Battery testing methods and related equipment

Abstract

This application provides a battery detection method and related equipment. The method includes: acquiring the state of charge (SOC) of multiple cells in a battery; calculating the SOC offset of each cell based on the SOC of each cell; calculating the SOC offset variation between the cells based on the SOC offset of each cell; extracting the divergence features between the cells based on the SOC variation; and detecting whether the battery is abnormal based on the divergence features between the cells, which can effectively capture the situation of overall cell abnormality.

Description

Technical Field

[0001] This application relates to the field of batteries, and more particularly to a battery testing method and related equipment. Background Technology

[0002] Batteries are one of the core components of electrical devices, responsible for providing power and storing energy. Battery performance significantly impacts the range, safety, and lifespan of these devices. Given the critical importance of batteries, it is necessary to detect battery anomalies to reduce the probability of device malfunctions. While some battery detection solutions exist, they are not always effective at detecting battery abnormalities.

[0003] Therefore, there is an urgent need for a battery detection solution that can effectively detect battery abnormalities in order to reduce the probability of failure. Summary of the Invention

[0004] This application provides a battery testing method and related equipment that can effectively detect battery abnormalities.

[0005] In a first aspect, a battery detection method is provided, comprising: acquiring the state of charge (SOC) corresponding to multiple cells in a battery; calculating a SOC offset for each cell based on the SOC of each cell, wherein the SOC offset characterizes the degree of offset of the SOC of the cell relative to the SOC of the multiple cells; calculating a variation degree of SOC offset between the cells based on the SOC offset of each cell, wherein the variation degree of SOC offset characterizes the degree of variation of the SOC offset of different cells; extracting divergence features between the cells based on the variation degree of SOC offset between the cells; and detecting whether the battery is abnormal based on the divergence features between the cells.

[0006] The technical solution of this application embodiment is based on the phenomenon that the state of charge (SOC) of an abnormal cell may deviate significantly from that of other normally functioning cells. By calculating the SOC offset of each cell through the SOC of multiple cells in the battery, the SOC offset variation between cells is calculated. Since the SOC offset variation between cells can effectively reflect the inconsistency between cells, the divergence characteristics between cells are extracted based on the SOC offset variation. Based on the divergence characteristics, the abnormal situation of overall cell self-discharge dispersion can be detected, which can effectively capture the abnormal situation of the overall cell. This effectively avoids the problem that some related technologies cannot effectively capture battery abnormalities by comparing the open-circuit voltage deviation and capacity deviation rate between the tested cell and the reference cell.

[0007] In one possible implementation, the method further includes: extracting divergence features between the battery cells based on the degree of change in the state of charge offset between the battery cells, including: calculating the degree of divergence and / or the divergence rate between the battery cells based on the degree of change in the state of charge offset between the battery cells.

[0008] This technical solution effectively captures the inconsistency of the state of charge between battery cells by measuring the degree of divergence. Therefore, using the degree of divergence to detect battery abnormalities can effectively avoid the situation where most battery cells are normal while a few cells exhibit discrete self-discharge abnormalities. Furthermore, since the divergence rate effectively captures the trend of the inconsistency of the state of charge between battery cells over time, using the divergence rate to detect battery abnormalities can promptly identify potential problems.

[0009] In one possible implementation, the method further includes: calculating the state-of-charge offset variation between the battery cells based on the state-of-charge offset corresponding to each of the battery cells, comprising: sorting the state-of-charge offsets corresponding to multiple battery cells at any given time to obtain a sorting result; calculating the difference in state-of-charge offsets between adjacent battery cells based on the sorting result, wherein the difference is used to represent the state-of-charge offset variation. Extracting the divergence characteristics between the battery cells based on the state-of-charge offset variation includes: sorting the state-of-charge offset variation between the battery cells, and taking the state-of-charge offset variation ranking first in the sort as the divergence degree between the battery cells at any given time.

[0010] The technical solution of this implementation sorts the degree of change of state of charge between adjacent cells and takes the degree of change of state of charge at the first preset number as the degree of divergence. This can eliminate the influence of extreme values, cover most cells, and effectively capture the inconsistency of state of charge between cells.

[0011] In one possible implementation, the method further includes: for the degree of divergence between the cells at any given time, using a corresponding divergence degree dataset and a sliding window median filtering strategy to remove noise from the divergence degree, wherein the divergence degree dataset includes: the degree of divergence between the cells at any given time, and the degree of divergence between the cells at other times before and after the given time.

[0012] The technical solution of this implementation method can effectively remove noise in the divergence by using a sliding window median filtering strategy.

[0013] In one possible implementation, the method further includes: calculating the degree of divergence and / or the divergence rate between the battery cells based on the degree of change in the state of charge offset between the battery cells, including: calculating the degree of divergence between the battery cells at multiple time points based on the degree of change in the state of charge offset between the battery cells at multiple time points; and fitting the slope of the degree of divergence between the battery cells at multiple time points to obtain the divergence rate between the battery cells.

[0014] The technical solution of this implementation method obtains the divergence rate by fitting the slope of the divergence between cells at multiple time points, which can detect potential anomalies as early as possible and improve detection efficiency.

[0015] In one possible implementation, the method further includes: detecting whether the battery is abnormal based on the divergence characteristics between the cells, including: comparing the divergence characteristics between the cells with a preset divergence characteristic threshold to obtain a divergence anomaly detection result.

[0016] The technical solution of this implementation method can quickly and accurately obtain the detection results of divergence anomalies by comparing the divergence characteristics between battery cells with a preset divergence characteristic threshold, thereby improving detection efficiency.

[0017] In one possible implementation, the method further includes: analyzing divergence feature data in a historical sample set to obtain the preset divergence feature threshold, wherein the historical sample set includes: divergence feature data among multiple cells of batteries known to have abnormal or normal divergence features.

[0018] The technical solution of this implementation method can analyze the historical sample set to determine the threshold for evaluating whether the divergence characteristics between battery cells are normal or abnormal, thus providing an accurate judgment standard for battery testing.

[0019] In one possible implementation, the method further includes: identifying an anomaly handling strategy corresponding to the abnormal detection result of the battery; and executing the anomaly handling strategy.

[0020] The technical solution of this implementation can automatically execute the abnormal handling strategy after identifying the abnormal detection result, which can effectively improve the reliability of the battery system and reduce the probability of failure.

[0021] In one possible implementation, the method further includes: obtaining the state of charge (SOC) of multiple cells in the battery, including: obtaining the correspondence between the open-circuit voltage and the SOC of the multiple cells in the battery under static conditions; and calculating a list of the SOC of the multiple cells in the battery based on the correspondence.

[0022] The technical solution of this implementation method can eliminate external interference and obtain a more accurate state of charge by extracting the correspondence between the open circuit voltage and the state of charge of the battery under static conditions.

[0023] In one possible implementation, the method further includes: calculating the state of charge (SCC) offset for each of the battery cells based on the SCC of each battery cell, including: determining the maximum SCC, minimum SCC, and sum of the SCCs of the multiple battery cells at any given time for the SCC of the multiple battery cells at any given time; calculating the average SCC of the multiple battery cells at any given time based on the sum of the SCCs of the multiple battery cells, the maximum SCC, the minimum SCC, and the number of the multiple battery cells; and calculating the SCC offset of any battery cell at any given time based on the SCC of any battery cell at any given time and the average SCC.

[0024] The technical solution of this implementation method evaluates the state of charge deviation of each cell by calculating the difference between the SOC value of each cell and the average value after removing the maximum and minimum values. This can effectively eliminate the influence of extreme values ​​and make the evaluation more accurate.

[0025] In one possible implementation, the method further includes: when an abnormal self-discharge of the battery is detected based on the divergence characteristics between the cells, outputting the self-discharge information of the battery through a data platform.

[0026] The technical solution of this implementation method, if a divergent self-discharge anomaly is detected, outputs the self-discharge information through the data platform, thereby facilitating relevant personnel to accurately locate the abnormal electrical device.

[0027] Secondly, a battery detection device is provided, comprising: a state of charge (SOC) acquisition module for acquiring the SOC corresponding to multiple cells in a battery; a SOC offset calculation module for calculating the SOC offset for each cell based on the SOC of each cell, wherein the SOC offset characterizes the degree of offset of the SOC of the cell relative to the SOC of the multiple cells; a SOC offset variation calculation module for calculating the SOC offset variation among the cells based on the SOC offset of each cell, wherein the SOC offset variation characterizes the degree of variation of the SOC offset among different cells; a divergence feature extraction module for extracting divergence features among the cells based on the SOC offset variation among the cells; and an anomaly detection module for detecting whether the battery is abnormal based on the divergence features among the cells.

[0028] The technical solution of this application embodiment is based on the phenomenon that the state of charge (SOC) of an abnormal cell may deviate significantly from that of other normally functioning cells. By calculating the SOC offset of each cell through the SOC of multiple cells in the battery, the SOC offset variation between cells is calculated. Since the SOC offset variation between cells can effectively reflect the inconsistency between cells, the divergence characteristics between cells are extracted based on the SOC offset variation. Based on the divergence characteristics, the abnormal situation of overall cell self-discharge dispersion can be detected, which can effectively capture the abnormal situation of the overall cell. This effectively avoids the problem that some related technologies cannot effectively capture battery abnormalities by comparing the open-circuit voltage deviation and capacity deviation rate between the tested cell and the reference cell.

[0029] In one possible implementation, the divergence feature extraction module is used to calculate the degree of divergence and / or the divergence rate between the battery cells based on the degree of change in the state of charge offset between the battery cells.

[0030] This technical solution effectively captures the inconsistency of the state of charge between battery cells by measuring the degree of divergence. Therefore, using the degree of divergence to detect battery abnormalities can effectively avoid the situation where most battery cells are normal while a few cells exhibit discrete self-discharge abnormalities. Furthermore, since the divergence rate effectively captures the trend of the inconsistency of the state of charge between battery cells over time, using the divergence rate to detect battery abnormalities can promptly identify potential problems.

[0031] In one possible implementation, the charge state offset change calculation module includes:

[0032] The offset sorting submodule is used to sort the state of charge offsets of multiple cells at any given time to obtain the sorting result.

[0033] The difference calculation submodule is used to calculate the difference in the state of charge offset between adjacent cells based on the sorting result. The difference is used to represent the degree of change in the state of charge offset.

[0034] The divergence feature extraction module is used to sort the degree of change of state of charge offset between the cells, and take the degree of change of state of charge offset that ranks first in the preset order as the degree of divergence between the cells at any given time.

[0035] The technical solution of this implementation sorts the degree of change of state of charge between adjacent cells and takes the degree of change of state of charge at the first preset number as the degree of divergence. This can eliminate the influence of extreme values, cover most cells, and effectively capture the inconsistency of state of charge between cells.

[0036] In one possible implementation, the device further includes: a noise reduction module, used to remove noise from the divergence degree between the cells at any given time by using a corresponding divergence degree dataset and a sliding window median filtering strategy, wherein the divergence degree dataset includes: the divergence degree between the cells at any given time, and the divergence degree between the cells at other times before and after the given time.

[0037] The technical solution of this implementation method can effectively remove noise in the divergence by using a sliding window median filtering strategy.

[0038] In one possible implementation, the divergent feature extraction module includes:

[0039] The divergence degree extraction submodule is used to calculate the divergence degree between the cells at multiple time points based on the change in state of charge offset between the cells at multiple time points.

[0040] The divergence rate extraction submodule is used to fit the slope of the divergence degree between the cells at multiple time points to obtain the divergence rate between the cells.

[0041] The technical solution of this implementation method obtains the divergence rate by fitting the slope of the divergence between cells at multiple time points, which can detect potential anomalies as early as possible and improve detection efficiency.

[0042] In one possible implementation, the anomaly detection module is used to compare the divergence characteristics between the battery cells with a preset divergence characteristic threshold to obtain a divergence anomaly detection result.

[0043] The technical solution of this implementation method can quickly and accurately obtain the detection results of divergence anomalies by comparing the divergence characteristics between battery cells with a preset divergence characteristic threshold, thereby improving detection efficiency.

[0044] In one possible implementation, the device further includes: a threshold analysis module, used to analyze divergent feature data in a historical sample set to obtain the preset divergent feature threshold, wherein the historical sample set includes: divergent feature data among multiple cells of batteries known to have abnormal or normal divergent features.

[0045] The technical solution of this implementation method can analyze the historical sample set to determine the threshold for evaluating whether the divergence characteristics between battery cells are normal or abnormal, thus providing an accurate judgment standard for battery testing.

[0046] In one possible implementation, the device further includes:

[0047] The handling strategy identification module is used to identify the abnormal handling strategy corresponding to the abnormal detection result of the battery.

[0048] The exception handling strategy execution module is used to execute the exception handling strategy.

[0049] The technical solution of this implementation can automatically execute the abnormal handling strategy after identifying the abnormal detection result, which can effectively improve the reliability of the battery system and reduce the probability of failure.

[0050] In one possible implementation, the state of charge acquisition module includes:

[0051] The relationship acquisition submodule is used to acquire the correspondence between the open-circuit voltage and the state of charge of multiple cells in the battery when the battery is in a static state.

[0052] The list retrieval submodule is used to calculate a list of states of charge of multiple cells in the battery based on the correspondence.

[0053] The technical solution of this implementation method can eliminate external interference and obtain a more accurate state of charge by extracting the correspondence between the open circuit voltage and the state of charge of the battery under static conditions.

[0054] In one possible implementation, the state of charge offset calculation module includes: a state value acquisition submodule, used to determine the maximum state of charge, the minimum state of charge, and the sum of the states of charge of the multiple cells at any given time for the state of charge of the multiple cells at any given time; an average value calculation submodule, used to calculate the average state of charge of the multiple cells at any given time based on the sum of the states of charge of the multiple cells, the maximum state of charge, the minimum state of charge, and the number of the multiple cells; and an offset calculation submodule, used to calculate the state of charge offset of any cell at any given time based on the state of charge of any cell at any given time and the average state of charge.

[0055] The technical solution of this implementation method evaluates the state of charge deviation of each cell by calculating the difference between the SOC value of each cell and the average value after removing the maximum and minimum values. This can effectively eliminate the influence of extreme values ​​and make the evaluation more accurate.

[0056] In one possible implementation, the device further includes an anomaly output module, used to output the self-discharge information of the battery through a data platform when an anomaly in the battery is detected based on the divergence characteristics between the cells.

[0057] If the technical solution of this implementation method detects a divergent self-discharge anomaly, it outputs the self-discharge information through the data platform, thereby facilitating relevant personnel to accurately locate the abnormal electrical device.

[0058] Thirdly, an electronic device is provided, comprising: a processor and a memory storing a program or instructions; wherein the processor, when executing the program or instructions, implements the method as described in the first aspect.

[0059] Fourthly, a machine-readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the method described in the first aspect.

[0060] Fifthly, a computer program product is provided, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the method as described in the first aspect.

[0061] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0062] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0063] Figure 1 This is an architectural diagram of a battery system applicable to one embodiment of this application;

[0064] Figure 2 This is one of the schematic flowcharts of a battery detection method provided in the embodiments of this application;

[0065] Figure 3 This is a second schematic flowchart of a battery detection method provided in an embodiment of this application;

[0066] Figure 4 This is a third illustrative flowchart of a battery detection method provided in an embodiment of this application;

[0067] Figure 5 This is the fourth illustrative flowchart of a battery detection method provided in the embodiments of this application;

[0068] Figure 6 This is the fifth illustrative flowchart of a battery detection method provided in the embodiments of this application;

[0069] Figure 7This is a sixth schematic flowchart of a battery detection method provided in the embodiments of this application;

[0070] Figure 8 This is the seventh illustrative flowchart of a battery detection method provided in the embodiments of this application;

[0071] Figure 9 This is the eighth illustrative flowchart of a battery detection method provided in the embodiments of this application;

[0072] Figure 10 This is the ninth illustrative flowchart of a battery detection method provided in the embodiments of this application;

[0073] Figure 11 This is the tenth illustrative flowchart of a battery detection method provided in the embodiments of this application;

[0074] Figure 12 This is an eleventh schematic flowchart of a battery detection method provided in an embodiment of this application;

[0075] Figure 13 This is the twelfth schematic flowchart of a battery detection method proposed in the embodiments of this application;

[0076] Figure 14 This is a schematic structural block diagram of a battery detection device according to an embodiment of this application;

[0077] Figure 15 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application.

[0078] The accompanying drawings are not drawn to scale. Detailed Implementation

[0079] The embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to illustrate the principles of this application by way of example, but should not be used to limit the scope of this application, that is, this application is not limited to the described embodiments.

[0080] In the description of this application, it should be noted that, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicating orientation or positional relationships, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. Furthermore, the terms "first," "second," and "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. "Vertical" is not vertical in the strict sense, but within the allowable tolerance range. "Parallel" is not parallel in the strict sense, but within the allowable tolerance range.

[0081] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0082] In related technologies, batteries are the primary power source for electrical devices (such as vehicles, ships, or spacecraft). The battery cell is the basic building block of a battery, responsible for storing and releasing electrical energy. Battery cells can be lithium-ion cells or lithium-ion polymer cells, etc. A battery can consist of a single cell or multiple cells. When a battery comprises multiple cells, these cells can be connected in series, parallel, or series-parallel configurations to achieve the desired voltage, capacity, and power output. Because the battery cell is the fundamental building block of a battery, battery testing is typically based on cell performance. While some battery testing schemes based on cell performance have emerged, these schemes are not effective at detecting battery anomalies.

[0083] For example, battery self-discharge is an abnormal phenomenon that may occur when a battery is left unused in an open-circuit environment. This means that a battery containing a certain amount of charge loses some capacity after being left at a certain ambient temperature for a period of time. Battery self-discharge depends on the nature of the electrode materials, surface condition, electrolyte composition and concentration, impurity content, and also on the environmental conditions of storage, such as temperature and humidity. Self-discharge not only leads to a reduction in the battery's capacity, but also causes significant differences in the state of charge (SOC) of the battery pack after a period of storage, seriously affecting battery pack matching, cycle life, and safety. Related technologies typically detect abnormal self-discharge in a few cells by using the maximum SOC difference, but this method cannot cover situations where all cells exhibit abnormal self-discharge.

[0084] It should be noted that the state of charge of an abnormal battery cell will deviate significantly from that of other normally functioning battery cells. This is because there may be slight differences in materials, dimensions, or processes between different battery cells during the manufacturing process. These differences will cause the battery cells to exhibit different performance during use. Thus, when a battery cell malfunctions, its state of charge will deviate significantly from that of other normally functioning battery cells.

[0085] In view of this, this application proposes a new battery detection technology based on the phenomenon that the state of charge of abnormal cells may deviate significantly from that of other normally functioning cells, which can effectively detect battery abnormalities.

[0086] Specifically, in this application, the state of charge (POC) offset of each cell is calculated based on the POC of multiple cells in the battery. This leads to the calculation of the POC offset variation between cells. Since the POC offset variation between cells effectively reflects inconsistencies, the divergence characteristics between cells are extracted based on these characteristics. These divergence characteristics are then used to detect anomalies in the overall cell self-discharge, effectively capturing overall cell anomalies. This technical solution effectively avoids the problem of some related technologies failing to effectively capture battery anomalies by comparing the open-circuit voltage deviation and capacity deviation rate between the tested cell and a reference cell.

[0087] Based on this, embodiments of this application provide a battery detection method and related equipment to effectively detect battery abnormalities and reduce the probability of electrical device failure. The battery detection method provided in this application embodiment is described below first.

[0088] Figure 1 A schematic diagram of a battery system applicable to embodiments of this application is shown.

[0089] like Figure 1 As shown, the battery system 100 may include at least one battery pack, which can be collectively referred to as battery 121. In terms of battery type, battery 121 can be any type of battery, including but not limited to: lithium-ion batteries (such as ternary lithium-ion batteries), lithium metal batteries, lithium-sulfur batteries, lead-acid batteries, nickel-metal hydride batteries, or lithium-air batteries, etc. In this embodiment, battery 121 may include multiple cells. In this embodiment, the specific type and size of battery 121 are not specifically limited.

[0090] Optionally, the battery system 100 may be a battery system in an electric vehicle (including pure electric vehicles and plug-in hybrid electric vehicles) or a battery system in other application scenarios.

[0091] Optionally, the battery system 100 generally also includes a battery management system (BMS) 122 for detecting the status of the battery 121. Optionally, the BMS 122 can be integrated with the battery 121 in the same device, or the BMS 122 can be set as a separate device outside the battery 121.

[0092] In some embodiments, the battery detection method provided in this application can be applied to, for example... Figure 1 The battery management system 122 shown detects abnormal self-discharge of battery cells and uploads the detection results to a data platform for output. In other embodiments, the battery detection method provided in this application can be applied to a cloud server, which monitors vehicle data such as... Figure 1 The battery cell self-discharge anomaly is detected, and the detection results are output through the data platform running on it.

[0093] It should be noted that, Figure 1 The battery system shown is one possible application scenario of this application. Depending on the actual application needs, this application can also be applied to other possible application scenarios.

[0094] Figure 2 A schematic flowchart of a battery detection method proposed in an embodiment of this application is shown.

[0095] like Figure 2 As shown, the battery detection method may include the following steps.

[0096] Step 210: Obtain the state of charge (SOC) of multiple cells in the battery.

[0097] The battery in this application embodiment may be a battery comprising multiple cells. State of Charge (SOC) is state data used to describe the electrical state of a cell; for example, SOC can be represented by the proportion of remaining electrical charge to its full capacity.

[0098] Optionally, in some embodiments, in step 210, the state of charge (SOC) of multiple cells in the battery at a given moment can be obtained, and the battery can be tested based on the SOC of the multiple cells at that given moment. For example, if battery A is to be tested, and battery A includes cell 1, cell 2, and cell 3, the SOC of cell 1, cell 2, and cell 3 in battery A at time T can be obtained for battery testing.

[0099] In other embodiments, in step 210, the state of charge (SOC) of multiple cells in the battery within a time range (including multiple moments) can be obtained, and the battery is detected based on the SOC of the multiple cells within this time range. Following the previous example, the SOC of each cell in battery A within a time range T1-Tn (e.g., including moments T1, T2… to Tn) can be obtained, with each cell corresponding to a SOC list. The SOC list corresponding to each cell includes the SOC for each moment from T1 to Tn.

[0100] There are no restrictions on the specific method for obtaining the state of charge of the battery cell.

[0101] Optionally, in some embodiments, in step 210, the curves showing the relationship between the open-circuit voltage and the state of charge of multiple cells in the battery under static conditions can be extracted, and a list of the states of charge of multiple cells can be calculated by linear interpolation.

[0102] In other embodiments, in step 210, the amount of charge added and released by the battery can be calculated by accumulating the current during the battery charging and discharging process, and the state of charge can be deduced.

[0103] Step 220: Calculate the state of charge offset for each of the cells based on the state of charge for each cell.

[0104] State of charge (SCC) shift describes the degree of deviation of the state of charge (SCC) of a single cell in a battery relative to the overall SCC of all cells in the battery. The SCC shift for each cell can be calculated for a single moment in the battery's state, or it can be calculated for multiple moments based on the SCC shift obtained at a single moment.

[0105] There are no restrictions on the specific method for calculating the state of charge offset for each cell.

[0106] Optionally, in some embodiments, in step 220, if any cell at any given time is taken as the current cell, the state of charge (SOC) offset can be calculated based on the degree of deviation of the current cell's SOC from the average SOC of the multiple cells at that time. Specifically, the difference between the current cell's SOC and the average SOC of the multiple cells can be calculated, and this difference can be used as the current cell's SOC offset. Continuing with the previous example, in step 220, the SOC offsets corresponding to cells 1 through 3 in battery A can be calculated based on the SOC of cell 1, cell 2, and cell 3 at time T.

[0107] In other embodiments, in step 220, the state of charge (SOC) offset can be calculated based on the degree of offset between the current SOC of the battery cell and the median of the overall SOC of multiple battery cells. Specifically, the difference between the current SOC of the battery cell and the median of the overall SOC of multiple battery cells can be calculated, and this difference can be used as the SOC offset of the current battery cell. The median refers to the SOC that is located in the middle position after sorting the SOCs of multiple battery cells in order of magnitude.

[0108] Step 230: Calculate the change in state of charge offset between the cells based on the state of charge offset corresponding to each cell.

[0109] State of charge shift variation is used to characterize the degree of variation in state of charge shift between different cells of a battery.

[0110] There are no restrictions on the specific method for calculating the degree of change in the state of charge between battery cells.

[0111] Optionally, in some embodiments, in step 230, the state-of-charge (POC) shifts of multiple cells in the battery at any given time can be sorted. Based on the sorting result, the difference in POC shifts between adjacent cells is calculated, and this difference is used as the POC shift variation. Following the previous example, in step 220, the POC shifts of cells 1 through 3 at time T can be sorted. Assuming the sorting result is in the order of cell 1, cell 3, and cell 2, the difference in POC shifts between adjacent cells 1 and 3 is calculated to obtain difference 1, and the difference in POC shifts between adjacent cells 3 and 2 is calculated to obtain difference 2. Therefore, at time T, the POC shift variation between the cells of battery A includes difference 1 and difference 2.

[0112] In other embodiments, in step 230, after calculating the difference in state of charge offset between adjacent cells, all differences can be summed, and the summation result can be used as the state of charge offset variation between cells of battery A.

[0113] Step 240: Extract the divergence features between the cells based on the change in state of charge offset between the cells.

[0114] Divergence characteristics are features used to characterize the differences in the state of charge of different cells in a battery. They can include one or more aspects, such as the degree of divergence and the rate of divergence.

[0115] There are no restrictions on the specific methods for extracting the divergent characteristics between battery cells.

[0116] Optionally, in some embodiments, the divergence characteristics include at least one of the degree of divergence and the divergence rate.

[0117] The degree of divergence is used to characterize the degree of difference in the state of charge between cells, and is specifically determined by the degree of change in the state of charge offset between cells.

[0118] Optionally, in some embodiments, the degree of divergence can be determined based on the statistical value of the change in state of charge offset between cells at the same time. Specifically, a preset quantile (such as the 95th quantile, 90th quantile, etc.) of the change in state of charge offset between cells at the same time can be taken as the degree of divergence between cells at that time.

[0119] In other embodiments, the maximum value of the change in state of charge offset between cells at the same time can be calculated as the degree of divergence.

[0120] The divergence rate is used to characterize the rate at which the degree of divergence between battery cells changes over time, and there are no restrictions on the specific calculation method.

[0121] Optionally, in some embodiments, the divergence rate between cells can be obtained by fitting the slope of the divergence degree between cells at multiple time points. The slope can represent how quickly the divergence degree changes over time.

[0122] In other embodiments, the time series of divergence between battery cells can be converted to the frequency domain using Fourier transform, and the features in the frequency domain can be extracted to obtain the divergence rate.

[0123] Step 250: Detect whether the battery is abnormal based on the divergence characteristics between the cells.

[0124] In some embodiments, in step 250, the battery may be detected as abnormal by comparing the divergent characteristics with a preset threshold.

[0125] In other embodiments, in step 250, the divergent features between battery cells can be input into a pre-trained machine learning model to predict whether the battery is abnormal. This machine learning model can be trained using a training dataset, which may include several samples of divergent features from battery cells and their corresponding anomaly labels. After training, the machine learning model can predict whether the battery is abnormal based on the input divergent features in practical applications.

[0126] In this embodiment, the state of charge (SOC) offset of each cell is calculated based on the SOC of multiple cells in the battery. Then, the SOC offset variation between cells is calculated. Since the SOC offset variation between cells can effectively reflect the inconsistency between cells, the divergence features between cells are extracted based on the SOC offset variation. Based on the divergence features, the abnormal situation of overall cell self-discharge dispersion can be detected, which can effectively capture the anomaly.

[0127] In some embodiments, such as Figure 3 The illustrated flowchart shows a battery detection method according to an embodiment of this application. Step 240 in other embodiments can be represented as step 340 in this embodiment. Wherein:

[0128] Step 340: Calculate the degree of divergence and / or the rate of divergence between the cells based on the degree of change in the state of charge offset between the cells.

[0129] The calculation method for the degree of divergence and / or the divergence rate can be found in [reference needed]. Figure 2 The relevant descriptions of the embodiments shown will not be repeated here.

[0130] Since the degree of divergence can effectively capture the inconsistency of the state of charge between cells, using the degree of divergence to detect whether the battery is abnormal can effectively avoid the situation where most cells are normal and a few cells are discrete self-discharge abnormalities.

[0131] Since the divergence rate can effectively capture the trend of the inconsistency of the state of charge between cells over time, using the divergence rate to detect whether the battery is abnormal can help to discover potential problems in a timely manner.

[0132] It should be noted that, Figure 3 Other specific implementations of the illustrated embodiments can be found in the descriptions of other embodiments, and will not be repeated here.

[0133] In some embodiments, such as Figure 4 The illustrated flowchart shows a battery detection method according to an embodiment of this application. Step 230 in other embodiments is... Figure 4 In the illustrated embodiment, steps 4301-4302 can be represented, and step 340 can be represented as step 440. Wherein:

[0134] Step 4301: Sort the state of charge offsets of multiple cells at any given time to obtain the sorting results.

[0135] In step 4301, the cells can be sorted from largest to smallest or smallest to largest by their state of charge offset. The sorting result can include multiple cells sorted and their corresponding state of charge offsets.

[0136] In some embodiments, the state of charge offsets of multiple cells at a given time can be sorted to obtain the sorting result at that time.

[0137] In other embodiments, the state of charge offset of multiple cells at each time point can be sorted to obtain the sorting result corresponding to that time point.

[0138] Step 4302: Based on the sorting results, calculate the difference in the state of charge offset between adjacent cells, whereby the difference represents the degree of change in the state of charge offset.

[0139] In some embodiments, the degree of change in state of charge offset among multiple cells at a given moment can be calculated.

[0140] In other embodiments, the degree of change in the state of charge offset between multiple cells can be calculated separately for each of the multiple time points.

[0141] Step 440: Sort the state of charge offset changes between the cells, and take the state of charge offset change that ranks first in the preset order as the divergence degree between the cells at any given time.

[0142] In some embodiments, the degree of change in state of charge offset between cells at a given moment can be sorted.

[0143] In other embodiments, the degree of change in the state of charge offset between cells at each time point can be sorted to obtain the degree of divergence between multiple cells at multiple time points.

[0144] This application embodiment sorts the state of charge offset variation between adjacent cells and takes the state of charge offset variation at the first preset number (such as the 95th percentile) as the degree of divergence. This can eliminate the influence of extreme values, cover most cells, and effectively capture the inconsistency of state of charge between cells.

[0145] It should be noted that, Figure 4 Other specific implementations of the illustrated embodiments can be found in the descriptions of other embodiments, and will not be repeated here.

[0146] In some embodiments, such as Figure 5 The schematic flowchart shown in this application illustrates a battery detection method, which differs from other embodiments in that... Figure 5 The illustrated embodiment further includes step 560 before detecting whether the battery is abnormal based on the divergence characteristics between cells. Wherein:

[0147] Step 560: For the degree of divergence between the cells at any given time, noise in the divergence is removed using the corresponding divergence dataset and a sliding window median filtering strategy. The divergence dataset includes: the degree of divergence between the cells at any given time, and the degree of divergence between the cells at other times before and after the given time.

[0148] For example, for any given time T, the degree of divergence between cells can be taken from the degree of divergence between cells 7 days before and after time T, and the 50th quantile can be used to update the degree of divergence between cells at time T, thus eliminating the influence of noise.

[0149] The sliding window median filtering strategy refers to sorting data within a sliding window of a certain length and then taking the median value as the filtered output. The embodiments of this application utilize this sliding window median filtering strategy to effectively remove noise from divergent data.

[0150] It should be noted that, Figure 5 The specific implementation of other steps in the illustrated embodiment can be found in the descriptions of other embodiments, and will not be repeated here.

[0151] In some embodiments, such as Figure 6 The illustrated flowchart shows a battery detection method according to an embodiment of this application. Step 340 in other embodiments is... Figure 6 This can be represented by steps 6401 and 6402. Where:

[0152] Step 6401: Calculate the degree of divergence between the cells at multiple time points based on the change in state of charge offset between the cells at multiple time points.

[0153] For example, if the degree of change in the state of charge shift between cells at multiple times T1-Tn is calculated, then a degree of divergence can be calculated for each time in T1-Tn.

[0154] Step 6402: Fit the slope of the divergence between the cells at multiple time points to obtain the divergence rate between the cells.

[0155] In some embodiments, optionally, a linear regression method can be used in step 6402 to fit the slope of the divergence between cells at multiple time points.

[0156] In this embodiment of the application, the divergence rate is obtained by fitting the slope of the divergence degree between cells at multiple time points, which can detect potential anomalies as early as possible and improve detection efficiency.

[0157] It should be noted that, Figure 6 The specific implementation of other steps in the illustrated embodiment can be found in the descriptions of other embodiments, and will not be repeated here.

[0158] In some embodiments, such as Figure 7 The illustrated flowchart shows a battery detection method according to an embodiment of this application. Step 250 in other embodiments is... Figure 7 In the illustrated embodiment, this can be represented as step 750. Wherein:

[0159] Step 750: Compare the divergence characteristics between the cells with a preset divergence characteristic threshold to obtain the divergence anomaly detection result.

[0160] A preset divergence characteristic threshold is used to evaluate whether the divergence characteristics between battery cells are within the abnormal or normal range.

[0161] Optionally, when the divergence characteristic is the degree of divergence, the preset divergence characteristic threshold can be expressed as a preset divergence degree threshold, used to assess whether the degree of divergence between cells is within the normal range. When the divergence characteristic is the divergence rate, the preset divergence characteristic threshold can be expressed as a preset divergence rate threshold, used to assess whether the divergence rate between cells is within the abnormal or normal range.

[0162] The preset divergence characteristic threshold can be obtained empirically or calculated using algorithms. In some embodiments, the preset divergence characteristic threshold can be obtained by analyzing the divergence characteristic data of multiple cells in a historical sample set that are known to have abnormal or normal divergence characteristics. If, through comparison, it is found that the actual measured divergence characteristic exceeds the preset divergence characteristic threshold, it can be determined that the inconsistency between cells has reached a level requiring attention, and the abnormal divergence characteristic detection result can be shown as abnormal; otherwise, it can be determined that the inconsistency between cells has not reached a level requiring attention, and the abnormal divergence characteristic detection result can be shown as normal.

[0163] Optionally, the preset divergence characteristic threshold can be stored in the configuration information of the battery management system. When detection is required, the preset divergence characteristic threshold is read from the configuration information for comparison. This implementation facilitates the updating configuration of the preset divergence characteristic threshold. Optionally, the preset divergence characteristic threshold can be written into the battery detection logic, and the battery detection logic directly compares against the preset divergence characteristic threshold written therein.

[0164] Optionally, if the divergence feature includes divergence degree, the divergence degree is compared with a preset divergence degree threshold to obtain a divergence degree anomaly detection result. If the divergence feature includes divergence rate, the divergence rate is compared with a preset divergence rate threshold to obtain a divergence rate anomaly detection result.

[0165] In this embodiment of the application, by comparing the divergence characteristics between battery cells with a preset divergence characteristic threshold, the divergence anomaly detection result can be obtained quickly and accurately, thereby improving the detection efficiency.

[0166] It should be noted that, Figure 7 The specific implementation of other steps in the illustrated embodiment can be found in the relevant descriptions in other embodiments, and will not be repeated here.

[0167] In some embodiments, such as Figure 8The schematic flowchart shown in this application illustrates a battery detection method, which differs from other embodiments in that... Figure 8 The illustrated embodiment further includes step 860. Wherein:

[0168] Step 860: Analyze the divergence feature data in the historical sample set to obtain the preset divergence feature threshold, wherein the historical sample set includes: divergence feature data among multiple cells of batteries known to have abnormal or normal divergence features.

[0169] In step 860, the historical sample set refers to a collection of divergence characteristic data among multiple cells of a battery known to have abnormal or normal divergence characteristics. This data is used to analyze the divergence characteristics among the cells to determine a threshold that is used to assess whether the divergence characteristics among the cells are within the abnormal or normal range.

[0170] There are various ways to analyze historical sample sets.

[0171] Optionally, the abnormal range of divergence characteristics of an abnormal battery can be obtained by statistically analyzing the divergence characteristics among multiple cells of a known abnormal battery, and a preset abnormal divergence characteristic threshold can be obtained based on this range. Optionally, the normal range of divergence characteristics of a normal battery can be obtained by statistically analyzing the divergence characteristics among multiple cells of a known normal battery, and a preset normal divergence characteristic threshold can be obtained based on this range.

[0172] Optionally, the historical sample set includes: divergence data among multiple cells of batteries known to have abnormal or normal divergence, and / or divergence rate data among multiple cells of batteries known to have abnormal or normal divergence rates.

[0173] Through the embodiments of this application, it is possible to analyze from historical sample sets the thresholds used to evaluate whether the divergence characteristics between battery cells are normal or abnormal, thereby providing an accurate judgment standard for battery testing.

[0174] It should be noted that, Figure 8 The specific implementation of other steps in the illustrated embodiment can be found in the descriptions of other embodiments, and will not be repeated here.

[0175] In some embodiments, such as Figure 9 The schematic flowchart shown in this application illustrates a battery detection method, which differs from other embodiments in that... Figure 9 The illustrated embodiment further includes steps 9601 and 9602. Wherein:

[0176] Step 9601: Identify the abnormal handling strategy corresponding to the abnormal detection result of the battery.

[0177] Anomaly handling strategies are measures taken in response to abnormal detection results in order to resolve battery malfunctions.

[0178] Anomaly handling strategies can be set according to actual application needs. Optionally, anomaly handling strategies may include one or more of the following: fault isolation, replacement of backup battery cells, alarm, etc.

[0179] For example, when the divergence characteristics include divergence degree and / or divergence rate, the anomaly detection results can include the following: abnormal divergence degree, abnormal divergence rate, abnormal divergence degree and abnormal divergence rate, abnormal divergence degree but normal divergence rate, normal divergence degree and normal divergence rate, and normal divergence degree but abnormal divergence rate. The same or different anomaly handling strategies can correspond to different anomaly detection results.

[0180] Step 9602: Execute the aforementioned exception handling strategy.

[0181] Through the embodiments of this application, after identifying the anomaly handling strategy corresponding to the anomaly detection result, the anomaly handling strategy can be automatically executed, which can effectively improve the reliability of the battery system and reduce the probability of failure.

[0182] It should be noted that, Figure 9 The specific implementation of other steps in the illustrated embodiment can be found in the descriptions of other embodiments, and will not be repeated here.

[0183] In some embodiments, such as Figure 10 The illustrated flowchart shows a battery detection method according to an embodiment of this application. Step 210 in other embodiments is... Figure 10 In the illustrated embodiment, this is specifically manifested as steps 1011 and 1012. Wherein:

[0184] Step 1011: Obtain the correspondence between the open-circuit voltage and state of charge of multiple cells in the battery under static conditions.

[0185] The static operating condition refers to a battery's state of rest when there is no external charging or discharging load. In this state, the chemical reactions and physical processes inside the battery are still ongoing, but no external current flows through the battery.

[0186] Open-circuit voltage is the voltage across the battery terminals when there is no external load (no charging or discharging). Under resting conditions, open-circuit voltage can accurately represent the battery's state of charge.

[0187] The relationship between open-circuit voltage and state of charge (SCC) describes the open-circuit voltage of a battery under different SCC conditions. This relationship can be represented by a curve. Based on this relationship, the SCC of the battery can be calculated.

[0188] Optionally, the curve showing the relationship between open-circuit voltage and state of charge can be obtained through experiments, simulations, or other methods, and can be stored in the battery management system for later retrieval when needed.

[0189] Step 1012: Based on the correspondence, calculate a list of the states of charge of multiple cells in the battery.

[0190] Alternatively, a list of states of charge (SOCs) for multiple cells in the battery can be calculated using methods such as linear interpolation or lookup tables. For example, when calculating using linear interpolation, the function between known points can be assumed to be linear on the corresponding relationship curve, and the SOC of the desired point can be found on the line.

[0191] The embodiments of this application can obtain a relatively accurate state of charge by extracting the correspondence between the open-circuit voltage and the state of charge of the battery under static conditions.

[0192] It should be noted that, Figure 10 The specific implementation of steps 220-250 in the illustrated embodiment can be found in the relevant descriptions of other embodiments, and will not be repeated here.

[0193] In some embodiments, such as Figure 11 The diagram shown is a schematic flowchart of a battery detection method according to an embodiment of this application. Step 220 in other embodiments is... Figure 11 In the illustrated embodiment, this is specifically manifested as steps 1121-1123.

[0194] in:

[0195] Step 1121: For the state of charge of the multiple cells at any given time, determine the maximum value of the state of charge, the minimum value of the state of charge, and the sum of the states of charge of the multiple cells at that given time.

[0196] In this step, the maximum and minimum states of charge (SOCs) of multiple cells can be identified by comparing them.

[0197] Step 1122: Based on the sum of the states of charge of the multiple cells, the maximum state of charge, the minimum state of charge, and the number of the multiple cells, calculate the average state of charge of the multiple cells at any given time.

[0198] Optionally, the average state of charge of the multiple cells at any given time can be calculated using the following formula:

[0199]

[0200] Wherein, sum(soc) represents the sum of the states of charge of the multiple battery cells, max(soc) represents the maximum state of charge, min(soc) represents the minimum state of charge, and len(soc) represents the number of the multiple battery cells.

[0201] Step 1123: Based on the state of charge of any cell at any given time and the average state of charge, calculate the state of charge offset of any cell at any given time.

[0202] Optionally, the state-of-charge shift of any cell at any given time can be calculated using the following formula:

[0203]

[0204] Where deltaSOC represents the state of charge offset and soc represents the state of charge of any cell.

[0205] In the above formula, the larger the absolute value of the state of charge (SOC) deviation, the greater the deviation of the cell from the average level. Therefore, in some embodiments, all cells at the same time can be sorted by deltaSOC, and the difference between adjacent deltaSOC values ​​of the sorted cells represents the degree of divergence between cells. The statistical value of the divergence between cells at the same time represents the overall divergence at that moment. In other embodiments, the trend of divergence over time is characterized by the divergence rate, thereby enabling further determination of the specific starting divergence time point and the speed of divergence.

[0206] This application's embodiments assess the state-of-charge deviation of each cell by calculating the difference between the SOC value of each cell and the average value after removing the maximum and minimum values. This effectively eliminates the influence of extreme values ​​and makes the assessment more accurate.

[0207] It should be noted that the specific implementation methods of other steps in the embodiments of this application can be referred to the relevant descriptions of other embodiments, and will not be repeated here.

[0208] In some embodiments, such as Figure 12 The schematic flowchart shown in this application illustrates a battery detection method, which differs from other embodiments in that... Figure 12 The illustrated embodiment also includes step 1201. Wherein:

[0209] Step 1201: If the battery is found to have an abnormal self-discharge based on the divergence characteristics between the cells, the self-discharge information of the battery is output through the data platform.

[0210] Self-discharge anomaly refers to abnormal energy loss in a battery due to internal chemical reactions or other factors when there is no external load. It is understood that cells in a normal battery typically have similar states of charge. If the divergence characteristics between cells exceed a threshold, it indicates that the performance of one cell significantly deviates from that of the others, which is one indication of self-discharge anomaly. Therefore, this embodiment can detect self-discharge anomalies based on the divergence characteristics between cells, and then output the battery's self-discharge information through a data platform to accurately locate the anomaly and allow relevant personnel to obtain relevant information in a timely manner.

[0211] Optionally, the self-discharge information may include: alarm information indicating the presence of self-discharge, specific parameter information of the self-discharging battery, etc.

[0212] For example, according to this embodiment, if a divergent self-discharge anomaly is detected, self-discharge information can be output and a self-discharge image of the electrical device (such as an electric vehicle) can be drawn. The self-discharge information can be output through the data platform, so that relevant personnel can accurately locate the abnormal electrical device and intuitively see the self-discharge phenomenon through the image displayed on the data platform.

[0213] It should be noted that the specific implementation methods of other steps in the embodiments of this application can be referred to the relevant descriptions of other embodiments, and will not be repeated here.

[0214] In some embodiments, such as Figure 13 The illustrated schematic flowchart shows a battery detection method according to an embodiment of this application. The method includes:

[0215] Step 1301: Obtain the curves showing the relationship between the open-circuit voltage and state of charge of multiple cells in the battery under static conditions.

[0216] Step 1302: Extract a list of states of charge of multiple cells in the battery from the corresponding relationship curves using linear interpolation.

[0217] Step 1303: For multiple moments in the state of charge list of multiple cells, calculate the state of charge offset of each cell at each moment based on the state of charge of each cell at each moment, the sum of the states of charge of multiple cells at each moment, the maximum state of charge, the minimum state of charge, and the number of cells.

[0218] Step 1304: Based on the state of charge offset of each cell at each time step, calculate the change in state of charge offset between cells at each time step.

[0219] Step 1305: Based on the change in state of charge offset between cells at each time step, extract the degree of divergence and the divergence rate between cells at each time step as divergence features.

[0220] The degree of divergence can be optimized by using sliding window median filtering and quantiles.

[0221] Step 1306: Determine whether the extracted divergence degree and divergence rate exceed the corresponding thresholds.

[0222] In this step, the degree of divergence can be compared with a preset divergence threshold, and the divergence rate can be compared with a preset divergence rate threshold for judgment.

[0223] If it is determined from the judgment result of step 1306 that an abnormal warning needs to be issued, proceed to step 1307.

[0224] Step 1307: Anomaly Warning.

[0225] Step 1308: Investigation and handling.

[0226] Step 1308 involves troubleshooting and handling anomalies, which can be understood as... Figure 8 The abnormal handling strategy corresponding to the abnormal detection result of the battery in the illustrated embodiment, and the steps for executing the abnormal handling strategy, will not be described in detail here.

[0227] In this embodiment, the state of charge (SOC) of multiple cells in a battery under static conditions is obtained, making the obtained SOC more accurate. This leads to more accurate calculations of the divergence degree and rate. Furthermore, anomaly detection is performed on the divergence degree and rate using thresholds, resulting in higher detection efficiency. Finally, the divergence degree and rate are used to detect abnormalities in the overall cell self-discharge, effectively avoiding the false detection of most cells being normal while a few cells are discrete. This significantly improves the accuracy of battery detection and reduces the probability of failure.

[0228] The above text combined Figures 2-13 Specific embodiments of the battery detection method provided in this application are described below. Figures 14 to 15 The following describes specific embodiments of the related devices provided in this application. It is understood that the relevant descriptions in the following device embodiments can be referred to the foregoing method embodiments, and for the sake of brevity, they will not be repeated.

[0229] Figure 14 A schematic structural block diagram of a battery detection device 1400 according to an embodiment of this application is shown. Figure 14 As shown, the battery testing device 1400 includes:

[0230] The state of charge acquisition module 1410 is used to acquire the state of charge of multiple cells in the battery.

[0231] The state of charge offset calculation module 1420 is used to calculate the state of charge offset of each battery cell based on the state of charge of each battery cell.

[0232] The state of charge offset change calculation module 1430 is used to calculate the state of charge offset change between the cells based on the state of charge offset corresponding to each cell.

[0233] The divergence feature extraction module 1440 is used to extract the divergence features between the battery cells based on the change in the state of charge offset between the battery cells.

[0234] Anomaly detection module 1450 is used to detect whether the battery is abnormal based on the divergence characteristics between the cells.

[0235] Optionally, the divergence feature extraction module is used to calculate the degree of divergence and / or the divergence rate between the cells based on the degree of change in the state of charge offset between the cells.

[0236] Optionally, the charge state offset change calculation module includes:

[0237] The offset sorting submodule is used to sort the state of charge offsets of multiple cells at any given time to obtain the sorting result.

[0238] The difference calculation submodule is used to calculate the difference in the state of charge offset between adjacent cells based on the sorting result. The difference is used to represent the degree of change in the state of charge offset.

[0239] The divergence feature extraction module is used to sort the degree of change of state of charge offset between the cells, and take the degree of change of state of charge offset that ranks first in the preset order as the degree of divergence between the cells at any given time.

[0240] Optionally, the device further includes: a noise reduction module, used to remove noise from the divergence between the cells at any given time by using a corresponding divergence dataset and a sliding window median filtering strategy, wherein the divergence dataset includes: the divergence between the cells at any given time, and the divergence between the cells at other times before and after the given time.

[0241] Optionally, the divergent feature extraction module includes:

[0242] The divergence degree extraction submodule is used to calculate the divergence degree between the cells at multiple time points based on the change in state of charge offset between the cells at multiple time points.

[0243] The divergence rate extraction submodule is used to fit the slope of the divergence degree between the cells at multiple time points to obtain the divergence rate between the cells.

[0244] The anomaly detection module is used to compare the divergence characteristics between the battery cells with a preset divergence characteristic threshold to obtain the divergence anomaly detection result.

[0245] Optionally, the device further includes:

[0246] The threshold analysis module is used to analyze the divergence feature data in the historical sample set to obtain the preset divergence feature threshold. The historical sample set includes divergence feature data among multiple cells of batteries known to have abnormal or normal divergence features.

[0247] Optionally, the device further includes:

[0248] The handling strategy identification module is used to identify the abnormal handling strategy corresponding to the abnormal detection result of the battery.

[0249] The exception handling strategy execution module is used to execute the exception handling strategy.

[0250] Optionally, the state of charge acquisition module includes:

[0251] The relationship acquisition submodule is used to acquire the correspondence between the open-circuit voltage and the state of charge of multiple cells in the battery when the battery is in a static state.

[0252] The list retrieval submodule is used to calculate a list of states of charge of multiple cells in the battery based on the correspondence.

[0253] Optionally, the state of charge offset calculation module includes:

[0254] The state value acquisition submodule is used to determine the maximum value of the state of charge of the multiple cells at any given time, the minimum value of the state of charge, and the sum of the states of charge of the multiple cells at any given time.

[0255] The average value calculation submodule is used to calculate the average state of charge of the multiple battery cells at any given time based on the sum of the states of charge of the multiple battery cells, the maximum value of the states of charge, the minimum value of the states of charge, and the number of the multiple battery cells.

[0256] The offset calculation submodule is used to calculate the state of charge offset of any cell at any given time based on the state of charge of any cell at any given time and the average state of charge.

[0257] Optionally, the device further includes:

[0258] An abnormal output module is used to output the self-discharge information of the battery through a data platform when the self-discharge abnormality of the battery is detected based on the divergence characteristics between the cells.

[0259] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. They are devices corresponding to the above-mentioned battery detection method. All implementation methods in the above-mentioned method embodiments are applicable to the embodiments of this device. For details on its specific functions and the resulting technical effects, please refer to the method embodiment section. It will not be repeated here.

[0260] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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 as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0261] Figure 15 A schematic diagram of the hardware structure of an electronic device provided in yet another embodiment of this application is shown.

[0262] Electronic device 1500 may include processor 1501 and memory 1502 storing programs or instructions. When processor 1501 executes the program, it implements the steps in any of the above method embodiments.

[0263] For example, the program can be divided into one or more modules / units, one or more of which are stored in memory 1502 and executed by processor 1501 to complete this application. One or more modules / units can be a series of program instruction segments capable of performing a specific function, which describe the program's execution process in the device.

[0264] Specifically, the processor 1501 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0265] Memory 1502 may include mass storage for data or instructions. For example, and not limitingly, memory 1502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1502 may include removable or non-removable (or fixed) media. Where appropriate, memory 1502 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 1502 is non-volatile solid-state memory.

[0266] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.

[0267] The processor 1501 implements any of the methods described above by reading and executing programs or instructions stored in the memory 1502.

[0268] In one example, the electronic device may also include a communication interface 1503 and a bus 1504. The processor 1501, memory 1502, and communication interface 1503 are connected via the bus 1504 and communicate with each other.

[0269] The communication interface 1503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0270] Bus 1504 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1504 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0271] Furthermore, in conjunction with the methods in the above embodiments, this application embodiment can provide a machine-readable storage medium for implementation. This machine-readable storage medium stores a program or instructions; when executed by a processor, the program or instructions implement any of the methods in the above embodiments. This machine-readable storage medium can be read by a machine such as a computer.

[0272] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above method embodiments and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0273] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0274] This application provides a computer program product stored in a machine-readable storage medium. The program product is executed by at least one processor to implement the various processes of the above method embodiments and achieve the same technical effects. To avoid repetition, it will not be described again here.

[0275] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0276] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer grids such as the Internet, intranets, etc.

[0277] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0278] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by a computer program or instructions. These programs or 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 these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0279] Although this application has been described with reference to preferred embodiments, various modifications can be made thereto and components can be replaced with equivalents without departing from the scope of this application. In particular, the technical features mentioned in the various embodiments can be combined in any manner, provided there is no structural conflict. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A battery testing method, characterized in that, include: Obtain the state of charge (SOC) of multiple cells in the battery; Based on the state of charge corresponding to each of the battery cells, the state of charge offset of each of the battery cells is calculated, wherein the state of charge offset is used to characterize the degree of deviation of the state of charge of the battery cell relative to the state of charge of the plurality of battery cells; Based on the state of charge offset corresponding to each of the battery cells, the state of charge offset variation between the battery cells is calculated, wherein the state of charge offset variation is used to characterize the degree of variation of the state of charge offset of different battery cells. Based on the change in state of charge offset between the cells, the divergence characteristics between the cells are extracted. The battery is detected to be abnormal based on the divergence characteristics between the cells.

2. The method according to claim 1, characterized in that, The extraction of divergent features between the battery cells based on the change in state of charge offset between the cells includes: Based on the degree of change in state of charge offset between the cells, the degree of divergence and / or the rate of divergence between the cells are calculated.

3. The method according to claim 2, characterized in that, The calculation of the change in state of charge offset between the cells based on the state of charge offset corresponding to each cell includes: The state of charge offsets of multiple cells at any given time are sorted to obtain the sorting result; Based on the sorting results, the difference in the state of charge offset between adjacent cells is calculated, and the difference is used to represent the degree of change in the state of charge offset. The extraction of divergent features between the battery cells based on the change in state of charge offset between the cells includes: The degree of change of state of charge (SOC) between the cells is sorted, and the SOC at the first preset number is taken as the degree of divergence between the cells at any given time.

4. The method according to claim 2, characterized in that, The method further includes: For the degree of divergence between the cells at any given time, noise in the divergence is removed using the corresponding divergence dataset and a sliding window median filtering strategy. The divergence dataset includes: the degree of divergence between the cells at any given time, and the degree of divergence between the cells at other times before and after the given time.

5. The method according to claim 2, characterized in that, The calculation of the divergence degree and / or divergence rate between the battery cells based on the change in state of charge offset between the cells includes: Based on the change in state of charge offset between the cells at multiple time points, the degree of divergence between the cells at multiple time points is calculated; By fitting the slope of the divergence between the cells at multiple time points, the divergence rate between the cells is obtained.

6. The method according to claim 1 or 2, characterized in that, The method of detecting whether the battery is abnormal based on the divergence characteristics between the cells includes: The divergence characteristics between the battery cells are compared with a preset divergence characteristic threshold to obtain the divergence anomaly detection result.

7. The method according to claim 6, characterized in that, The method further includes: Analyze the divergence feature data in the historical sample set to obtain the preset divergence feature threshold, wherein the historical sample set includes: divergence feature data among multiple cells of batteries known to have abnormal or normal divergence features.

8. The method according to claim 1 or 2, characterized in that, The method further includes: Identify the abnormal handling strategy corresponding to the abnormal detection results of the battery; Execute the aforementioned exception handling strategy.

9. The method according to claim 1 or 2, characterized in that, The process of obtaining the state of charge (SOC) of multiple cells in the battery includes: Obtain the correspondence between the open-circuit voltage and state of charge of multiple cells in the battery under static conditions; Based on the aforementioned correspondence, a list of states of charge of multiple cells in the battery is calculated.

10. The method according to claim 1 or 2, characterized in that, The step of calculating the state of charge offset for each battery cell based on its state of charge includes: For the state of charge of multiple cells at any given time, determine the maximum value of the state of charge, the minimum value of the state of charge, and the sum of the states of charge of the multiple cells at that given time; Based on the sum of the states of charge of the multiple battery cells, the maximum state of charge, the minimum state of charge, and the number of the multiple battery cells, the average state of charge of the multiple battery cells at any given time is calculated. Based on the state of charge of any cell at any given time and the average state of charge, the state of charge offset of any cell at any given time is calculated.

11. The method according to claim 1 or 2, characterized in that, The method further includes: If the battery is found to have an abnormal self-discharge based on the divergence characteristics between the cells, the self-discharge information of the battery is output through the data platform.

12. A battery testing device, characterized in that, include: The state of charge acquisition module is used to acquire the state of charge of multiple cells in the battery. The state of charge offset calculation module is used to calculate the state of charge offset of each of the battery cells based on the state of charge of each of the battery cells, wherein the state of charge offset is used to characterize the degree of offset of the state of charge of the battery cell relative to the state of charge of the plurality of battery cells. The state of charge offset variation calculation module is used to calculate the state of charge offset variation between the cells based on the state of charge offset corresponding to each cell, wherein the state of charge offset variation is used to characterize the degree of variation of the state of charge offset of different cells. The divergence feature extraction module is used to extract the divergence features between the battery cells based on the degree of change in the state of charge offset between the battery cells; An anomaly detection module is used to detect whether the battery is abnormal based on the divergence characteristics between the battery cells.

13. An electronic device, characterized in that, The device includes: a processor and a memory storing programs or instructions; When the processor executes the program or instructions, it implements the method as described in any one of claims 1-11.

14. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores a program or instructions that, when executed by a processor, implement the method as claimed in any one of claims 1-11.

15. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1-11.

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