Abnormal state detection methods, devices, equipment and storage media for battery packs

Abstract

This invention belongs to the field of battery technology, specifically relating to a method, apparatus, device, and storage medium for detecting abnormal states of battery packs, addressing the problems of inefficiency, high cost, and false alarms in existing detection technologies. The method includes: acquiring records from historical charging data of the battery pack; calculating the state-of-charge (SOC) deviation of each battery cell during each charging process based on each historical charging record; calculating the rate of increase of SOC deviation for each battery cell based on the SOC deviation and its corresponding time information; calculating the internal resistance of each battery cell; comparing the rate of increase of SOC deviation and the internal resistance of each battery cell with a preset reference threshold, and determining whether the state of each battery cell is abnormal based on the comparison result. This method enables real-time online monitoring of vehicles, reducing the cost of offline testing and the installation of other devices; effectively identifying abnormalities in specific battery cells within a battery string; and proactively identifying and addressing problematic batteries, thus reducing vehicle operation risks.

Description

Technical Field

[0001] This invention belongs to the field of battery technology, and specifically relates to a method, device, equipment and storage medium for detecting abnormal states of battery packs. Background Technology

[0002] With the increasing demand for safety performance in new energy vehicles, the safety of power batteries in these vehicles has become a major concern. Among the solutions, developing battery abnormality detection technology to identify and address faulty batteries that are prone to safety issues in advance is one of the effective methods to solve battery safety problems.

[0003] In existing technologies, there are two main methods for detecting abnormal states of battery packs. One is offline visual inspection, where the battery pack is removed when the vehicle is taken to a service station for maintenance, and the dimensions of each battery module are measured and compared with standards to determine whether the battery is in an abnormal state. The other is that manufacturers install stress detection devices inside the battery pack to identify abnormal battery states by detecting stress changes.

[0004] However, identifying abnormal battery conditions through on-site inspection and dimensional measurement lacks specificity, is time-consuming, labor-intensive, inefficient, and costly. It cannot inspect a large number of batteries in a short period of time, nor can it monitor batteries in real time and continuously. Installing stress detection devices requires adding extra stress monitoring components to each module, increasing production costs and occupying limited space within the battery pack. Secondly, the stress monitoring area is limited, making it impossible to accurately monitor the entire battery status, leading to missed detections. Furthermore, changes in battery pack stress are greatly affected by external environmental factors such as temperature and vibration, resulting in false alarms. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method, apparatus, device and storage medium for detecting abnormal states of battery packs that overcomes or at least partially solves the above problems.

[0006] In a first aspect, this disclosure provides a method for detecting abnormal states of a battery pack, including:

[0007] Retrieve each historical charging record from the battery pack's historical charging data;

[0008] Based on each historical charging record, calculate the state-of-charge deviation of each battery cell during each charging process;

[0009] Based on the state-of-charge deviation of each cell and its corresponding time information, the rate of increase of the state-of-charge deviation of each cell is calculated; and the internal resistance of each cell is calculated.

[0010] The rate of increase in the state of charge deviation and the internal resistance of each battery cell are compared with a preset reference threshold, and the state of each battery cell is detected as abnormal based on the comparison results.

[0011] Secondly, this disclosure provides an abnormal state detection device for a battery pack, comprising:

[0012] The acquisition module is suitable for acquiring each historical charging record contained in the historical charging data of the battery pack;

[0013] The calculation module is suitable for calculating the state-of-charge deviation of each battery cell in each historical charging record, and for calculating the rate of increase of the state-of-charge deviation of each battery cell based on the state-of-charge deviation and its corresponding time information; and for calculating the internal resistance of each battery cell.

[0014] The detection module is suitable for comparing the rate of increase of the state of charge deviation and the internal resistance of each battery cell with a preset reference threshold, and detecting whether the state of each battery cell in the battery pack is abnormal based on the comparison results.

[0015] Thirdly, this disclosure provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor;

[0016] The memory stores one or more computer programs that can be executed by at least one processor, which enables the at least one processor to implement the abnormal state detection method for the battery pack as described above.

[0017] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the abnormal state detection method for a battery pack as described above.

[0018] The present disclosure provides a method for detecting abnormal states in battery packs, enabling the detection of such abnormal states. This method only requires online monitoring of the vehicle, eliminating the need for extensive on-site inspections and dimensional measurements, thus reducing troubleshooting costs. Furthermore, the method effectively identifies specific battery cells within the battery pack that are in an abnormal state, facilitating subsequent repair and maintenance. Additionally, the method eliminates the need for additional devices on the battery pack, reducing production costs. Finally, the method enables large-scale real-time online monitoring of vehicle batteries, allowing for timely detection and handling of battery packs in abnormal states, reducing vehicle operational risks, and achieving the long-term goal of proactive identification, early intervention, and risk mitigation.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0021] Figure 1 The flowchart of an abnormal state detection method for a battery pack provided in Embodiment 1 of the present invention is shown.

[0022] Figure 2 The flowchart of a battery pack abnormal state detection method provided in Embodiment 2 of the present invention is shown;

[0023] Figure 3 The flowchart of the power battery bulging defect identification and detection method provided in a specific example of Embodiment 2 of the present invention is shown.

[0024] Figure 4 The flowchart of the power battery charging SOC deviation calculation method provided in a specific example of Embodiment 2 of the present invention is shown.

[0025] Figure 5 The static SOC-OCV curve of the charging process of the power battery under test is shown in a specific example of Embodiment 2 of the present invention.

[0026] Figure 6 A diagram illustrating the time period segmentation method within a data source range provided in a specific example of Embodiment 2 of the present invention is shown.

[0027] Figure 7 The diagram shows the internal resistance control of each cell in the power battery under test in a specific example of Embodiment 2 of the present invention.

[0028] Figure 8 This diagram shows a structural block diagram of an abnormal state detection device for a battery pack according to Embodiment 3 of the present invention.

[0029] Figure 9 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention is shown. Detailed Implementation

[0030] To enable those skilled in the art to better understand the technical solutions of this disclosure, exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments of this disclosure to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0031] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0032] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0033] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Words such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0034] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined herein.

[0035] Example 1

[0036] Figure 1 A flowchart of an abnormal state detection method for a battery pack according to Embodiment 1 of the present invention is shown. (Refer to...) Figure 1 The method includes:

[0037] Step S110: Obtain each historical charging record contained in the historical charging data of the battery pack.

[0038] In this embodiment, the battery pack mainly refers to a power battery, which is a power source that provides power to the tool. It primarily refers to the storage battery that powers electric vehicles, electric trains, electric bicycles, and golf carts, and is distinguished from the starting battery used to start a car engine. The power batteries in this embodiment are mostly lithium-ion secondary batteries such as lithium iron phosphate, lithium cobalt oxide, lithium nickel cobalt manganese oxide, and lithium nickel cobalt aluminum oxide.

[0039] Historical charging data of the battery pack is used to describe the historical charging status of the battery pack. The battery pack is structured as multiple strings of cells connected in parallel or in series. Accordingly, when obtaining historical charging data of the battery pack, it is necessary to obtain each historical charging record of each cell in each string, including: the start time of each charging process and the static voltage of each cell before and after each charging. The charging data mentioned above is mainly used for the subsequent calculation process in this embodiment. Those skilled in the art can adjust it according to specific circumstances when implementing this method, and should not be limited by the data requirements of this embodiment.

[0040] Step S120: Based on each historical charging record, calculate the state-of-charge deviation of each battery cell during each charging process.

[0041] The state of charge (SOC) of a battery refers to the ratio of its remaining capacity after a period of use or long-term disuse to its capacity in a fully charged state. By obtaining the historical charging records in step S110, the static voltage of each battery cell before and after charging is calculated, thus determining the corresponding battery SOC based on the SOC-OCV curve. OCV, or open circuit voltage, is the terminal voltage of the battery in an open-circuit state. The SOC deviation of each battery cell is the difference between the initial and final SOC of that cell during the charging process and the average difference in SOC across all battery cells.

[0042] Step S130: Calculate the rate of increase of the state of charge deviation for each battery cell based on the individual cell state of charge deviation and the time information corresponding to the individual cell state of charge deviation.

[0043] The individual cell state of charge deviation has been calculated in step S120. The time information corresponding to the individual cell state of charge deviation includes the relative time corresponding to the individual cell state of charge deviation for each charge. The specific calculation rule is as follows: using days as the unit, the end of the first charge is recorded as day 0, and the above relative time is the time interval between the end of each charge and the end of the first charge. Therefore, based on the individual cell state of charge deviation and its corresponding time information, the rate of increase of the state of charge deviation for each battery cell is calculated.

[0044] Step S140: Calculate the internal resistance of each battery cell.

[0045] Specifically, data segments from the battery pack's historical charging records used for calculating internal resistance are selected, and then the internal resistance of each individual battery cell is calculated. Those skilled in the art can flexibly select the data segments used for calculating internal resistance depending on the specific circumstances.

[0046] Step S140 can be performed before or simultaneously with steps S120 and S130.

[0047] Step S150: Compare the rate of increase of the state of charge deviation and the internal resistance of each battery cell with a preset reference threshold, and detect whether the state of each battery cell is abnormal based on the comparison results.

[0048] Specifically, various standards can be set for comparing the rate of increase of the state of charge deviation of a single battery cell and the internal resistance of the cell with a preset reference threshold. Those skilled in the art can flexibly set different reference thresholds and judgment standards for the problem to be tested, the battery model, the actual application scenario, etc. After obtaining the comparison results, the judgment standards are used to determine whether the state of the battery cell is abnormal.

[0049] Therefore, this method monitors historical battery charging data online, calculates results from various data points, compares the calculated results with preset thresholds, and determines the status of the battery in real time based on set conditions. This reduces the manpower and material costs required for offline testing and the installation of additional testing devices. It can also test a large number of battery packs simultaneously, improving testing efficiency. Furthermore, this method can monitor the data of each individual battery cell within the battery pack to determine the specific abnormal state of that cell. This more targeted approach improves efficiency while reducing the possibility of missed or false alarms in related stress monitoring technologies.

[0050] Example 2

[0051] Figure 2 A flowchart of a battery pack abnormal state detection method according to Embodiment 2 of the present invention is shown. (Refer to...) Figure 2 The method includes:

[0052] Step S210: Obtain each historical charging record contained in the historical charging data of the battery pack.

[0053] The specific implementation method of this step is the same as that of step S110, and will not be repeated here.

[0054] Optionally, when acquiring historical charging data for the battery pack, it is necessary to acquire each historical charging record for each individual battery cell in the string, including: the start time of each charging process, the static voltage of each battery cell before and after each charging, and the voltage and current data of each battery cell in the two frames before and after the current jump to full charge during the last charging. The charging data mentioned above is mainly used for subsequent calculations in this embodiment. Those skilled in the art can adjust it according to specific circumstances when implementing this method, and it should not be limited by the data requirements in this embodiment.

[0055] Step S220: Based on each historical charging record, calculate the state-of-charge deviation of each battery cell during each charging process.

[0056] Specifically, the deviation of the state of charge (SOC) of a single cell can be calculated based on the difference in SOC during battery charging and the average value. This step can be achieved in the following ways:

[0057] First, calculate the difference in state of charge of each individual battery cell during each charging process.

[0058] For each historical charging record, the initial state of charge (SOC) of each battery cell before the start of the current charging process and the final state of charge (SOC) after the end of the current charging process are determined. The difference between the final and initial SOC of each battery cell is defined as the SOC difference for that battery cell during the current charging process. Specifically, the static voltage of each battery cell before and after the start of the current charging process is obtained, and the SOC of each battery cell before and after charging is calculated based on the static SOC-OCV curve of the battery charging process. The SOC difference for each battery cell is then calculated based on the SOC of each battery cell before and after charging.

[0059] Then, based on the difference in state of charge of each battery cell during this charging process, the average state of charge of each battery cell during this charging process is calculated.

[0060] For example, first sort the state-of-charge (SOC) differences of the individual cells within the battery pack from largest to smallest. Then, remove the top 10% and bottom 10% of the data; that is, the SOC differences of the top 10% and bottom 10% of the cells in the above sorting are not included in the calculation of the average SOC. If the 10% of the sorting order is not an integer, round down to the nearest integer. Calculate the average SOC difference of the remaining individual cells, which is the average SOC of each individual cell during this charging process.

[0061] Finally, for each battery cell, the difference between the cell's state of charge difference and the average cell state of charge is determined as the cell's state of charge deviation.

[0062] Optionally, to improve the accuracy of subsequent calculations, before calculating the state-of-charge (SOC) deviation of each battery cell, historical charging records corresponding to charging processes where the change in SOC before and after charging is not less than a preset threshold are further selected from the historical charging data of the battery pack, and used as the data source for subsequent calculations. Accordingly, when calculating the SOC deviation of each battery cell, the historical charging records corresponding to charging processes where the change in SOC before and after charging is not less than the preset threshold are used as the calculation data source.

[0063] The change threshold is set by those skilled in the art as needed, and historical charging records that meet the criteria will be selected as the data source for calculating the abnormal state detection of the battery pack.

[0064] Step S230: Based on the relative duration of each charging process of each battery cell, divide the historical charging period of each battery cell corresponding to each charging process into multiple time periods.

[0065] Specifically, the time interval between the current charging time and the first charging time for each battery cell is determined as the relative duration of the current charging process for that battery cell.

[0066] The historical charging periods selected in step S220 are divided into four time intervals based on the maximum relative time value: a first time interval, a second time interval adjacent to the first time interval, a third time interval adjacent to the second time interval, and a fourth time interval adjacent to the third time interval. The first and second time intervals are designated as the first time period, the second and third time intervals as the second time period, and the third and fourth time intervals as the third time period.

[0067] Step S240: Calculate the rate of increase of the state of charge deviation for each battery cell based on the individual cell state of charge deviation and its corresponding time information.

[0068] Specifically, the aforementioned single-cell state of charge deviation has been calculated in step S220, and the time information corresponding to the single-cell state of charge deviation is the relative duration of each battery cell in each charging process, which has been calculated in step S230.

[0069] Specifically, it is necessary to calculate the rate of increase of state-of-charge deviation for each battery cell in each time period. Using the state-of-charge deviation for each charge within the time period calculated in step S220 as the y-value, and the relative time corresponding to the state-of-charge deviation for each charge calculated in step S230 as the x-value, a linear fit is performed using a function formula. The fitted parameter value is then used as the rate of increase of the state-of-charge deviation for each battery cell during each charge process. The rate of increase of the state-of-charge deviation for the three time periods should be calculated separately.

[0070] Step S250: Calculate the internal resistance of each battery cell.

[0071] Specifically, for each battery cell, calculate the voltage difference and current difference before and after the jump to full charge current during the last charge; and calculate the internal resistance of the battery cell based on the ratio between the voltage difference and the current difference.

[0072] Step S250 can be performed before or simultaneously with steps S220, S230, and S240.

[0073] Step S260: Compare the rate of increase of the state of charge deviation and the internal resistance of each battery cell with a preset reference threshold, and detect whether the state of each battery cell is abnormal based on the comparison results.

[0074] The preset reference thresholds include multiple time-period reference thresholds and internal resistance reference thresholds, each corresponding to a different time period. The multiple time-period reference thresholds are used to determine whether the rate of increase in the state-of-charge deviation of a single battery cell increases over time; the internal resistance reference thresholds are used to determine whether the internal resistance of a single cell is outlier. Specifically, the aforementioned multiple time-period reference thresholds include: a first time-period reference threshold, a second time-period reference threshold, and a third time-period reference threshold.

[0075] Specifically, the rate of increase in the state of charge deviation and the internal resistance of each battery cell are compared with a preset reference threshold. Based on the comparison results, the state of each battery cell is detected to determine if it is abnormal. The core logic is that if the rate of increase in the state of charge deviation of a certain battery cell is increasing and the internal resistance of the cell is out of range, it indicates that the battery cell is in an abnormal state. The specific steps include the following:

[0076] Step 1: Compare the rate of increase of state of charge deviation of each battery cell calculated in step S240 within the first time period with the reference threshold of the first time period to obtain the first comparison result.

[0077] Step 2: Calculate the first preset calculation result between the growth rate of the state of charge deviation of each battery cell in the second time period and the growth rate of the state of charge deviation in the first time period, as calculated in step S240. Compare this result with the reference threshold of the second time period to obtain the second comparison result.

[0078] Step 3: Calculate the second preset calculation result between the growth rate of the state of charge deviation of each battery cell in the third time period and the growth rate of the state of charge deviation in the second time period, and compare the result with the reference threshold of the third time period to obtain the third comparison result.

[0079] Step 4: Compare the internal resistance of each battery cell calculated in step S250 with the internal resistance reference threshold to obtain the fourth comparison result; wherein, the internal resistance reference threshold is determined based on the average value and standard deviation of the internal resistance of each battery cell.

[0080] Step 5: Based on at least one of the first, second, third, and fourth comparison results, check whether the state of each battery cell is abnormal. That is, if all four comparison results of a battery cell meet the conditions for determining abnormality, it indicates that the battery cell is in an abnormal state.

[0081] Specifically, abnormal states of the battery pack include: bulging state, abnormal aging state, and abnormal consistency assessment state.

[0082] Those skilled in the art can flexibly adjust the execution order of the above steps, and can break down the above steps into more steps, or merge them into fewer steps, and can also delete some of the steps. Furthermore, Embodiment 1 and Embodiment 2 can be combined with each other, and the present invention does not limit this. Moreover, the above steps can be repeatedly executed; for example, historical charging record data of the battery can be collected periodically to detect whether the battery is currently in an abnormal state. In summary, this method can achieve real-time and continuous monitoring of whether the battery is in an abnormal state.

[0083] In summary, this method monitors historical battery charging data online, calculates results from various data points, compares the calculated results with preset thresholds, and determines the appropriate outcome based on set conditions. This allows for real-time detection of abnormal battery conditions, reducing the manpower and material costs associated with offline testing and the installation of additional detection devices. It also enables simultaneous testing of large numbers of battery packs, improving efficiency. Furthermore, by monitoring data from each individual battery cell within the battery pack, this method can identify specific cells in an abnormal state, improving efficiency through targeted analysis and reducing the likelihood of false alarms or missed detections common in stress monitoring technologies. Moreover, by filtering historical charging records corresponding to the charging process using preset threshold changes, the scope of the calculation data source is narrowed, resulting in more targeted data analysis and reducing unnecessary workload.

[0084] To facilitate understanding, a specific example will be used to describe the detailed implementation of this embodiment. This example uses a method for identifying and detecting bulging defects in power batteries. In this example, the power battery pack structure consists of n strings of battery cells connected in series, where i refers to the i-th string of battery cells in the battery pack, 1≤i≤n, and i is an integer. Specifically, the power battery under test selected in this example is a pack of 88 strings of battery cells connected in series, i.e., the power battery under test in this example is n=88, 1≤i≤88, and i is an integer.

[0085] Figure 3 A flowchart illustrating a method for identifying and detecting bulging defects in a power battery, as shown in this example, is presented. (Refer to...) Figure 3 This example includes the following detailed steps:

[0086] Step S310: Select the data source for the detection and calculation of bulging defects in the power battery under test. In this specific example, the charging process in which the average SOC difference of the power battery under test in the most recent 60 days is greater than or equal to 30% (i.e., ΔSOC is ≥30%) is selected as the data source for the detection and calculation of bulging defects, where ΔSOC refers to the difference in static SOC before and after the start of battery charging.

[0087] Step S320: Calculate the ΔSOCi deviation of the power battery for each charge. In this example, the SOC deviation of each individual battery cell for each charge within the data source range is calculated, and ΔSOCi deviation is recorded. The method for calculating the SOC deviation of a power battery for a given charge is as follows: Figure 4 As shown, the specific steps include the following:

[0088] Step S410: Obtain the static voltage of each battery cell before the start of this charging cycle. Let OCVi be the initial voltage, i refers to the i-th battery cell in the battery pack, 1≤i≤n, and i is an integer.

[0089] Step S420: Based on the static SOC-OCV curve of the power battery itself during the charging process, calculate the SOC state of each battery cell before charging, and denote SOCi as initial; wherein, the static SOC-OCV curve of the power battery selected in this example during the charging process is shown in [reference needed]. Figure 5 ;

[0090] Step S430: Obtain the static voltage of each cell of the power battery after this charging is completed, and record it as OCVi.

[0091] Step S440: Based on the static SOC-OCV curve of the charging process of the power battery itself, calculate the SOC state of each battery cell after charging, and record SOCi as the final value.

[0092] Step S450: Calculate the SOC difference of each battery cell in the power battery using the formula △SOCi=(SOCi_end - SOCi_start), i.e. △SOCi.

[0093] Step S460: Calculate the average value of the SOC difference of each cell in the power battery, i.e., ΔSOC average. ΔSOC average is calculated in the following way:

[0094] First, sort the charging SOC differences of each battery cell in the power battery from largest to smallest;

[0095] Then, based on the sorting order, the SOC difference between the top 10% and bottom 10% of battery cells is excluded. That is, the SOC difference between the top 10% and bottom 10% of battery cells in the sorting order is not included in the ΔSOC average calculation. If the 10% of the sorting order is not an integer, then the integer part is taken. In this example, the power battery under test has 88 battery cells, and the 10% of the sorting order is an integer of 8. This means that among the ΔSOCi values ​​of each battery cell in the power battery under test, the 8 largest and 8 smallest ΔSOCi values ​​are not included in the ΔSOC average calculation of the power battery under test.

[0096] Finally, the average of the SOC differences of the remaining battery cells is calculated, which is the average ΔSOC. In this example, the average of the ΔSOCi values ​​from the 9th to the 80th ranked ΔSOCi values ​​is calculated, and this value is the average ΔSOC of the battery under test.

[0097] Step S470: Calculate the charging SOC deviation of each battery cell in the power battery using the formula △SOCi deviation = (△SOCi - △SOC average).

[0098] Step S330: Calculate the relative time corresponding to the ΔSOCi deviation of the power battery for each charge, denoted as t. The calculation rule for t is: the time interval between the end of each charge within the data segment and the end of the first charge within the data segment, in days; the t value for the end of the first charge is 0 days. In this example, the relative time t corresponding to the ΔSOCi deviation of each charge within the data source range of the power battery under test is shown in Table 1.

[0099] Table 1

[0100]

[0101]

[0102]

[0103] Step S340: Divide the data source into three time periods based on the data source's time range. The splitting rules are detailed below. Figure 6The specific method is as follows: the data source range is divided into four equal parts according to the maximum relative time t value. The first and second parts are recorded as the first time range, the second and third parts as the second time range, and the third and fourth parts as the third time range. In this example, the three time periods of the data source range of the power battery under test are shown in Table 1.

[0104] Step S350: Calculate the rate of increase of charging SOC deviation for each individual cell of the power battery under test within a time period. The calculation method for the rate of increase of charging SOC deviation for each individual cell of the power battery within a time period is as follows:

[0105] Using the ΔSOCi deviation of each charge within the time period of the power battery under test as the ordinate y value, and the relative time t corresponding to the ΔSOCi deviation of each charge as the abscissa x value, a linear fit is performed using the formula y = ki * x + b, where the ki value of the linear fit is the growth rate of the SOC deviation of the i-th battery cell.

[0106] Based on the three time periods extracted in step S340, the charging SOC deviation growth rate of each individual battery cell within its time period is calculated using the method for calculating the charging SOC deviation growth rate within each time period. The growth rate within the first time period is denoted as Ki1, the second as Ki2, and the third as Ki3. To facilitate subsequent threshold setting and anomaly detection, Ki1, Ki2, and Ki3 are multiplied by 1000 to amplify the signal values. The amplified Ki1, Ki2, and Ki3 signal values ​​calculated for the battery under test in this example are shown in Table 2.

[0107] Table 2

[0108]

[0109]

[0110]

[0111]

[0112] Step S360: Calculate the internal resistance of each cell in the power battery under test. In this example, the data segment before and after the last charge to full charge current jump is selected from the data source range to calculate the current internal resistance of each cell, denoted as Ωi. The internal resistance values ​​of each cell in the power battery under test selected in this example are shown in [reference needed]. Figure 7 . Figure 7In the diagram, the vertical axis represents the internal resistance value of each individual cell in the power battery under test, and the horizontal axis represents the serial number of each individual cell. The method for determining the internal resistance of each individual cell in the power battery includes the following specific steps:

[0113] Step 1: Select the data segment for calculating the internal resistance of the battery under test. In this example, the voltage and current data of each cell are selected from two frames before and after the moment of the current jump from the last charge to full charge of the battery under test for subsequent internal resistance calculation.

[0114] Step 2: Calculate the internal resistance of each battery cell using the formula Ωi = (OCVi1 - OCVi2) / (I2 - I1). Where: Ωi is the internal resistance of the i-th battery cell in the string, OCVi1 is the voltage of the i-th battery cell before the current jump, OCVi2 is the voltage of the i-th battery cell after the current jump, I2 is the current of the battery under test before the current jump, and I1 is the current of the battery under test after the current jump. In this example, the battery charging current is negative, and the battery discharging current is positive.

[0115] Step S370: Determination of Power Battery Swelling Defect. The principle for determining power battery swelling defects is as follows: within the data source range, there exists a charging SOC deviation of a certain battery cell i, i.e., the deviation ΔSOCi gradually increases with relative time t, and the larger the relative time t, the faster the rate of increase of the deviation ΔSOCi, and the internal resistance Ωi of this battery cell is severely outlier. Based on this principle for determining power battery swelling defects, different battery models have different specific determination conditions. In this example, considering the specific model of the battery under test, the specific determination conditions for the swelling defect of the power battery under test are as follows:

[0116] (1) Ki3*1000>a, where a is the threshold parameter;

[0117] (2) Ki2*1000 / |Ki1*1000|>b, where b is the threshold parameter;

[0118] (3) Ki3*1000 / Ki2*1000>c, where c is the threshold parameter;

[0119] (4) Ωi>UCL, where UCL is the control limit of the single-value control chart of the internal resistance of the power battery under test. UCL is calculated by the formula UCL=Ω_average+3*σ, where Ω_average is the average value of the internal resistance of each cell of the power battery under test, and σ is the standard deviation of the internal resistance of each cell of the power battery under test.

[0120] Regarding the values ​​of threshold parameters a, b, and c in the above four criteria for determining power battery bulging defects, the following explanations are provided:

[0121] (1) The values ​​of threshold parameters a, b, and c are related to the model of the power battery under test. The values ​​of the threshold parameters differ for different models of power batteries under test;

[0122] (2) The values ​​of threshold parameters a, b, and c are related to the measured sample data of bulging battery packs. As the sample data increases, the values ​​of threshold parameters a, b, and c will be gradually optimized and eventually reach an optimal value.

[0123] In this example, the threshold parameter a of this type of power battery is 1.7, the threshold parameter b is 1.1, and the threshold parameter c is 1.1.

[0124] If there is an i in the power battery under test that satisfies the above four judgment conditions, then the power battery under test is determined to have a bulging defect, and the i-th battery cell in the string is bulging. Otherwise, the power battery is determined not to have a bulging defect.

[0125] In this example, the values ​​i=76 and i=82 in the tested power battery both satisfy the above four judgment conditions, as shown in Table 2 and... Figure 7 Therefore, it was determined that the battery under test had a bulging defect, and that the 76th and 82nd battery cells were bulging.

[0126] Step S380: Power Battery Safety Warning. In this example, if the battery is determined to have a bulging defect, a risk warning is issued for the power battery, and the warning information is sent to relevant personnel via communication technology.

[0127] To verify this method, the power battery under test in this example was returned to the factory. It was found that the power battery with the bulging defect identified by this method exhibited obvious gas-generating bulging defects. Furthermore, the power battery with these bulging defects was disassembled, and it was discovered that the 76th and 82nd strings of the battery had severe gas-generating bulging defects in individual cells. This is consistent with the results of the aforementioned method for identifying bulging defects in the power battery under test. The bulging of the power battery under test and the severe gas-generating bulging defects in the 76th and 82nd strings of the battery after disassembly demonstrate the practical feasibility of this method.

[0128] Example 3

[0129] Figure 8 A structural block diagram of a battery pack abnormal state detection device according to Embodiment 3 of the present invention is shown. (Refer to...) Figure 8 The device includes:

[0130] The acquisition module 81 acquires each historical charging record contained in the historical charging data of the battery pack;

[0131] The calculation module 82 is adapted to calculate the state-of-charge deviation of each battery cell during each charging process based on each historical charging record; calculate the rate of increase of the state-of-charge deviation of each battery cell based on the state-of-charge deviation and its corresponding time information; and calculate the internal resistance of each battery cell.

[0132] The detection module 83 is adapted to compare the rate of increase of the state of charge deviation and the internal resistance of each battery cell with a preset reference threshold, and detect whether the state of each battery cell is abnormal based on the comparison results.

[0133] Optionally, module 81 is specifically adapted to:

[0134] From the historical charging data of the battery pack, select historical charging records corresponding to charging processes in which the change in static state of charge before and after charging is not less than a preset change threshold.

[0135] Optionally, the computing module 82 is specifically adapted to:

[0136] For each historical charging record, determine the initial state of charge of each battery cell before the start of the current charging process and the final state of charge after the end of the current charging process. The difference between the final state of charge and the initial state of charge of the battery cell is determined as the single cell state of charge difference during the current charging process.

[0137] Based on the difference in state of charge of each battery cell during this charging process, calculate the average state of charge of each battery cell during this charging process.

[0138] For each battery cell, the difference between the cell's state of charge difference and the average cell state of charge is defined as the cell state of charge deviation.

[0139] Optionally, the computing module 82 is specifically adapted to:

[0140] The time interval between the current charging time and the first charging time for each battery cell is determined as the relative duration of the current charging process for that battery cell.

[0141] Based on the relative duration of each charging process for each battery cell, the historical charging period corresponding to each charging process for each battery cell is divided into multiple time periods, and the rate of increase of the state of charge deviation for each battery cell in each time period is calculated.

[0142] Optionally, based on the relative duration of each charging process for each battery cell, the historical charging period corresponding to each charging process for each battery cell is divided into multiple time periods, including:

[0143] The historical charging period is divided into a first time interval, a second time interval adjacent to the first time interval, a third time interval adjacent to the second time interval, and a fourth time interval adjacent to the third time interval.

[0144] The first and second time intervals are defined as the first time period, the second and third time intervals are defined as the second time period, and the third and fourth time intervals are defined as the third time period.

[0145] Optionally, the multiple time period reference thresholds include: a first time period reference threshold, a second time period reference threshold, and a third time period reference threshold.

[0146] Optionally, the computing module 82 is specifically adapted to:

[0147] For each battery cell, calculate the voltage difference and current difference before and after the jump to full charge current during the last charge; calculate the internal resistance of the battery cell by the ratio between the voltage difference and the current difference.

[0148] Optionally, the preset reference thresholds include: multiple time period reference thresholds corresponding to different time periods; wherein, the multiple time period reference thresholds are used to determine whether the rate of increase of the state of charge deviation of a single battery cell increases with the passage of time; and the preset reference thresholds include: an internal resistance reference threshold; wherein, the internal resistance reference threshold is used to determine whether the internal resistance of a single cell is out of bounds.

[0149] Optionally, the detection module 83 is specifically adapted to:

[0150] The rate of increase of the state of charge deviation of each battery cell in the first time period is compared with the reference threshold of the first time period to obtain the first comparison result;

[0151] Calculate the first preset calculation result between the growth rate of the state of charge deviation of each battery cell in the second time period and the growth rate of the state of charge deviation in the first time period, and compare the result with the reference threshold of the second time period to obtain the second comparison result;

[0152] Calculate the second preset calculation result between the growth rate of the state of charge deviation of each battery cell in the third time period and the growth rate of the state of charge deviation in the second time period, and compare the result with the reference threshold of the third time period to obtain the third comparison result.

[0153] The internal resistance of each battery cell is compared with the internal resistance reference threshold to obtain the fourth comparison result; wherein, the internal resistance reference threshold is determined based on the average value and standard deviation of the internal resistance of each battery cell.

[0154] Based on at least one of the first comparison result, second comparison result, third comparison result and fourth comparison result, detect whether the state of each battery cell is abnormal.

[0155] Optionally, abnormal states of the battery pack include: bulging state, aging state, and abnormal state in consistency assessment.

[0156] The specific structure and working principle of each of the above modules can be found in the descriptions of the corresponding parts of Method Embodiment 1 and Embodiment 2, and will not be repeated here.

[0157] Example 4

[0158] Figure 9 The diagram illustrates the structure of an electronic device according to Embodiment 4 of the present invention. These specific embodiments do not limit the specific implementation of the electronic device. (Refer to...) Figure 9 The electronic device includes:

[0159] At least one processor 901; a memory 902 communicatively connected to at least one processor; a communication interface 903; and a communication bus 904.

[0160] in:

[0161] The processor 901, memory 902, and communication interface 903 communicate with each other through the communication bus 904.

[0162] The communication interface 903 is used to communicate with other network elements such as clients or other servers.

[0163] The memory 902 stores one or more computer programs 905 that can be executed by at least one processor 901, which enables the at least one processor 901 to perform the corresponding operations as described in the above-described embodiment of the abnormal state detection method for the battery pack.

[0164] Example 5

[0165] Embodiment 5 of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the abnormal state detection method of the battery pack as described above.

[0166] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0167] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable program instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0168] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0169] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0170] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0171] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0172] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0173] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0174] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0175] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in connection with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in connection with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A method of detecting an abnormal state of a battery pack, characterized by, The battery pack comprises multiple individual battery cells, and the method includes the following steps: Obtain each historical charging record contained in the historical charging data of the battery pack; Based on each historical charging record, calculate the state-of-charge deviation of each battery cell during each charging process; Based on the state-of-charge deviation of each individual cell and the time information corresponding to the state-of-charge deviation of each individual cell, the rate of increase of the state-of-charge deviation of each individual cell is calculated; and the internal resistance of each individual cell is calculated. The rate of increase in the state of charge deviation and the internal resistance of each battery cell are compared with a preset reference threshold, and the state of each battery cell is detected as abnormal based on the comparison results. The preset reference thresholds include: multiple time period reference thresholds corresponding to different time periods; wherein, the multiple time period reference thresholds are used to determine whether the rate of increase of the state of charge deviation of a single battery cell increases with the passage of time; Furthermore, the preset reference threshold includes an internal resistance reference threshold; wherein the internal resistance reference threshold is used to determine whether the internal resistance of a single cell is out of bounds.

2. The method of claim 1, wherein, The calculation of the single-cell state-of-charge deviation for each battery cell during each charging process, based on each historical charging record, includes: For each historical charging record, determine the initial state of charge of each battery cell before the start of the current charging process and the final state of charge after the end of the current charging process. The difference between the final state of charge and the initial state of charge of the battery cell is determined as the single cell state of charge difference during the current charging process. Based on the difference in state of charge of each battery cell during this charging process, calculate the average state of charge of each battery cell during this charging process. For each battery cell, the difference between the cell's state of charge difference and the average cell state of charge is defined as the cell state of charge deviation.

3. The method according to claim 1 or 2, characterized in that, The step of calculating the rate of increase of the state of charge deviation for each battery cell based on the individual cell state of charge deviation and the time information corresponding to the individual cell state of charge deviation includes: The time interval between the current charging time and the first charging time for each battery cell is determined as the relative duration of the current charging process for that battery cell. Based on the relative duration of each charging process for each battery cell, the historical charging period corresponding to each charging process for each battery cell is divided into multiple time periods, and the rate of increase of the state of charge deviation for each battery cell in each time period is calculated.

4. The method of claim 3, wherein, The method of dividing the historical charging period of each battery cell corresponding to each charging process into multiple time periods based on the relative duration of each charging process includes: The historical charging period is divided into a first time interval, a second time interval adjacent to the first time interval, a third time interval adjacent to the second time interval, and a fourth time interval adjacent to the third time interval. The first time interval and the second time interval are designated as the first time period, the second time interval and the third time interval are designated as the second time period, and the third time interval and the fourth time interval are designated as the third time period.

5. The method of claim 4, wherein, The multiple time-period reference thresholds include: a first time-period reference threshold, a second time-period reference threshold, and a third time-period reference threshold. The step of comparing the rate of increase in the state-of-charge deviation and the internal resistance of each battery cell with preset reference thresholds, and detecting whether the state of each battery cell is abnormal based on the comparison results, includes: The rate of increase of the state of charge deviation of each battery cell in the first time period is compared with the reference threshold of the first time period to obtain the first comparison result; Calculate a first preset calculation result between the growth rate of the state of charge deviation of each battery cell in the second time period and the growth rate of the state of charge deviation in the first time period, and compare the first preset calculation result with the reference threshold of the second time period to obtain a second comparison result; Calculate a second preset calculation result between the growth rate of the state of charge deviation of each battery cell in the third time period and the growth rate of the state of charge deviation in the second time period, and compare the second preset calculation result with the reference threshold of the third time period to obtain a third comparison result. The internal resistance of each battery cell is compared with the internal resistance reference threshold to obtain a fourth comparison result; wherein the internal resistance reference threshold is determined based on the average value and standard deviation of the internal resistance of each battery cell. Based on at least one of the first comparison result, the second comparison result, the third comparison result, and the fourth comparison result, detect whether the state of each battery cell is abnormal.

6. The method according to claim 1 or 2, characterized in that, The calculation of the internal resistance of each battery cell includes: For each battery cell, calculate the voltage difference and current difference before and after the jump in the last charging current to full charge. The internal resistance of the battery cell is calculated based on the ratio between the voltage difference and the current difference.

7. The method according to claim 1 or 2, characterized in that, The abnormal states of the battery pack include: bulging state, abnormal aging state, and abnormal consistency assessment state; and the historical charging records included in the historical charging data of the battery pack include: From the historical charging data of the battery pack, select historical charging records corresponding to charging processes in which the change in static state of charge before and after charging is not less than a preset change threshold.

8. A battery pack abnormal state detection device, characterized in that, The battery pack includes multiple battery cells, and the device includes: The acquisition module is adapted to acquire each historical charging record contained in the historical charging data of the battery pack; The calculation module is adapted to calculate the state-of-charge deviation of each battery cell in each historical charging record, and to calculate the rate of increase of the state-of-charge deviation of each battery cell based on the state-of-charge deviation and the time information corresponding to the state-of-charge deviation; and to calculate the internal resistance of each battery cell. The detection module is adapted to compare the rate of increase of the state of charge deviation and the internal resistance of each battery cell with a preset reference threshold, and detect whether the state of each battery cell in the battery pack is abnormal based on the comparison results.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, the one or more computer programs being executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, implements the method as described in any one of claims 1-7.

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