A battery foreign matter particle detection method and device, electronic equipment and storage medium
By acquiring battery charging data and calculating the functional relationship between the differential pressure growth rate and SOC data, the problem of low detection efficiency of foreign particles in batteries is solved, achieving efficient detection and saving disassembly costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DR OCTOPUS INTELLIGENT TECH (SHANGHAI) CO LTD
- Filing Date
- 2022-12-05
- Publication Date
- 2026-05-29
AI Technical Summary
Existing technologies have low efficiency in detecting foreign particles in batteries, and the battery disassembly process is cumbersome and costly.
By acquiring battery charging data, including cell voltage data and SOC data, the functional relationship between the differential pressure growth rate and SOC data is calculated to determine the foreign particle detection results and avoid battery disassembly.
It improves the efficiency of foreign particle detection in batteries, saves battery disassembly costs, and can prioritize the disassembly of abnormal batteries with larger foreign particles.
Smart Images
Figure CN115877235B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery anomaly detection technology, and in particular to a method, apparatus, electronic device and storage medium for detecting foreign particles in batteries. Background Technology
[0002] Currently, as one of the core components of new energy vehicles, batteries need to undergo anomaly detection to ensure their safety, such as detecting whether foreign particles are present inside.
[0003] In existing technologies, battery abnormalities are typically analyzed based on data such as cell voltage. Once an abnormality is confirmed, the battery is disassembled to inspect for foreign particles inside. However, the disassembly process is quite cumbersome, resulting in low efficiency in detecting foreign particles. Summary of the Invention
[0004] This application provides a method, apparatus, electronic device, and storage medium for detecting foreign particles in batteries, in order to overcome the shortcomings of existing technologies, such as low efficiency in detecting foreign particles in batteries.
[0005] The first aspect of this application provides a method for detecting foreign particles in a battery, including:
[0006] Acquire the charging data of the battery under test; wherein the charging data includes at least the cell voltage data and SOC data of the battery under test;
[0007] When the charging data indicates that the battery under test has an abnormal risk, the rate of increase of the voltage difference of the battery under test is determined based on the cell voltage data of the battery under test.
[0008] Based on the differential pressure growth rate of the battery under test and the SOC data, the foreign particle detection result of the battery under test is determined.
[0009] Optionally, determining the foreign particle detection result of the battery under test based on the differential pressure growth rate and the SOC data includes:
[0010] Based on the differential pressure growth rate of the battery under test and the SOC data, the foreign particle size value of the battery under test is calculated.
[0011] The foreign particle detection result of the battery under test is determined based on the size value of the foreign particles.
[0012] Optionally, calculating the foreign particle size value of the battery under test based on the differential pressure growth rate and the SOC data includes:
[0013] Obtain foreign particle detection data from the sample battery;
[0014] Based on the foreign particle detection data, the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery was analyzed.
[0015] Based on the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery, a formula for calculating the foreign particle size is constructed.
[0016] Based on the foreign particle size calculation formula, the foreign particle size of the battery under test is calculated according to the differential pressure growth rate and the SOC data.
[0017] Optional, also includes:
[0018] According to the order of charging time, the charging data of the battery under test is divided into the charging information corresponding to each charging session.
[0019] Based on the charging status change information of the battery under test represented by the charging information corresponding to each charging, it is determined whether the battery under test has any abnormal risks.
[0020] Optionally, determining whether the battery under test has any abnormal risks based on the charging state change information of the battery under test represented by the charging information corresponding to each charging session includes:
[0021] For any of the current charging information, based on the current charging information, determine the SOC value of the battery under test when the cell voltage difference of the battery under test reaches a preset threshold during this charging;
[0022] Based on the SOC value corresponding to each time the cell voltage difference of the battery under test reaches the preset threshold, the charging state change information of the battery under test is determined;
[0023] When the charging state change information of the battery under test indicates that the rate of increase in the differential voltage of the battery cell is increasing, it is determined that the battery under test has an abnormal risk.
[0024] Optionally, determining the state-of-charge (SOC) change information of the battery under test based on the SOC value corresponding to each time the cell voltage difference of the battery under test reaches the preset threshold includes:
[0025] The SOC values corresponding to the cell voltage difference between two consecutive charges of the battery under test are compared to the preset threshold to obtain the corresponding SOC value comparison results;
[0026] Based on the comparison results of the SOC values corresponding to each two adjacent charges of the battery under test, the charging state change information of the battery under test is determined.
[0027] Optionally, acquiring the charging data of the battery under test includes:
[0028] Acquire the status monitoring data of the battery under test;
[0029] The status monitoring data is cleaned to remove invalid parameters;
[0030] From the cleaned status monitoring data, data on charging scenarios are filtered out.
[0031] Data with a SOC value lower than a preset minimum SOC value in the charging scenario data are removed to obtain the charging data of the battery under test.
[0032] A second aspect of this application provides a battery foreign particle detection device, comprising:
[0033] An acquisition module is used to acquire charging data of the battery under test; wherein, the charging data includes at least the cell voltage data and SOC data of the battery under test;
[0034] The determination module is used to determine the rate of increase of the voltage difference of the battery under test based on the cell voltage data of the battery under test when the charging data indicates that the battery under test has an abnormal risk.
[0035] The detection module is used to determine the foreign particle detection result of the battery under test based on the differential pressure growth rate and the SOC data.
[0036] Optionally, the detection module is specifically used for:
[0037] Based on the differential pressure growth rate of the battery under test and the SOC data, the foreign particle size value of the battery under test is calculated.
[0038] The foreign particle detection result of the battery under test is determined based on the size value of the foreign particles.
[0039] Optionally, the detection module is specifically used for:
[0040] Obtain foreign particle detection data from the sample battery;
[0041] Based on the foreign particle detection data, the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery was analyzed.
[0042] Based on the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery, a formula for calculating the foreign particle size is constructed.
[0043] Based on the foreign particle size calculation formula, the foreign particle size of the battery under test is calculated according to the differential pressure growth rate and the SOC data.
[0044] Optionally, the determining module is further configured to:
[0045] According to the order of charging time, the charging data of the battery under test is divided into the charging information corresponding to each charging session.
[0046] Based on the charging status change information of the battery under test represented by the charging information corresponding to each charging, it is determined whether the battery under test has any abnormal risks.
[0047] Optionally, the determining module is specifically used for:
[0048] For any of the current charging information, based on the current charging information, determine the SOC value of the battery under test when the cell voltage difference of the battery under test reaches a preset threshold during this charging;
[0049] Based on the SOC value corresponding to each time the cell voltage difference of the battery under test reaches the preset threshold, the charging state change information of the battery under test is determined;
[0050] When the charging state change information of the battery under test indicates that the rate of increase in the differential voltage of the battery cell is increasing, it is determined that the battery under test has an abnormal risk.
[0051] Optionally, the determining module is specifically used for:
[0052] The SOC values corresponding to the cell voltage difference between two consecutive charges of the battery under test are compared to the preset threshold to obtain the corresponding SOC value comparison results;
[0053] Based on the comparison results of the SOC values corresponding to each two adjacent charges of the battery under test, the charging state change information of the battery under test is determined.
[0054] Optionally, the acquisition module is specifically used for:
[0055] Acquire the status monitoring data of the battery under test;
[0056] The status monitoring data is cleaned to remove invalid parameters;
[0057] From the cleaned status monitoring data, data on charging scenarios are filtered out.
[0058] Data with a SOC value lower than a preset minimum SOC value in the charging scenario data are removed to obtain the charging data of the battery under test.
[0059] A third aspect of this application provides an electronic device, comprising: at least one processor and a memory;
[0060] The memory stores computer-executed instructions;
[0061] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method described in the first aspect above and various possible designs of the first aspect.
[0062] The fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method described in the first aspect above and various possible designs of the first aspect.
[0063] The technical solution of this application has the following advantages:
[0064] This application provides a method, apparatus, electronic device, and storage medium for detecting foreign particles in batteries. The method includes: acquiring charging data of a battery under test; wherein the charging data includes at least cell voltage data and SOC data of the battery under test; when the charging data indicates that the battery under test has an abnormal risk, determining the voltage difference growth rate of the battery under test based on the cell voltage data; and determining the foreign particle detection result of the battery under test based on the voltage difference growth rate and SOC data. The method provided above, by determining the foreign particle detection result of the battery under test based on the voltage difference growth rate and SOC data when an abnormal risk is determined, eliminates the need for battery disassembly and analysis, thus saving battery disassembly costs and improving the efficiency of foreign particle detection in batteries. Attached Figure Description
[0065] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.
[0066] Figure 1 This is a schematic diagram of the battery foreign particle detection system based on the embodiments of this application;
[0067] Figure 2 This is a schematic flowchart of the battery foreign particle detection method provided in the embodiments of this application;
[0068] Figure 3 This is a schematic diagram of the structure of the battery foreign particle detection device provided in the embodiments of this application;
[0069] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.
[0070] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the present disclosure in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation
[0071] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0072] Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. In the following descriptions of embodiments, "a plurality of" means two or more, unless otherwise explicitly defined.
[0073] In existing technologies, battery anomalies are typically analyzed based on data such as cell voltage. Once an anomaly is confirmed, the battery is disassembled to inspect for foreign particles inside. However, because vehicle batteries consist of numerous cells, the cell disassembly process is cumbersome, resulting in low efficiency in detecting foreign particles. Furthermore, there is currently no good match between abnormal battery data and disassembly results, leading to high disassembly costs and wasted resources.
[0074] To address the aforementioned issues, the battery foreign particle detection method, apparatus, electronic device, and storage medium provided in this application acquire charging data of the battery under test. This charging data includes at least the cell voltage data and SOC data of the battery under test. When the charging data indicates an abnormal risk in the battery under test, the voltage difference growth rate of each cell is determined based on the cell voltage data. Based on the voltage difference growth rate and SOC data of each cell, target abnormal cells containing foreign particles are screened in the battery under test to obtain the foreign particle detection result. The method provided above determines the foreign particle detection result of the battery under test based on the voltage difference growth rate and SOC data when an abnormal risk is identified, without requiring battery disassembly and analysis. This not only saves battery disassembly costs but also improves the efficiency of foreign particle detection in batteries.
[0075] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.
[0076] First, the structure of the battery foreign particle detection system on which this application is based will be described:
[0077] The battery foreign particle detection method, apparatus, electronic device, and storage medium provided in this application are applicable to the detection of foreign particles in multi-cell batteries, such as vehicle batteries. Figure 1 The diagram shown is a structural schematic of the battery foreign particle detection system based on an embodiment of this application. It mainly includes a battery under test, a data acquisition device, and a battery foreign particle detection device. Specifically, the data acquisition device can collect charging data of the battery under test, and then send the charging data to the battery foreign particle detection device, which then performs foreign particle detection on the battery under test based on the obtained data.
[0078] This application provides a method for detecting foreign particles in batteries, used to detect foreign particles in multi-cell batteries such as vehicle batteries. The execution subject of this application embodiment is an electronic device, such as a server, desktop computer, laptop computer, tablet computer, and other electronic devices that can be used to analyze battery charging data.
[0079] like Figure 2 The diagram shown is a flowchart illustrating a battery foreign particle detection method provided in an embodiment of this application. The method includes:
[0080] Step 201: Obtain the charging data of the battery under test.
[0081] The charging data includes at least the cell voltage data and SOC data of the battery under test.
[0082] It should be noted that the environmental factors during the dynamic operation of a battery are complex and can easily cause fluctuations in cell voltage. However, the cell voltage is relatively stable during charging. Therefore, the charging data of the battery under test can be obtained to ensure the accuracy of the final test results.
[0083] Specifically, in one embodiment, state monitoring data of the battery under test can be acquired; the state monitoring data can be cleaned to remove invalid parameters; charging scenario data can be filtered from the cleaned state monitoring data; data with a SOC value lower than a preset minimum SOC value in the charging scenario data can be removed to obtain the charging data of the battery under test.
[0084] The invalid parameters that were removed mainly include the initial and default values of certain indicators of the battery under test, as these data cannot reflect the actual state information of the battery under test.
[0085] Specifically, after removing invalid parameters from the state monitoring data of the battery under test, driving scenario data (discharge scenario data) can be further filtered out to obtain charging scenario data. Since the battery may have a large voltage difference due to the capacity difference between different cells at low SOC, in order to further ensure the accuracy of the test results, data with an SOC value lower than the preset minimum SOC value can be removed, for example, data with an SOC value <10%. The remaining data is then used as the charging data of the battery under test.
[0086] Step 202: When the charging data indicates that the battery under test has an abnormal risk, determine the voltage difference growth rate of the battery under test based on the cell voltage data of the battery under test.
[0087] It should be noted that when an abnormality occurs inside the battery, data such as the battery cell voltage will change in a certain pattern. Therefore, the battery under test can be judged to have any abnormal risks based on the pattern of cell voltage change characterized by the charging data.
[0088] Specifically, the cell voltage difference of the battery under test can be determined at different times based on the cell voltage data of the battery under test. The cell voltage difference specifically refers to the difference ΔI between the highest cell voltage Imax and the lowest cell voltage Imin. Then, based on the change of the cell voltage difference, the rate of increase of the voltage difference of the battery under test can be calculated. For specific calculation methods, please refer to the prior art. This application does not limit the specific calculation method.
[0089] Step 203: Determine the foreign particle detection results of the battery under test based on the differential pressure growth rate and SOC data.
[0090] The foreign particle detection results include at least the size value of the foreign particles.
[0091] It should be noted that experimental studies have found a strong correlation between the battery's differential pressure growth rate and state of charge (SOC) and the size of foreign particles in the battery.
[0092] The table below shows the battery data for batteries containing foreign particles:
[0093] Pressure difference growth rate SOC Foreign object particle size 0.39mV / day 85％ 0.31mm 0.29mV / day 85％ 2.67mm 0.55mV / day 85％ 0.58mm … … … 3.7mV / day 60％ 3mm 0.31mV / day 80％ 0.21mm 0.61mV / day 70％ 0.5mm 1.39mV / day 75％ 1.27mm 0.89mV / day 85％ 0.92mm
[0094] Furthermore, Pearson correlation calculations were performed on the battery data containing foreign particles. The correlation coefficient between the foreign particle size and the State of Charge (SOC) was -0.744821, and the correlation coefficient between the foreign particle size and the rate of change of pressure (Vrate) was 0.986390. Specifically, a correlation coefficient of -1 indicates a completely negative correlation; a correlation coefficient of 0 indicates no linear correlation; and a correlation coefficient of +1 indicates a completely positive correlation.
[0095] Specifically, in one embodiment, the foreign particle size of the battery under test can be calculated based on the differential pressure growth rate and SOC data; and the foreign particle detection result of the battery under test can be determined based on the foreign particle size.
[0096] It should be noted that the foreign particle detection results include not only the size value of the foreign particles, but also information on abnormal battery cells. Abnormal battery cells are generally the highest voltage battery cells and / or the lowest voltage battery cells.
[0097] Specifically, in order to further improve the accuracy of the foreign particle size calculation results, the method provided in this application embodiment uses two indicators that are strongly correlated with the foreign particle size to calculate the foreign particle size of the battery under test.
[0098] Specifically, in one embodiment, foreign particle detection data of the sample battery can be acquired; based on the foreign particle detection data, the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery can be analyzed; based on the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery, a foreign particle size calculation formula can be constructed; based on the foreign particle size calculation formula, the foreign particle size of each battery under test can be calculated according to the differential pressure growth rate and SOC data of the battery under test.
[0099] Specifically, we can first construct an initial formula for calculating the size of foreign particles:
[0100] Y = k1 * Vrat + k2 * SOC
[0101] Furthermore, based on the battery data containing foreign particles mentioned above, the initial formula for calculating the size of foreign particles can be fitted and calculated. Let the initial values of k1 and k2 be 0.1, and the learning rate be 0.01, resulting in the following correlation coefficient error table:
[0102] <![CDATA[k1]]> <![CDATA[k2]]> error 0.01 0.01 7.985 0.08 0.01 7.551 0.18 0.08 6.35 0.72 0.09 2.919 0.74 0.19 1.965 0.74 0.29 1.135 0.74 0.31 0.969 … … … 0.61 0.41 0.945 0.45 0.55 0.775 0.13 0.80 0.155
[0103] The error of the relationship coefficient can be calculated using the following formula:
[0104]
[0105] Where m represents the number of sample batteries, y i This represents the actual foreign particle size value of the i-th sample battery.
[0106] Furthermore, through fitting calculations, the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery was determined. The minimum error was found when k1 = 0.8 and k2 = 0.13. Therefore, the formula for calculating the foreign particle size can be determined as follows:
[0107] Y = 0.8 * Vrat + 0.13 * SOC
[0108] Based on the above embodiments, in order to improve the accuracy of the abnormal risk assessment results of the battery under test, as an implementable approach, in one embodiment, the method further includes:
[0109] Step 301: According to the order of charging time, divide the charging data of the battery under test into the charging information corresponding to each charging session.
[0110] Step 302: Based on the charging status change information of the battery under test represented by the charging information corresponding to each charging session, determine whether there is any abnormal risk in the battery under test.
[0111] Specifically, the charging data of the battery under test is the monitoring data generated by the battery under test through multiple charging. In order to accurately analyze the charging state change information of the battery under test, the state information of the battery under test can be analyzed one by one during each charging to obtain the charging state change information of the battery under test.
[0112] Specifically, in one embodiment, for any given charging information, the SOC value of the battery under test can be determined when the cell voltage difference of the battery under test reaches a preset threshold during this charging. Based on the SOC value corresponding to each time the cell voltage difference of the battery under test reaches the preset threshold, the charging state change information of the battery under test can be determined. When the charging state change information of the battery under test indicates that the rate of increase of the cell voltage difference of the battery under test is increasing, it is determined that the battery under test has an abnormal risk.
[0113] The preset threshold can be the cell voltage difference of the battery under test at any time during the nth charging process.
[0114] Specifically, the State of Charge (SOC) value corresponding to when the cell voltage difference of the battery under test reaches a preset threshold during each charge can be determined. Then, based on the successive changes in the SOC value, the charging state change information of the battery under test can be determined. If the charging state change information indicates that the rate of increase in the voltage difference of the battery cells is increasing, it is determined that the battery under test has an abnormal risk.
[0115] Specifically, in one embodiment, the SOC values corresponding to the cell voltage difference between two adjacent charging cycles of the battery under test can be compared to obtain the corresponding SOC value comparison results; based on the SOC value comparison results corresponding to each two adjacent charging cycles of the battery under test, the charging state change information of the battery under test can be determined.
[0116] For example, let the cell voltage difference of the battery under test during the nth charge be ΔI1, and the corresponding SOC value be SOCn. The SOC value of the (n+1)th charge reaching ΔI1 is SOCn+1. When SOCn+1 < SOCn, it is recorded as 1, and when SOCn+1 ≥ SOCn, it is recorded as 0. When the SOC value of the (n+2)th charge reaching ΔI1 is SOCn+2, it is recorded as 1 when SOCn+2 < SOCn+1, and when SOCn+2 ≥ SOCn+1, it is recorded as 0, and so on. The difference between SOCn and SOCn+x is denoted as ΔSOC. This involves calculating the change in SOC of the battery under test after a number of charging cycles (x times), and counting the percentage of times 1 occurs between the nth and n+xth charging cycles in the total number of charging cycles x. In other words, it represents the percentage of times the SOC value decreases in the total number of charging cycles. When ΔSOC is greater than 10% and the percentage of 1 exceeds 1 / 2, it indicates that the rate of increase in the voltage difference of the battery cell under test is increasing, thus indicating that the battery under test has an abnormal risk.
[0117] The battery foreign particle detection method provided in this application acquires charging data of the battery under test. The charging data includes at least cell voltage data and SOC data. When the charging data indicates an abnormal risk in the battery under test, the differential voltage growth rate of the battery under test is determined based on the cell voltage data. The foreign particle detection result of the battery under test is then determined based on the differential voltage growth rate and SOC data. This method, by determining the foreign particle detection result of the battery under test based on its differential voltage growth rate and SOC data when an abnormal risk is identified, eliminates the need for battery disassembly and analysis. This not only saves on battery disassembly costs but also improves the efficiency of foreign particle detection. Furthermore, when disassembly and analysis of abnormal batteries are required, the disassembly priority of abnormal batteries can be prioritized based on the foreign particle detection results obtained by this method, allowing for the priority disassembly of batteries with larger foreign particles.
[0118] This application provides a battery foreign object particle detection device for performing the battery foreign object particle detection method provided in the above embodiments.
[0119] like Figure 3 The diagram shown is a structural schematic of a battery foreign particle detection device provided in an embodiment of this application. The battery foreign particle detection device 30 includes: an acquisition module 301, a determination module 302, and a detection module 303.
[0120] The system includes an acquisition module for acquiring charging data of the battery under test, wherein the charging data includes at least the cell voltage data and SOC data of the battery under test; a determination module for determining the differential voltage growth rate of the battery under test based on the cell voltage data when the charging data indicates that the battery under test has an abnormal risk; and a detection module for determining the foreign particle detection result of the battery under test based on the differential voltage growth rate and SOC data.
[0121] Specifically, in one embodiment, the detection module is specifically used for:
[0122] Based on the differential pressure growth rate and SOC data of the battery under test, the foreign particle size value of the battery under test is calculated.
[0123] The foreign particle detection result of the battery under test is determined based on the size value of the foreign particles.
[0124] Specifically, in one embodiment, the detection module is specifically used for:
[0125] Obtain foreign particle detection data from the sample battery;
[0126] Based on the foreign particle detection data, the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery was analyzed.
[0127] Based on the functional relationship between foreign particle size, differential pressure growth rate, and SOC data of the sample battery, a formula for calculating foreign particle size is constructed.
[0128] Based on the formula for calculating foreign particle size, the foreign particle size of the battery under test is calculated according to the differential pressure growth rate and SOC data.
[0129] Specifically, in one embodiment, the determining module is further configured to:
[0130] According to the order of charging time, the charging data of the battery under test is divided into the charging information corresponding to each charging session.
[0131] Based on the charging status change information of the battery under test represented by the charging information corresponding to each charging, it is determined whether there is any abnormal risk in the battery under test.
[0132] Specifically, in one embodiment, the determining module is specifically used for:
[0133] For any given charging information, determine the SOC value of the battery under test when the cell voltage difference reaches a preset threshold during the current charging.
[0134] Based on the SOC value corresponding to each time the cell voltage difference of the battery under test reaches a preset threshold, the charging state change information of the battery under test is determined.
[0135] When the change in the state of charge of the battery under test indicates that the rate of increase in the differential voltage between the battery cells is increasing, it is determined that the battery under test has an abnormal risk.
[0136] Specifically, in one embodiment, the determining module is specifically used for:
[0137] The SOC values corresponding to the cell voltage difference between two consecutive charging cycles of the battery under test are compared to obtain the corresponding SOC value comparison results.
[0138] Based on the comparison of the SOC values corresponding to each two adjacent charges of the battery under test, the charging state change information of the battery under test is determined.
[0139] Specifically, in one embodiment, the acquisition module is specifically used for:
[0140] Acquire the status monitoring data of the battery under test;
[0141] Perform data cleaning on the condition monitoring data to remove invalid parameters;
[0142] From the cleaned status monitoring data, data on charging scenarios are filtered out.
[0143] Data with a SOC value lower than the preset minimum SOC value in the charging scenario data is removed to obtain the charging data of the battery under test.
[0144] Regarding the battery foreign particle detection device in this embodiment, the specific way in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated here.
[0145] The battery foreign object particle detection device provided in this application embodiment is used to execute the battery foreign object particle detection method provided in the above embodiment. Its implementation method and principle are the same, and will not be described again.
[0146] This application provides an electronic device for performing the battery foreign particle detection method provided in the above embodiments.
[0147] like Figure 4 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application. The electronic device 40 includes at least one processor 41 and a memory 42.
[0148] The memory stores computer-executable instructions; at least one processor executes the computer-executable instructions stored in the memory, causing the at least one processor to perform the battery foreign particle detection method provided in the above embodiment.
[0149] This application provides an electronic device for executing the battery foreign particle detection method provided in the above embodiments. Its implementation method and principle are the same, and will not be described again.
[0150] This application provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the battery foreign particle detection method provided in any of the above embodiments.
[0151] The storage medium containing computer-executable instructions in the embodiments of this application can be used to store the computer-executable instructions for the battery foreign particle detection method provided in the foregoing embodiments. Its implementation method and principle are the same, and will not be described again.
[0152] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0153] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0154] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.
[0155] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0156] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, 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 or all of the technical features therein. Such 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 this application.
Claims
1. A method for detecting foreign particles in a battery, characterized in that, include: Acquire the charging data of the battery under test; wherein the charging data includes at least the cell voltage data and SOC data of the battery under test; When the charging data indicates that the battery under test has an abnormal risk, the rate of increase of the voltage difference of the battery under test is determined based on the cell voltage data of the battery under test. Based on the differential pressure growth rate of the battery under test and the SOC data, the foreign particle detection result of the battery under test is determined; The step of determining the foreign particle detection result of the battery under test based on the differential pressure growth rate and the SOC data includes: Based on the differential pressure growth rate of the battery under test and the SOC data, the foreign particle size value of the battery under test is calculated. The foreign particle detection result of the battery under test is determined based on the size value of the foreign particles in the battery under test. The step of calculating the foreign particle size value of the battery under test based on the differential pressure growth rate and the SOC data includes: Obtain foreign particle detection data from the sample battery; Based on the foreign particle detection data, the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery was analyzed. Based on the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery, a formula for calculating the foreign particle size is constructed. Based on the foreign particle size calculation formula, the foreign particle size of the battery under test is calculated according to the differential pressure growth rate and the SOC data.
2. The method according to claim 1, characterized in that, Also includes: According to the order of charging time, the charging data of the battery under test is divided into the charging information corresponding to each charging session. Based on the charging status change information of the battery under test represented by the charging information corresponding to each charging, it is determined whether the battery under test has any abnormal risks.
3. The method according to claim 2, characterized in that, The step of determining whether the battery under test has any abnormal risks based on the charging state change information of the battery under test represented by the charging information corresponding to each charging session includes: For any of the current charging information, based on the current charging information, determine the SOC value of the battery under test when the cell voltage difference of the battery under test reaches a preset threshold during this charging; Based on the SOC value corresponding to each time the cell voltage difference of the battery under test reaches the preset threshold, the charging state change information of the battery under test is determined; When the charging state change information of the battery under test indicates that the rate of increase in the differential voltage of the battery cell is increasing, it is determined that the battery under test has an abnormal risk.
4. The method according to claim 3, characterized in that, The step of determining the state-of-charge (SOC) change information of the battery under test based on the SOC value corresponding to each time the cell voltage difference of the battery under test reaches the preset threshold includes: The SOC values corresponding to the cell voltage difference between two consecutive charges of the battery under test are compared to the preset threshold to obtain the corresponding SOC value comparison results; Based on the comparison results of the SOC values corresponding to each two adjacent charges of the battery under test, the charging state change information of the battery under test is determined.
5. The method according to claim 1, characterized in that, The acquisition of charging data for the battery under test includes: Acquire the status monitoring data of the battery under test; The status monitoring data is cleaned to remove invalid parameters; From the cleaned status monitoring data, data on charging scenarios are filtered out. Data with a SOC value lower than a preset minimum SOC value in the charging scenario data are removed to obtain the charging data of the battery under test.
6. A battery foreign particle detection device, characterized in that, include: An acquisition module is used to acquire charging data of the battery under test; wherein, the charging data includes at least the cell voltage data and SOC data of the battery under test; The determination module is used to determine the rate of increase of the voltage difference of the battery under test based on the cell voltage data of the battery under test when the charging data indicates that the battery under test has an abnormal risk. The detection module is used to determine the foreign particle detection result of the battery under test based on the differential pressure growth rate and the SOC data. The detection module is specifically used for: Based on the differential pressure growth rate of the battery under test and the SOC data, the foreign particle size value of the battery under test is calculated. The foreign particle detection result of the battery under test is determined based on the size value of the foreign particles in the battery under test. The detection module is specifically used for: Obtain foreign particle detection data from the sample battery; Based on the foreign particle detection data, the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery was analyzed. Based on the functional relationship between the foreign particle size, differential pressure growth rate, and SOC data of the sample battery, a formula for calculating the foreign particle size is constructed. Based on the foreign particle size calculation formula, the foreign particle size of the battery under test is calculated according to the differential pressure growth rate and the SOC data.
7. An electronic device, characterized in that, include: At least one processor and memory; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method as described in any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the method as described in any one of claims 1 to 5.