperature from the standard temperature. The data correction module multiplies the second difference by a correction coefficient to obtain a correction value; The data correction module adds 1 to the correction value and multiplies it by the first difference to obtain the second data; The second data obtained at different times is grouped using the data grouping module; The charging deviation coefficient is obtained by jointly calculating the second data from different groups through the second calculation module. The data analysis module determines the internal short circuit status of the battery based on the charging deviation coefficient.

2. The method for detecting internal short circuits in a power battery as described in claim 1, characterized in that, The method for grouping the second data obtained at different times using the data grouping module includes: The second data at different times is compared with a pre-set threshold input into the data grouping module; If the second data is greater than the threshold, then the second data corresponding to the time point is assigned to the first group; If the second data is less than the threshold, then the second data corresponding to the time point is assigned to the second group.

3. The method for detecting internal short circuits in a power battery as described in claim 1, characterized in that, The method for extracting at least one segment of data from multiple battery charging cycle data using a data acquisition module to obtain the first data includes: The data acquisition module acquires historical data of multiple charging cycles of the battery; The data acquisition module extracts the segment of each charging cycle data that is between 20% and 80% of the total value as the first data.

4. The method for detecting internal short circuits in a power battery as described in claim 2, characterized in that, The method for obtaining the charging deviation coefficient by jointly calculating the second data from different groups using the second calculation module includes: The second calculation module obtains the first time difference between the second data in each group and the previous second data, and the second time difference between the second data and the next second data; The second calculation module adds the first time difference and the second time difference to obtain the time; The second calculation module multiplies each of the second data in the first group with its corresponding time and then sums them to obtain a first accumulated sum; The second calculation module multiplies each of the second data in the second group with its corresponding time and then sums them to obtain a second cumulative sum; The second calculation module divides the second sum by the first sum to obtain the segment coefficient; The second calculation module adds the segment coefficients of each segment to obtain the charging deviation coefficient.

5. The method for detecting internal short circuits in a power battery as described in claim 1, characterized in that, The charging cycle data includes one or more of the following: charging time, charging current, charging voltage, SOC, battery temperature, and state of charge.

6. The method for detecting internal short circuits in a power battery as described in claim 1, characterized in that, The method for determining the internal short circuit status of the power battery based on the charging deviation coefficient using a data analysis module includes: The slope is obtained by linearly fitting the charging deviation coefficient with the number of charging cycles. The data analysis module compares the slope with a preset slope threshold and determines the internal short circuit status of the battery based on the comparison result.

7. An electronic device, characterized in that: It includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the power battery internal short circuit detection method as described in any one of claims 1 to 6 when the computer program is executed.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the power battery internal short circuit detection method as described in any one of claims 1 to 6.

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