Internal resistance determination method, apparatus, computer device and storage medium

Abstract

The present application relates to an internal resistance determination method, an apparatus, a computer device and a storage medium. The method comprises: acquiring a plurality of sets of sampling data of a cell in a battery under test; on the basis of the plurality of sets of sampling data, determining a plurality of initial data variations; then performing fitting on the initial data variations, so as to obtain voltage fitting values corresponding to current variations among the initial data variations; on the basis of voltage variations and the voltage fitting values corresponding to the current variations among the initial data variations, performing screening on the initial data variations, so as to obtain a target data variation; and, on the basis of the target data variation, determining the internal resistance of the cell, wherein each set of sampling data comprises sampling data at two moments, and the initial data variations comprise the current variations and the voltage variations. The present method can be used to reduce the dependence on the battery status.

Description

Internal resistance determination method, device, computer equipment and storage medium

[0001] Related applications

[0002] This application claims priority to Chinese patent application number 202311714495X, filed on December 13, 2023, entitled “Internal Resistance Determination Method, Device, Computer Equipment and Storage Medium,” the entire text of which is incorporated herein by reference. Technical Field

[0003] The present application relates to the field of battery technology, and in particular to a method, apparatus, computer device, and storage medium for determining internal resistance. Background Art

[0004] Internal resistance is an important parameter in the battery field, so it is crucial to determine the accurate internal resistance.

[0005] Because internal resistance is affected by factors such as the battery's manufacturing materials, manufacturing process, connector structure, and battery usage, it is usually detected through DC charging or DC discharging. For example, when the battery is in a stable state, the current pulses are controlled to cause the battery voltage to rise and fall multiple times, and the internal resistance is estimated by the ratio between the voltage change and the current change. However, the above method of determining internal resistance is highly dependent on the battery state.

[0006] Summary of the Invention

[0007] Based on this, it is necessary to provide a method, apparatus, computer device and storage medium for determining internal resistance that can reduce dependence on battery status in order to address the above technical issues.

[0008] In a first aspect, the present application provides a method for determining internal resistance, comprising:

[0009] Acquire multiple sampling data groups of the cells in the battery to be tested; the sampling data groups include sampling data at two moments;

[0010] Determine multiple initial data changes based on multiple sampling data groups; the initial data changes include current changes and voltage changes;

[0011] Fitting each initial data variation to obtain a voltage fitting value corresponding to the current variation in each initial data variation;

[0012] According to the voltage variation and voltage fitting amount corresponding to the current variation in each initial data variation, each initial data variation is screened to obtain the target data variation;

[0013] Determine the internal resistance of the battery cell based on the target data change.

[0014] In the above-mentioned internal resistance determination method, since the sampling data group includes sampling data at two moments, after obtaining multiple sampling data groups of the battery cells in the battery to be tested, multiple initial data changes including current changes and voltage changes can be determined based on the multiple sampling data groups. In addition, since each initial data change is fitted, the voltage fitting amount corresponding to the current change in each initial data change is obtained, and based on the voltage change and voltage fitting amount corresponding to the current change in each initial data change, each initial data change is screened to obtain the target data change. Therefore, the degree of influence of the battery state on the initial data change can be reduced to improve the stability and accuracy of the obtained target data change. Based on this, in the process of determining the internal resistance, this embodiment does not need to wait for the battery to be in a stable battery state, but can determine a relatively stable and accurate internal resistance of the battery cell based on the target data change, further reducing the dependence on the battery state, expanding the application scenarios for determining the internal resistance, and being more flexible.

[0015] In one embodiment, the target data variation is obtained by screening the initial data variation according to the voltage variation and the voltage fitting value corresponding to the current variation in each initial data variation, including:

[0016] Determine the absolute value of the residual between the voltage change corresponding to the current change in each initial data change and the voltage fitting value;

[0017] The initial data change corresponding to the absolute value not less than the reference residual is taken as the target data change.

[0018] In the above embodiment, since the initial data variation corresponding to the absolute value not less than the reference residual is used as the target data variation, the accuracy of the target data variation is improved.

[0019] In one embodiment, determining the internal resistance of the battery cell according to the target data change includes:

[0020] Determine the internal resistance of the battery cell according to the target data change and the sampling parameters in the sampling data corresponding to the target data change;

[0021] The sampling parameters include at least one of an environmental parameter and a state of charge.

[0022] In the above embodiment, since the sampling parameters include at least one of the environmental parameters and the state of charge, based on the target data change and the sampling parameters in the sampling data corresponding to the target data change, at least one of the environmental parameters and the state of charge can be taken into account in determining the internal resistance of the battery cell, which is beneficial to reduce the influence of the state of the battery to be tested and improve the accuracy of the determined internal resistance.

[0023] In one embodiment, determining the internal resistance of the battery cell according to the target data change includes:

[0024] Fitting the data to be fitted to obtain a fitting curve; the data to be fitted includes a target data variation, or the data to be fitted includes the target data variation and sampling parameters in the sampling data corresponding to the target data variation;

[0025] The internal resistance of the battery cell is determined based on the slope of the fitting curve.

[0026] In the above embodiment, since the environmental parameters can be taken into account in determining the internal resistance of the battery cell based on the target data variation and the sampling parameters corresponding to the target data variation with relatively high accuracy, the data to be fitted includes the target data variation, or the data to be fitted includes the target data variation and the sampling parameters in the sampling data corresponding to the target data variation. Therefore, after fitting the data to be fitted to obtain a fitting curve, the internal resistance of the battery cell can be determined based on the slope of the fitting curve, thereby improving the efficiency of determining the internal resistance. In addition, the dependence on the battery state and the accuracy of the determined internal resistance can also be reduced.

[0027] In one embodiment, the method further comprises:

[0028] For the target data variation of each cell of the battery to be tested, the target data variation is grouped according to the time window to obtain the target data variation corresponding to each time window;

[0029] Determine the internal resistance of the battery cell in each time window based on the target data change corresponding to each time window;

[0030] Based on the internal resistance of each battery cell corresponding to each time window, each battery cell is tested to obtain a test result.

[0031] In the above embodiment, the target data variation of each cell of the battery to be tested can be grouped according to time windows to obtain the target data variation corresponding to each time window. Based on the target data variation corresponding to each time window, the internal resistance of the cell corresponding to each time window is determined. Based on the internal resistance of each cell corresponding to each time window, each cell can be tested to obtain a test result. Since the determined internal resistance is relatively stable and accurate, the accuracy of the test result determined based on the internal resistance is also improved.

[0032] In one embodiment, each battery cell is tested based on the internal resistance corresponding to each battery cell in each time window to obtain a test result, including:

[0033] Determine the reference internal resistance based on the internal resistance of each cell in the same time window;

[0034] Determine the internal resistance difference between the internal resistance of each battery cell in the same time window and the reference internal resistance;

[0035] For each cell, determine the relative internal resistance vector of the cell based on the internal resistance difference of the cell in each time window;

[0036] According to the relative internal resistance vector of each battery cell, each battery cell is tested to obtain a test result.

[0037] In the above embodiment, a reference internal resistance can be determined based on the internal resistance of each cell in the same time window, and the internal resistance difference between the internal resistance of each cell in the same time window and the reference internal resistance can be determined. Furthermore, for each cell, the relative internal resistance vector of the cell can be determined based on the internal resistance difference of the cell in each time window. Subsequently, based on the relative internal resistance vector of each cell, each cell can be tested to obtain a more accurate test result.

[0038] In one embodiment, testing each battery cell to obtain a test result based on the relative internal resistance vector of each battery cell includes:

[0039] Determine the abnormal threshold of each battery cell based on the relative internal resistance vector of each battery cell;

[0040] Based on the relative internal resistance vector and abnormal threshold of each battery cell, each battery cell is tested to obtain a test result.

[0041] In the above embodiment, since the abnormality threshold of each cell is determined based on the relative internal resistance vector of each cell, the accuracy of the abnormality threshold of each cell is improved. Furthermore, based on the relative internal resistance vector and the abnormality threshold of each cell, accurate detection results can be obtained by testing each cell.

[0042] In one embodiment, determining an abnormal threshold value of each battery cell according to a relative internal resistance vector of each battery cell includes:

[0043] For each battery cell, determine a first quantile and a second quantile based on the absolute value of each internal resistance difference in the relative internal resistance vector of the battery cell; the first quantile is greater than the second quantile;

[0044] Determine the target quantile range according to the second difference between the first quantile and the second quantile;

[0045] Determine the average value of the internal resistance differences in the relative internal resistance vector of the battery cell;

[0046] The abnormal threshold value of the battery cell is determined according to the sum of the average value and the first product; the first product is determined according to the product of the first preset multiple and the target percentile range.

[0047] Since the above embodiment determines the abnormality threshold of each battery cell according to the relative internal resistance vector of each battery cell, the accuracy of the abnormality threshold of each battery cell can be improved.

[0048] In one embodiment, based on the relative internal resistance vector and the abnormality threshold of each battery cell, each battery cell is tested to obtain a test result, including:

[0049] For each battery cell, if there is an internal resistance difference greater than an abnormal threshold value of the battery cell in the relative internal resistance vector of the battery cell, the detection result of the battery cell is determined to be an abnormal detection result.

[0050] In the above embodiment, for each battery cell, when there is an internal resistance difference greater than the abnormal threshold value of the battery cell in the relative internal resistance vector of the battery cell, the detection result of the battery cell is determined to be an abnormal detection result, thereby improving the efficiency of determining the abnormal detection result.

[0051] In one embodiment, determining that a test result of a battery cell is an abnormal test result includes:

[0052] If there are N consecutive internal resistance differences greater than the abnormal threshold, and the N internal resistance differences greater than the abnormal threshold show an increasing trend, the test result of the battery cell is determined to be an abnormal test result;

[0053] Wherein, N is an integer greater than 1.

[0054] In the above embodiment, the detection result of the battery cell is determined to be an abnormal detection result only when there are N consecutive internal resistance differences greater than the abnormal threshold, and the N internal resistance differences greater than the abnormal threshold show an increasing trend; since N is an integer greater than 1, the accuracy of the abnormal detection result is improved.

[0055] In one embodiment, the method further comprises:

[0056] If the test result of the battery cell is an abnormal test result, a risk quantification value of the battery cell is determined based on the abnormal parameters and abnormal thresholds of the battery cell;

[0057] Among them, the abnormal parameters include abnormal time, target internal resistance difference, and time span; the abnormal time is determined based on the time window closest to the current time among the N time windows of internal resistance differences, the target internal resistance difference is any internal resistance difference among the N internal resistance differences, and the time span is the duration between the time window corresponding to the target internal resistance difference and the maximum sampling time of the sampling data.

[0058] In the above embodiment, since the abnormal parameters include abnormal time, target internal resistance difference, and time span; the abnormal time is determined based on the time window closest to the current time among the N time windows of internal resistance differences, the target internal resistance difference is any internal resistance difference among the N internal resistance differences, and the time span is the length of time between the time window corresponding to the target internal resistance difference and the maximum sampling time of the sampling data, therefore, when the detection result of the battery cell is an abnormal detection result, a more accurate risk quantification value can be determined based on the abnormal parameters and abnormal thresholds of the battery cell.

[0059] In one embodiment, determining a risk quantification value of a battery cell based on abnormal parameters and abnormal thresholds of the battery cell includes:

[0060] determining the degree of abnormality of the battery cell according to a ratio between the third difference and the abnormality threshold of the battery cell; the third difference is the difference between the target internal resistance difference and the abnormality threshold of the battery cell;

[0061] Determining a time expansion factor based on a preset power of a natural base number; the preset power is the product of the second preset multiple and the fourth difference, and the fourth difference is the difference between the time span and the abnormal time;

[0062] The risk quantification value of the battery cell is determined based on the second product between the abnormality degree of the battery cell and the time expansion factor.

[0063] In the above embodiment, since the third difference is the difference between the target internal resistance difference and the abnormal threshold of the battery cell, and the fourth difference is the difference between the time span and the abnormal time, a more accurate risk quantification value of the battery cell can be determined based on the second product between the abnormality degree of the battery cell and the time expansion factor.

[0064] In one embodiment, the method further comprises:

[0065] Determine the early warning fault level of the battery cell according to the risk quantification value of the battery cell and the second preset relationship;

[0066] The second preset relationship is used to characterize the corresponding relationship between different risk quantification values ​​and different warning fault levels.

[0067] In the above embodiment, since the second preset relationship is used to characterize the correspondence between different risk quantification values ​​and different warning fault levels, it is possible to efficiently and accurately determine the warning fault level of the battery cell based on the risk quantification value of the battery cell and the second preset relationship.

[0068] In one embodiment, the method further comprises:

[0069] Obtain first sampling data of the battery cell from the server;

[0070] Preprocessing the first sampled data to obtain second sampled data;

[0071] The sampling data in the second sampling data, in which the temperature is not less than the preset temperature threshold and the state of charge is within the preset range, is used as the third sampling data;

[0072] The data in which the adjacent current changes in the third sampling data are greater than the first threshold and the adjacent current sampling intervals are less than the second threshold are used as the sampling data.

[0073] After the above embodiment obtains the first sampling data of the battery cell from the server, the first sampling data is preprocessed to obtain the second sampling data. Since the sampling data in which the temperature is not less than the preset temperature threshold and the charge state is within the preset range is used as the third sampling data, and the data in which the adjacent current changes are greater than the first threshold and the adjacent current sampling intervals are less than the second threshold are used as the sampling data, the accuracy of the sampling data is improved.

[0074] In a second aspect, the present application further provides an internal resistance determination device, comprising:

[0075] The first acquisition module is used to acquire multiple sampling data groups of the battery cells in the battery to be tested; the sampling data groups include sampling data at two moments;

[0076] A first determining module is configured to determine a plurality of initial data variation amounts based on a plurality of sampling data groups; the initial data variation amounts include a current variation amount and a voltage variation amount;

[0077] A fitting module is used to fit each initial data variation to obtain a voltage fitting value corresponding to the current variation in each initial data variation;

[0078] A screening module is used to screen each initial data variation to obtain a target data variation according to a voltage variation and a voltage fitting value corresponding to a current variation in each initial data variation;

[0079] The second determination module is used to determine the internal resistance of the battery cell according to the target data change.

[0080] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0081] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0082] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the steps of any of the above methods when executed by a processor.

[0083] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0085] FIG1 is a diagram showing an application environment of a method for determining internal resistance according to an embodiment of the present application;

[0086] FIG2 is a flow chart of a method for determining internal resistance in an embodiment of the present application;

[0087] FIG3 is a schematic diagram of a process for obtaining a target data variation according to an embodiment of the present application;

[0088] FIG4 is a schematic diagram of a process for determining internal resistance in an embodiment of the present application;

[0089] FIG5 is a schematic diagram of a process for obtaining a test result in an embodiment of the present application;

[0090] FIG6 is a schematic diagram of another process for obtaining a test result in an embodiment of the present application;

[0091] FIG7 is a schematic diagram of another process for obtaining a test result in an embodiment of the present application;

[0092] FIG8 is a schematic diagram of a process for determining an abnormality threshold in an embodiment of the present application;

[0093] FIG9 is a schematic diagram of a process for determining an abnormality detection result according to an embodiment of the present application;

[0094] FIG10 is a schematic diagram of a process for determining a risk quantification value in an embodiment of the present application;

[0095] FIG11 is a schematic diagram of a time expansion in an embodiment of the present application;

[0096] FIG12 is a schematic diagram of a process for obtaining sampled data in an embodiment of the present application;

[0097] FIG13 is a schematic diagram of an effect of an embodiment of the present application;

[0098] FIG14 is a schematic diagram of a process of determining internal resistance according to an embodiment of the present application;

[0099] FIG15 is a structural block diagram of a device for determining and adjusting internal resistance according to an embodiment of the present application;

[0100] FIG16 is a structural block diagram of a screening module according to an embodiment of the present application;

[0101] FIG17 is a structural block diagram of a second determination module in an embodiment of the present application;

[0102] FIG18 is a structural block diagram of another internal resistance determination and adjustment device according to an embodiment of the present application;

[0103] FIG19 is a structural block diagram of a detection module according to an embodiment of the present application;

[0104] FIG20 is a structural block diagram of a detection unit according to an embodiment of the present application;

[0105] FIG21 is a structural block diagram of another internal resistance determination and adjustment device according to an embodiment of the present application;

[0106] FIG22 is a structural block diagram of a fourth determination module in an embodiment of the present application;

[0107] FIG23 is a structural block diagram of another internal resistance determination and adjustment device according to an embodiment of the present application;

[0108] FIG24 is a structural block diagram of another internal resistance determination and adjustment device according to an embodiment of the present application;

[0109] FIG25 is a diagram showing the internal structure of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0110] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0111] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0112] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0113] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0114] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0115] Internal resistance is a crucial parameter in the battery industry. For example, in automotive applications, wide variations in resistance between battery cells can lead to vehicle failures. For example, cells with abnormal internal resistance can reach the charge cutoff voltage prematurely, terminating the charging process prematurely. Alternatively, excessive variations in cell internal resistance can cause frequent voltage differential fault alarms during driving.

[0116] Currently, internal resistance is determined through direct current charging or discharging. Specifically, when the battery is in a stable state, the current pulses are controlled to cause the battery voltage to rise and fall multiple times, and the internal resistance is estimated using the ratio of the voltage change to the current change, dU / dI. A stable battery state may include, for example, the battery's SOC and temperature being within a preset range, the charge current duration being greater than a certain threshold, the charge current change being less than a preset limit and the charge current being greater than a preset current threshold, and no battery fault warning.

[0117] It can be seen that the current method of determining the internal resistance is highly dependent on the battery state. On the one hand, since the determination of the internal resistance is more dependent on the battery state, the randomness of the battery state cannot guarantee the stability of the identification opportunity. At the same time, in some application scenarios, the conditions for the internal resistance of different powder batteries to be in a stable state cannot be unified. In addition, due to measurement errors, temperature and current fluctuations, SOC inaccuracies, and loss of collected data, it is easy to introduce errors when calculating the internal resistance of a single point, resulting in an unstable distribution variance of the single-point calculated value of the internal resistance. If the mean is smoothed, the final internal resistance estimation result is also easily affected by outliers. Therefore, the stability and accuracy of the currently determined internal resistance are poor.

[0118] On the other hand, the current method of determining internal resistance requires active control of the battery through current pulses, so it is not suitable for some big data scenarios where the battery cannot be actively controlled and only the battery usage process is recorded. In other words, in the battery big data scenario, there is no guarantee that the internal resistance identification opportunity that meets the battery status conditions will appear at different time points. In addition, due to the noise in data collection, processing and transmission, there will still be problems with the stability and accuracy of the determined internal resistance in the battery big data scenario.

[0119] Therefore, it is necessary to provide a method for determining internal resistance that can reduce the dependency on battery status in order to solve the above technical problems. The following describes the method for determining internal resistance.

[0120] The batteries disclosed in the embodiments of this application can be used, but are not limited to, in electrical devices such as vehicles, ships, or aircraft. A power supply system comprising the batteries disclosed in this application can be used to construct such electrical devices. This helps reduce dependence on battery status during internal resistance determination, thereby improving the stability and accuracy of internal resistance determination.

[0121] The present invention provides an electric device that uses a battery as a power source. The electric device may be, but is not limited to, a mobile phone, a tablet, a laptop computer, an electric toy, an electric tool, a battery-powered vehicle, an electric car, a ship, a spacecraft, etc. The electric toy may include a fixed or mobile electric toy, such as a game console, an electric car toy, an electric ship toy, and an electric airplane toy, etc. The spacecraft may include an airplane, a rocket, a space shuttle, and a spacecraft, etc.

[0122] For ease of explanation, the following embodiments are described using a vehicle 101 as an example of an electrical device in accordance with an embodiment of the present application. FIG1 illustrates an application environment for the internal resistance determination method in accordance with an embodiment of the present application. As shown in FIG1 , the vehicle 101 communicates with a computer device 103.

[0123] Among them, the vehicle 101 can be a fuel vehicle, a gas vehicle or a new energy vehicle, and the new energy vehicle can be a pure electric vehicle, a hybrid vehicle or an extended-range vehicle, etc. A battery 102 is provided inside the vehicle 101, and the battery 102 can be provided at the bottom, head or tail of the vehicle 101. The battery 102 can be used to power the vehicle 101, for example, the battery 102 can be used as an operating power source for the vehicle 101. The vehicle 101 may also include a controller and a motor (not shown in Figure 1), and the controller is used to control the battery 102 to power the motor, for example, for starting, navigating and driving the vehicle 101.

[0124] Computer device 103 can be located outside vehicle 101 and can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smart watches, smart bracelets, and head-mounted devices. Computer device 103 can also be implemented as a standalone server or a server cluster consisting of multiple servers.

[0125] The computer device 103 may also be set inside the vehicle 101, which includes but is not limited to a central processing unit (CPU), and may also include a digital signal processor (DSP), a field programmable gate array (FPGA) or other programmable logic devices.

[0126] In some embodiments of the present application, the battery 102 can serve not only as an operating power source for the vehicle 101 , but also as a driving power source for the vehicle 101 , replacing or partially replacing fuel or natural gas to provide driving power for the vehicle 101 .

[0127] Figure 2 is a flow chart of the internal resistance determination method in an embodiment of the present application. In an exemplary embodiment, as shown in Figure 2, an internal resistance determination method is provided, which is illustrated by taking the method applied to the computer device in Figure 1 as an example, including the following S201 to S203.

[0128] S201 , obtaining multiple sampling data groups of cells in a battery to be tested; the sampling data groups include sampling data at two moments.

[0129] The battery under test includes at least one battery cell. The sampled data includes the voltage and current of the battery cell at different time points. In some embodiments, the sampled data can be data acquired by a computer device from a server, cloud platform, or other device, or can be data pre-stored in the computer device.

[0130] Sampled data can be a time-dependent sequence, including current and voltage. Taking voltage as an example, sampled data might include voltage 1 at time point 1, voltage 2 at time point 2, voltage 3 at time point 3, and so on. The length of sampled data can be customized. For example, a computer device might retrieve sampled data for a cell one month prior to the current time.

[0131] In some embodiments, a computer device can acquire multiple sampling data groups of a battery cell. A sampling data group includes sampling data at two moments, and the sampling data at two moments can be data from two adjacent time points in the sampling data. For example, sampling data group 1 includes sampling data at time point 1 and sampling data at time point 2, sampling data group 2 includes sampling data at time point 2 and sampling data at time point 3, sampling data group 3 includes sampling data at time point 3 and sampling data at time point 4, and so on.

[0132] In some embodiments, the sampled data at two moments may also be data from two non-adjacent time points in the sampled data. For example, sampled data group 1 includes sampled data at time point 1 and sampled data at time point 3, sampled data group 2 includes sampled data at time point 2 and sampled data at time point 4, sampled data group 3 includes sampled data at time point 3 and sampled data at time point 5, and so on.

[0133] S202 , determining a plurality of initial data variations according to a plurality of sampling data groups; the initial data variations include current variations and voltage variations.

[0134] In some embodiments, a computer device can determine multiple initial data changes based on multiple sampled data sets. That is, for a sampled data set, the initial data change corresponding to the sampled data set can be obtained by subtracting the current at two moments in the sampled data set to obtain the current change, and by subtracting the voltage at two moments in the sampled data set to obtain the voltage change. It will be appreciated that each sampled data set corresponds to one initial data change.

[0135] Continuing with the above example, the initial data change 1 is obtained based on the sampling data group 1. The initial data change 1 includes the current change 1 and the voltage change 1. The current change 1 is determined based on the current at time point 1 and the current at time point 2. The voltage change 1 is determined based on the voltage at time point 1 and the voltage at time point 2.

[0136] The initial data change 2 is obtained according to the sampling data group 2. The initial data change 2 includes the current change 2 and the voltage change 2. The current change 2 is determined according to the current at time point 2 and the current at time point 3. The voltage change 2 is determined according to the voltage at time point 2 and the voltage at time point 3, and so on.

[0137] S203 , fitting each initial data variation to obtain a voltage fitting value corresponding to the current variation in each initial data variation.

[0138] Since the state of the battery to be tested may change, and the state of the battery to be tested may also cause errors in the initial data variation, in this embodiment, each initial data variation is fitted to obtain a voltage fitting value corresponding to the current variation in each initial data variation.

[0139] In this embodiment, assuming that the computer device obtains 200 initial data changes, recorded as initial data change 1 to initial data change 200, the computer device can fit the initial data change 1 to initial data change 200 to obtain the voltage fitting amount 1* corresponding to the current change 1 in the initial data change 1, the voltage fitting amount 2* corresponding to the current change 2, and... the voltage fitting amount 200* corresponding to the current change 200.

[0140] In some embodiments, the computer device can calculate the goodness of fit during the fitting process of the initial data changes, and stop fitting when the goodness of fit reaches a preset goodness, thereby obtaining the voltage fitting value corresponding to the current change value in each initial data change value. The goodness of fit reaching the preset goodness indicates that there are principal components with similar battery operating conditions between the initial data changes, which helps to improve the accuracy of the target data change value.

[0141] S204 , screening each initial data variation to obtain a target data variation according to the voltage variation and the voltage fitting value corresponding to the current variation in each initial data variation.

[0142] In some embodiments, for each initial data variation, the current variation corresponds to a voltage variation and a voltage fitting value. For example, in initial data variation 1, current variation 1 corresponds to voltage fitting value 1* and voltage variation 1, and in initial data variation 2, current variation 2 corresponds to voltage fitting value 2* and voltage variation 2.

[0143] The computer device can then filter each initial data variation based on the voltage variation corresponding to the current variation in each initial data variation and the voltage fitting value to obtain a target data variation. For example, the computer device can calculate the difference between the voltage variation corresponding to the current variation in each initial data variation and the voltage fitting value, and use the initial data variation with a difference smaller than a preset difference as the target data variation.

[0144] For example, assuming that the computer device obtains 200 initial data changes based on two adjacent sampling data of the battery cell, and fits each initial data change, and obtains 100 target data changes after screening, then the 100 target data changes can be recorded in chronological order as (ΔU1, ΔI1), (ΔU2, ΔI2),... (ΔU100, ΔI100).

[0145] S205 , determining the internal resistance of the battery cell according to the target data variation.

[0146] In this embodiment, after obtaining the target data change, the computer device can determine the internal resistance of the battery cell according to the target data change, wherein the internal resistance of the battery cell includes but is not limited to direct current resistance (DCR).

[0147] In some embodiments, the computer device can determine the internal resistance of the battery cell according to the following formula (1), wherein ΔU represents the voltage change, ΔI represents the current change, and DCR represents the internal resistance. For example, the computer device can fit the target data change to obtain a fitting curve, and use the slope of the fitting curve as the internal resistance of the battery cell. ΔU≈DCR*ΔI (1)

[0148] In the above-mentioned internal resistance determination method, since the sampling data group includes sampling data at two moments, after obtaining multiple sampling data groups of the battery cells in the battery to be tested, multiple initial data changes including current changes and voltage changes can be determined based on the multiple sampling data groups. In addition, since each initial data change is fitted, the voltage fitting amount corresponding to the current change in each initial data change is obtained, and based on the voltage change and voltage fitting amount corresponding to the current change in each initial data change, each initial data change is screened to obtain the target data change. Therefore, the degree of influence of the battery state on the initial data change can be reduced to improve the stability and accuracy of the obtained target data change. Based on this, in the process of determining the internal resistance, this embodiment does not need to wait for the battery to be in a stable battery state, but can determine a relatively stable and accurate internal resistance of the battery cell based on the target data change, further reducing the dependence on the battery state, expanding the application scenarios for determining the internal resistance, and being more flexible.

[0149] FIG3 is a flow chart of obtaining a target data variation in an embodiment of the present application. In an exemplary embodiment, as shown in FIG3 , S204 includes S301 to S302 .

[0150] S301 , determining the absolute value of the residual between the voltage variation corresponding to the current variation in each initial data variation and the voltage fitting value.

[0151] In this embodiment, continuing with the above example, the computer device can determine the absolute values ​​1 to 200 corresponding to the initial data change 1 to the initial data change 200, respectively, where the absolute value 1 is the absolute value of the residual between the voltage fitting amount 1* and the voltage change 1, the absolute value 2 is the absolute value of the residual between the voltage fitting amount 2* and the voltage change 2, and so on, the absolute value 200 is the absolute value of the residual between the voltage fitting amount 200* and the voltage change 200.

[0152] S302: Taking the initial data variation corresponding to the absolute value not less than the reference residual as the target data variation.

[0153] The reference residual may be a value determined by the computer device in response to an input operation, a value sent to the computer device by other devices, or a value determined by the computer device based on the absolute value of the residual corresponding to each initial data change.

[0154] For example, the computer device may use the 75th percentile of the residual of each initial data change as the reference residual. Assuming that the absolute values ​​51 to 150 are greater than or equal to the reference residual, and the absolute values ​​1 to 50 and the absolute values ​​151 to 200 are not greater than the reference residual, the computer device uses the initial data change 51 to the initial data change 150 as the target data change (ΔU1, ΔI1), (ΔU2, ΔI2), ... (ΔU100, ΔI100) in sequence.

[0155] This embodiment determines the absolute value of the residual between the voltage change corresponding to the current change in each initial data change and the voltage fitting value. Since the initial data change corresponding to the absolute value not less than the reference residual is used as the target data change, the accuracy of the target data change is improved.

[0156] In an exemplary embodiment, S205 may be implemented as follows:

[0157] The internal resistance of the battery cell is determined according to the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount; wherein the sampling parameters include at least one of an environmental parameter and a state of charge.

[0158] In this embodiment, the sampled data further includes sampling parameters of the battery cell, and the sampling parameters include at least one of an environmental parameter and a state of charge (SOC). The environmental parameter includes but is not limited to the temperature of the battery cell.

[0159] Taking the target data change (ΔU1, ΔI1) as an example, assuming that ΔU1 is determined based on the voltage of the battery cell at time point 50 and the voltage at time point 49, and ΔI1 is determined based on the current of the battery cell at time point 50 and the current at time point 49, then the sampling parameters corresponding to ΔU1, ΔI1) can be determined according to the sampling parameters corresponding to any time point in [time point 49, time point 50]. For example, the computer device uses the temperature and SOC of the battery cell at time point 49 as the sampling parameters corresponding to (ΔU1, ΔI1).

[0160] In some embodiments, the computer device may also determine the sampling parameters corresponding to (ΔU1, ΔI1) based on the sampling parameters corresponding to at least two time points [time point 49, time point 50]. For example, the computer device uses the average temperature and the average SOC corresponding to time points 49 to 50 as the sampling parameters corresponding to (ΔU1, ΔI1).

[0161] Furthermore, the computer device can determine the internal resistance of the battery cell based on the target data change and the sampling parameters in the sampled data corresponding to the target data change. For example, the computer device can perform principal component analysis on the target data change and the sampled data corresponding to the target data change to obtain an analysis result, fit the analysis result to obtain a fitting curve, and determine the internal resistance of the battery cell based on the slope of the fitting curve.

[0162] In this embodiment, since the sampling parameters include at least one of the environmental parameters and the state of charge, at least one of the environmental parameters and the state of charge can be taken into account in determining the internal resistance of the battery cell based on the target data change and the sampling parameters in the sampling data corresponding to the target data change, which is beneficial to reducing the influence of the state of the battery to be tested and improving the accuracy of the determined internal resistance.

[0163] FIG4 is a schematic diagram of a flow chart of determining internal resistance in an embodiment of the present application. In an exemplary embodiment, as shown in FIG4 , S205 includes S401 to S403 .

[0164] S401 , fitting the data to be fitted to obtain a fitting curve; the data to be fitted includes a target data variation, or the data to be fitted includes the target data variation and sampling parameters in the sampling data corresponding to the target data variation.

[0165] In order to more clearly illustrate the internal resistance determination method of the present application, the derivation principle is combined here for explanation. Given a time point t1 < t2, the dynamic voltage of any cell in the battery to be tested can be expressed as the following formula (2). In formula (2), U t1 and U t2 are the voltages of the cell at time t1 and time t2, I t1 and I t2They are the current of the cell at time t1 and time t2, DCR t1 and DCR t2 They are the internal resistance of the cell at time t1 and time t2, OCV t1 and OCV t2 are the open circuit voltages of the cell at time t1 and time t2 respectively. t1 =I t1 DCR t1 +OCV t1 , U t2 =I t2 DCR t2 +OCV t2 (2)

[0166] According to formula (2), the voltage difference ΔU between the battery cell at time point t1 and time point t2 is shown in formula (3). ΔU=I t2 DCR t2 -I t1 DCR t1 +OCV t2 -OCV t1 (3)

[0167] Since the sampling parameters such as temperature and SOC do not change much in a very short time, we limit t1≈t2 to make the time interval between two time points as small as possible, and we can get the following formula (4). t1 ≈OCV t2 , DCR t1 ≈DCR t2 (4)

[0168] Based on equations (3) and (4), we can get the following equation (5). and It is the infinitesimal difference in DCR and OCV between t1 and t2.

[0169] For the convenience of description, Since the internal resistance of the battery cell is affected by factors such as temperature, current change, and cumulative charging time, the open circuit voltage of the battery cell will also be affected by factors such as temperature and cumulative charging time. Therefore, the uncertainty of the working conditions of the battery cell will cause changes in δ. At the same time, the process of data collection, transmission, and acquisition will also bring certain errors. Since multiple uncertain factors overlap and influence each other, it can be assumed that the disturbance term δ obeys a certain normal distribution δ~N(μ,σ), and the following formula (6) can be obtained. Where ΔU=U t2 -U t1 , ΔI=I t2 -I t1 ΔU≈DCRt1 *ΔI+μ+ε,ε~N(0,σ) (6)

[0170] It can be seen that in a very short time, the current change and voltage change of the battery cell approximately satisfy the above formula (6). μ and ε are disturbance factors, which are related to the working conditions of the battery cell. Different working conditions correspond to different μ and ε. In some embodiments, combined with formula (6), the battery states corresponding to each initial data change are different, and μ and ε also have differences. When the impact of the battery state between the initial data changes is large, the linear relationship formed by the initial data changes will become small. Therefore, in the above embodiment, the principal component extraction of each initial data change helps to reduce the impact of the difference in battery state on the internal resistance.

[0171] Based on this, the computer device also determines the impedance extraction model shown in formula (6), wherein the computer device can determine the target disturbance factor according to the sampling parameters and the first preset relationship, and determine the first difference between the voltage change in the target data change and the target disturbance factor, so as to determine the ratio between the first difference and the current change in the target data change, and determine the impedance extraction model based on the ratio. The first preset relationship is used to characterize the correspondence between different sampling parameters and different disturbance factors. For example, assuming that the sampling parameters corresponding to (ΔU1, ΔI1) include temperature 1 and SOC1, and in the first preset relationship, temperature 1 and SOC1 correspond to μ1 and ε1, then the computer device determines the target disturbance factors of (ΔU1, ΔI1 to be μ1 and ε1, and so on.

[0172] Thus, in one embodiment, after obtaining the target data variation and the sampling parameters corresponding to the target data variation, the computer device can fit the target data variation and the sampling parameters in the sampled data corresponding to the target data variation to obtain a fitting curve. In other words, the computer device fits the relationship between ΔU, ΔI, temperature, and SOC in the target data variation and the corresponding sampling parameters, thereby obtaining a fitting curve that satisfies equation (7).

[0173] In one embodiment, the computer device may also fit the change amount of each target data according to formula (1) to obtain a fitting curve.

[0174] S402 : Determine the internal resistance of the battery cell according to the slope of the fitting curve.

[0175] In some embodiments, based on formula (1) or formula (6), the internal resistance of the battery cell can be determined according to the slope of the fitting curve obtained in S601. For example, the computer device can directly use the slope of the fitting curve obtained in S601 as the internal resistance of the battery cell.

[0176] In this embodiment, since the data to be fitted includes the target data variation, or the target data variation and the sampling parameters in the sampled data corresponding to the target data variation, after fitting the data to be fitted to obtain a fitting curve, the internal resistance of the battery cell can be determined based on the slope of the fitting curve, thereby improving the efficiency of determining the internal resistance. Furthermore, since the target data variation and the sampling parameters corresponding to the target data variation can be used with relatively high accuracy, at least one of the environmental parameters and the state of charge can be taken into account in determining the internal resistance of the battery cell, which can also reduce dependence on the battery state and improve the accuracy of the determined internal resistance.

[0177] FIG5 is a flow chart of obtaining a detection result in an embodiment of the present application. In an exemplary embodiment, as shown in FIG5 , the internal resistance determination method further includes steps S501 to S503 .

[0178] S501 , for the target data variation of each cell of the battery to be tested, group the target data variation according to time windows to obtain the target data variation corresponding to each time window.

[0179] In this embodiment, assuming that the battery to be tested includes three cells, namely cell A to cell C, the computer device can obtain the target data change of cell A after fitting and screening multiple sampling data groups of cell A according to the methods of S201 to S205. Similarly, the computer device can also obtain the target data change of cell B and the target data change of cell C.

[0180] Then, for the target data change of each cell of the battery under test, the computer device can group the target data change according to the time window, and obtain the target data change corresponding to each time window. The time window can be understood as a certain length of time, which can be set as needed. For example, if the time window is 7 days, the computer device will divide the target data change of each cell into a group every 7 days.

[0181] Continuing with the above example, assume that the target data changes of cell A are (ΔU1, ΔI1) to (ΔU100, ΔI100) in order from far to near in time. After grouping the target data changes of cell A according to the time window, (ΔU1, ΔI1) to (ΔU30, ΔI30) are group 1, (ΔU31, ΔI31) to (ΔU65, ΔI65) are group 2, and (ΔU66, ΔI66) to (ΔU100, ΔI100) are group 3. Group 1 contains the target data changes of cell A corresponding to the first time window, group 2 contains the target data changes of cell A corresponding to the second time window, and group 3 contains the target data changes of cell A corresponding to the third time window.

[0182] It is understandable that, among the three time windows mentioned above, the first time window is the time window closest to the current time, and the third time window is the time window farthest from the current time. The same applies to other cells and will not be repeated here.

[0183] S502 : Determine the internal resistance of the battery cell corresponding to each time window based on the target data variation corresponding to each time window.

[0184] The computer device can then determine the internal resistance of the battery cell in each time window based on the target data change corresponding to each time window. In some embodiments, the computer device can determine the internal resistance of the battery cell based on the target data change corresponding to each time window and the sampling parameters in the sampled data corresponding to the target data change.

[0185] For example, the computer device can fit (ΔU1, ΔI1) to (ΔU30, ΔI30) and the corresponding sampling data to obtain a fitting curve, and use the slope of the fitting curve as the internal resistance A1 of battery cell A in the first time window. Similarly, the computer device can obtain the internal resistance A2 of battery cell A in the second time window and the internal resistance A3 of the third time window. Similarly, the computer device can also obtain the internal resistance B1 of battery cell B in the first time window, the internal resistance B2 in the second time window, and the internal resistance B3 in the third time window, as well as the internal resistance C1 of battery cell C in the first time window, the internal resistance C2 in the second time window, and the internal resistance C3 in the third time window.

[0186] The process of determining the internal resistance of the battery cell corresponding to each time window may refer to the above embodiment and will not be described in detail here.

[0187] S503 : Detect each battery cell based on the internal resistance of each battery cell in each time window to obtain a detection result.

[0188] In some embodiments, the computer device can detect each battery cell based on the internal resistance of each battery cell in each time window to obtain a test result. The test result may include a normal test result and an abnormal test result. A normal test result indicates that the battery cell is normal, and an abnormal test result indicates that the battery cell is abnormal.

[0189] For example, the computer device may determine a reference internal resistance based on the internal resistance of each cell in each time window. If the internal resistance of the same cell in each time window exceeds the reference internal resistance by a predetermined number, the test result for that cell is determined to be abnormal. The predetermined number is an integer greater than 0. The computer device may also analyze the changes in the values ​​of each cell of the battery under test across all time windows and determine the test results for each cell based on the changes.

[0190] This embodiment can group the target data changes for each cell of the battery under test according to time windows, obtain the target data changes corresponding to each time window, and determine the internal resistance of the cell in each time window based on the target data changes corresponding to each time window. Based on the internal resistance of each cell in each time window, each cell can be tested and a test result can be obtained. Because the determined internal resistance is relatively stable and accurate, the accuracy of the test results determined based on internal resistance is also improved.

[0191] It can be understood that S501 to S503 list the process of directly grouping the target data changes to obtain the target data changes corresponding to each time window. In some embodiments, the computer device may also first group the initial data changes to obtain the initial data changes corresponding to each time window, and then fit and filter the initial data changes corresponding to each time window to obtain the target data changes corresponding to each time window.

[0192] FIG6 is a schematic diagram of another process for obtaining a detection result in an embodiment of the present application. In an exemplary embodiment, as shown in FIG6 , S503 includes S601 to S604 .

[0193] S601 , determining a reference internal resistance based on the internal resistance of each battery cell in the same time window.

[0194] In this embodiment, for the same battery to be tested, the computer device can determine the reference internal resistance based on the internal resistance corresponding to each battery cell in the same time window. Continuing with the above example where the battery to be tested includes battery cell A, battery cell B, and battery cell C. In the first time window, based on the internal resistance A1 of battery cell A, the internal resistance B1 of battery cell B, and the internal resistance C1 of battery cell C, the computer device can determine the reference internal resistance of the first time window. In the second time window, based on the internal resistance A2 of battery cell A, the internal resistance B2 of battery cell B, and the internal resistance C2 of battery cell C, the computer device can determine the reference internal resistance of the second time window. The same applies to other time windows and will not be repeated here. It is understandable that the reference internal resistance of each time window may be different.

[0195] The reference internal resistance may be an average or weighted average of the internal resistances of the battery cells in the same time window, or a median of the internal resistances of the battery cells in the same time window. For example, taking the first time window as an example, the computer device may use the median of the internal resistances A1, B1, and C1 as the reference internal resistance for the first time window.

[0196] S602 , determining the internal resistance difference between the internal resistance corresponding to each battery cell in the same time window and the reference internal resistance.

[0197] In this embodiment, continuing with the above example, let the reference internal resistance of the first time window be S1, then the computer device will determine the internal resistance difference A1-S1 between the internal resistance A1 of battery cell A and the reference internal resistance S1, the internal resistance difference B1-S1 between the internal resistance B1 of battery cell B and the reference internal resistance S1, and the internal resistance difference C1-S1 between the internal resistance C1 of battery cell C and the reference internal resistance S1.

[0198] Similarly, assuming that the reference internal resistance of the second time window is S2, the computer device will determine the internal resistance difference A2-S2 between the internal resistance A2 of battery cell A and the reference internal resistance S2, the internal resistance difference B2-S2 between the internal resistance B2 of battery cell B and the reference internal resistance S2, and the internal resistance difference C2-S2 between the internal resistance C2 of battery cell C and the reference internal resistance S2.

[0199] Assuming the reference internal resistance in the second time window is S3, the computer device will determine the internal resistance difference A3-S3 between the internal resistance A3 of battery cell A and the reference internal resistance S3, the internal resistance difference B3-S3 between the internal resistance B3 of battery cell B and the reference internal resistance S3, and the internal resistance difference C3-S3 between the internal resistance C3 of battery cell C and the reference internal resistance S3. In this way, the internal resistance difference between the internal resistance corresponding to each battery cell in the same time window and the reference internal resistance is determined.

[0200] S603 : For each battery cell, determine the relative internal resistance vector of the battery cell according to the internal resistance difference corresponding to the battery cell in each time window.

[0201] In this embodiment, for the same battery cell, the computer device may arrange the internal resistance differences of the battery cell corresponding to each time window in the order of the time windows to obtain the relative internal resistance vector of the battery cell.

[0202] Continuing with the above example, the relative internal resistance vector of battery cell A can be (A1-S1, A2-S2, A3-S3), the relative internal resistance vector of battery cell B can be (B1-S1, B2-S2, B3-S3), and the relative internal resistance vector of battery cell C can be (C1-S1, C2-S2, C3-S3).

[0203] S604: Detect each battery cell according to the relative internal resistance vector of each battery cell to obtain a detection result.

[0204] In some embodiments, the computer device may detect each battery cell based on the relative internal resistance vector of each battery cell and a preset risk threshold to obtain a test result. For example, if the relative internal resistance vector of battery cell A contains a continuous number of internal resistance differences that are greater than the preset risk threshold, the computer device may determine that the test result of battery cell A is an abnormal test result.

[0205] This embodiment can determine a reference internal resistance based on the internal resistance of each cell in the same time window, and determine the internal resistance difference between the internal resistance of each cell in the same time window and the reference internal resistance. Therefore, for each cell, the relative internal resistance vector of the cell can be determined based on the internal resistance difference of the cell in each time window. Subsequently, based on the relative internal resistance vector of each cell, each cell can be tested to obtain a relatively accurate test result.

[0206] In other words, given different target data changes, the corresponding operating condition effects of the fitted internal resistance are not exactly the same. Therefore, if the internal resistance of each battery cell is directly compared to identify abnormal cells, the influence of operating condition noise will be present. Therefore, under the assumption that the operating conditions experienced by different battery cells are similar, the internal resistance difference between the internal resistance of multiple batteries and the reference internal resistance is extracted. By using the relative internal resistance vector, the differences in operating condition effects between different target data changes can be further reduced, thus unifying the operating condition dimension for comparing internal resistance changes.

[0207] FIG7 is a schematic diagram of another process for obtaining a detection result in an embodiment of the present application. In an exemplary embodiment, as shown in FIG7 , S604 includes S701 to S702 .

[0208] S701 : Determine an abnormal threshold value of each battery cell according to the relative internal resistance vector of each battery cell.

[0209] In this embodiment, in some embodiments, for the same battery cell, the computer device may use the quantile, average value, or weighted average value between the internal resistance differences of the relative internal resistance vector of the battery cell as the abnormality threshold of the battery cell.

[0210] Continuing with the above example, the computer device determines the abnormality threshold for cell A based on the relative internal resistance vector of cell A. It determines the abnormality threshold for cell C based on the relative internal resistance vector of cell B, and further determines the abnormality threshold for cell C based on the relative internal resistance vector of cell C. In other words, the abnormality thresholds for each cell of the battery under test may be different.

[0211] S702 : Based on the relative internal resistance vector and the abnormality threshold of each battery cell, each battery cell is tested to obtain a test result.

[0212] In this embodiment, the computer device can detect each battery cell based on the relative internal resistance vector and the abnormality threshold of the same battery cell to obtain a detection result. For example, based on the relative internal resistance vector of battery cell A and the abnormality threshold of battery cell A, if the relative internal resistance vector of battery cell A contains a continuous number of internal resistance differences that are greater than a preset risk threshold, the computer device will determine that the detection result of battery cell A is an abnormal detection result.

[0213] Since the abnormality threshold of each cell is determined based on the relative internal resistance vector of each cell, this embodiment improves the accuracy of the abnormality threshold of each cell. Furthermore, based on the relative internal resistance vector and abnormality threshold of each cell, accurate detection results can be obtained by testing each cell.

[0214] FIG8 is a flowchart of determining an abnormality threshold in an embodiment of the present application. In an exemplary embodiment, as shown in FIG8 , S701 includes S801 to S804 .

[0215] S801 , for each battery cell, determining a first quantile and a second quantile based on the absolute value of each internal resistance difference in the relative internal resistance vector of the battery cell; the first quantile is greater than the second quantile.

[0216] Taking cell A as an example, the computer device determines the first quantile among |A1-S1|, |A2-S2|, and |A3-S3|, which is recorded as And determine the second quantile among |A1-S1|, |A2-S2|, |A3-S3|, denoted as

[0217] in, Represents the absolute value of the internal resistance difference in the relative internal resistance vector of cell A, namely |A1-S1|, |A2-S2|, |A3-S3|. up and q low are the multiquantiles of a given distribution, q up >q low For example, q up =75,q low =25, that is, the first quantile can be The third quartile of The first quartile of .

[0218] S802: Determine a target quantile range according to a second difference between the first quantile and the second quantile.

[0219] Continuing with cell A as an example, the computer device can determine the target percentile distance iqr according to the following formula (7).

[0220] S803 , determining an average value of each internal resistance difference in the relative internal resistance vector of the battery cell.

[0221] Continuing with cell A as an example, the computer device determines the average value of the internal resistance differences in the relative internal resistance vector of cell A.

[0222] S804 , determining an abnormal threshold value of the battery cell according to a sum of the average value and a first product; the first product is determined according to a product of a first preset multiple and a target percentile range.

[0223] In some embodiments, continuing to take cell A as an example, the computer device determines the first product c*iqr based on the product of the first preset multiple c and the target quantile range iqr, and calculates the product based on the average value. The sum of the result and the first product c*iqr determines the abnormal threshold of the battery cell.

[0224] For example, the computer device can determine the abnormal threshold value of cell A according to the following formula (8): A In some embodiments, the computer device can also determine the abnormal threshold value of cell A by multiplying the empirical coefficient based on formula (8): A , the empirical coefficient can be between 0 and 1.

[0225] It is understandable that the above description is based on the calculation of the abnormal threshold of battery cell A as an example. The calculation principles of the abnormal thresholds of other battery cells are the same and will not be repeated here.

[0226] This embodiment determines, for each battery cell, a first quantile and a second quantile based on the absolute values ​​of the internal resistance differences in the relative internal resistance vector of the battery cell. A target quantile range is determined based on the second difference between the first and second quantiles. The average value of the internal resistance differences in the relative internal resistance vector of the battery cell is then determined, and the abnormality threshold of the battery cell is determined based on the sum of the average value and a first product. The first quantile is greater than the second quantile, and the first product is determined by multiplying a first preset multiple by the target quantile range. This improves the accuracy of the abnormality threshold for each battery cell.

[0227] In an exemplary embodiment, S802 may be implemented as follows:

[0228] For each battery cell, if there is an internal resistance difference greater than an abnormal threshold value of the battery cell in the relative internal resistance vector of the battery cell, the detection result of the battery cell is determined to be an abnormal detection result.

[0229] Continuing with cell A as an example, if at least one of A1-S1, A2-S2, or A3-S3 is greater than the abnormal threshold of cell A, A , that is, there is an abnormal threshold greater than the threshold of cell A in A1-S1, A2-S2 or A3-S3 A The internal resistance difference is determined by the computer device, and the detection result of battery cell A is determined to be an abnormal detection result.

[0230] On the contrary, if there is no internal resistance difference greater than the abnormal threshold value of the battery cell in the relative internal resistance vector of the battery cell, the computer device may determine that the detection result of the battery cell is a normal detection result.

[0231] In this embodiment, for each battery cell, when there is an internal resistance difference greater than the abnormal threshold value of the battery cell in the relative internal resistance vector of the battery cell, the detection result of the battery cell is determined to be an abnormal detection result, thereby improving the efficiency of determining the abnormal detection result.

[0232] In an exemplary embodiment, the above-mentioned “determining that the detection result of the battery cell is an abnormal detection result” can also be achieved in the following manner:

[0233] If there are N consecutive internal resistance differences greater than the abnormal threshold, and the N internal resistance differences greater than the abnormal threshold show an increasing trend, the test result of the battery cell is determined to be an abnormal test result; where N is an integer greater than 1.

[0234] In this embodiment, N is set according to needs and is an integer greater than 1. The increasing trend may mean that the internal resistance difference in a subsequent time window is always greater than the internal resistance difference in a previous time window, or a certain proportion of the internal resistance difference is always greater than the internal resistance difference in the previous time window.

[0235] For example, taking N=2, if, among the relative internal resistance vectors (A1-S1, A2-S2, A3-S3) of cell A, both A1-S1 and A2-S2 are greater than the abnormal threshold of cell A, and A2-S2 is greater than A1-S1, then it means that there are two consecutive internal resistance differences greater than the abnormal threshold in the relative internal resistance vector of cell A, and the two internal resistance differences greater than the abnormal threshold are on an increasing trend. In this case, the computer device can determine that the detection result of cell A is an abnormal detection result.

[0236] If, among the relative internal resistance vectors (A1-S1, A2-S2, A3-S3) of cell A, both A1-S1 and A3-S3 are greater than the internal resistance difference of cell A's abnormality threshold, but A2-S2 is less than the abnormality threshold of cell A, then there are no two consecutive internal resistance differences greater than the abnormality threshold in the relative internal resistance vectors of cell A. In this case, the computer device can determine that the test result of cell A is not an abnormal test result.

[0237] In this embodiment, the detection result of the battery cell is determined to be an abnormal detection result only when there are N consecutive internal resistance differences greater than the abnormal threshold, and the N internal resistance differences greater than the abnormal threshold show an increasing trend; since N is an integer greater than 1, the accuracy of the abnormal detection result is improved.

[0238] Figure 9 is a schematic diagram of a process for determining an abnormal detection result in an embodiment of the present application. In Figure 9, the horizontal axis represents the time window, and the vertical axis represents the internal resistance difference of the battery cell. As shown in Figure 9, the internal resistance vectors of the battery cells are arranged in the order of the time windows as the relative internal resistance characteristics of the battery cells. The computer device can determine the abnormal threshold of the battery cell based on a certain proportion of the relative internal resistance vectors of the battery cell before the current time. For example, the abnormal threshold is determined based on the relative internal resistance vector of the time window of 2023 / 9 / 3. In some embodiments, when the internal resistance difference in the relative internal resistance vector continues to be greater than the abnormal threshold and shows an increasing trend, the computer device determines that the battery cell has experienced a continuous outlier abnormality, thereby determining that the detection result of the battery cell is an abnormal detection result.

[0239] In an exemplary embodiment, the internal resistance determination method further includes the following steps:

[0240] When the detection result of the battery cell is an abnormal detection result, the risk quantification value of the battery cell is determined according to the abnormal parameters and abnormal thresholds of the battery cell; wherein, the abnormal parameters include abnormal time, target internal resistance difference, and time span; the abnormal time is based on the time point when the last internal resistance difference greater than the abnormal threshold is greater than the abnormal threshold among N consecutive internal resistance differences greater than the abnormal threshold, the target internal resistance difference includes the last internal resistance difference greater than the abnormal threshold among N consecutive internal resistance differences greater than the abnormal threshold, and the time span is the time span between N consecutive internal resistance differences greater than the abnormal threshold and the last time point corresponding to the sampling data.

[0241] In this embodiment, continuing to take battery cell A as an example, when the detection result of battery cell A is an abnormal detection result, the computer device also determines the abnormal parameters of battery cell A.

[0242] Abnormal parameters include abnormal time, target internal resistance difference, and time span. Among them, the abnormal time is determined based on the time window closest to the current time in the time windows of N internal resistance differences. For example, the computer device can use any time point in the time window closest to the current time in the time windows of N internal resistance differences as the abnormal time. Taking battery cell A as an example, assuming that the relative internal resistance vector of battery cell A includes internal resistance difference 1 to internal resistance difference 8, and the time windows corresponding to internal resistance difference 1 to internal resistance difference 8 are in chronological order from far to near. If internal resistance difference 1, internal resistance difference 4 to internal resistance difference 6, and internal resistance difference 8 are all greater than the abnormal threshold of battery cell A, and internal resistance difference 4 to internal resistance difference 6 show an increasing trend, the computer device can use any time point in the time window corresponding to internal resistance difference 6 as the abnormal time.

[0243] The target internal resistance difference is any one of the N internal resistance differences. In some embodiments, the target internal resistance difference may be the internal resistance difference corresponding to the time window closest to the current time among the N internal resistance differences. Continuing with the above example, if internal resistance difference 1, internal resistance difference 4 to internal resistance difference 6, and internal resistance difference 8 are all greater than the abnormal threshold of battery cell A, and internal resistance difference 4 to internal resistance difference 6 show an increasing trend, the computer device may use internal resistance difference 6 as the target internal resistance difference.

[0244] The time span is the duration between the time window corresponding to the target internal resistance difference and the maximum sampling time of the sampled data. The maximum sampling time of the sampled data can be the maximum sampling time point of the original sampled data acquired by the computer device, or the maximum sampling time point of the sampled data after processing and filtering the original sampled data.

[0245] For example, assume that the computer device obtains sampling data of battery cell A between January 1, 2021 and December 31, 2021, and based on the sampling data, determines that the abnormal time of battery cell A is August 1, 2023 according to the steps of the above embodiment. If the time window corresponding to the target internal resistance difference is August 1, 2023, and the maximum sampling time of the sampling data is December 31, 2021, then the time span is the length of time between August 1, 2023 and December 31, 2021.

[0246] In some embodiments, the computer device can determine the risk quantification value of the battery cell based on the abnormal parameters and abnormal thresholds of the battery cell.

[0247] In some embodiments, the computer device can determine the degree of abnormality of the battery cell based on the ratio between the third difference and the abnormality threshold of the battery cell, and determine the time expansion factor of the battery cell based on a linear or exponential function between the time span and the abnormal time, and then determine the risk quantification value of the battery cell based on the degree of abnormality of the battery cell and the time expansion factor. The third difference can be the difference between the target internal resistance difference and the abnormality threshold of the battery cell. The computer device can also analyze and calculate the abnormal parameters and abnormality thresholds of the battery cell using a preset model to determine the risk quantification value of the battery cell.

[0248] In this embodiment, the abnormal parameters include abnormal time, target internal resistance difference, and time span; the abnormal time is determined based on the time window closest to the current time among the N internal resistance difference time windows, the target internal resistance difference is any internal resistance difference among the N internal resistance differences, and the time span is the length of time between the time window corresponding to the target internal resistance difference and the maximum sampling time of the sampling data. Therefore, when the detection result of the battery cell is an abnormal detection result, a more accurate risk quantification value can be determined based on the abnormal parameters and abnormal thresholds of the battery cell.

[0249] Figure 10 is a flowchart of determining a risk quantification value in an embodiment of the present application. In an exemplary embodiment, as shown in Figure 10, the above-mentioned "determining the risk quantification value of the battery cell based on the abnormal parameters and abnormal thresholds of the battery cell" includes S1001 to S1003.

[0250] S1001, determining the degree of abnormality of the battery cell according to the ratio between the third difference and the abnormal threshold of the battery cell; the third difference is the difference between the target internal resistance difference and the abnormal threshold of the battery cell.

[0251] In this embodiment, taking cell A as an example, when determining the risk quantification value score of cell A, A During the process, the computer device will determine the target internal resistance difference ΔDCR A Abnormal threshold of cell A A The third difference between the threshold A -ΔDCR A .

[0252] Then, the computer device determines a third difference threshold A -ΔDCR A Abnormal threshold of cell A A Ratio between

[0253] Afterwards, the computer equipment Determine the abnormality level of battery cell A. In some embodiments, the computer device can directly The abnormality level of cell A.

[0254] S1002, determining a time expansion factor according to a preset power of a natural base; the preset power is the product of the second preset multiple and a fourth difference, and the fourth difference is the difference between the time span and the abnormal time.

[0255] Continuing with the example of cell A, the computer device determines the time span T A-end and abnormal time T A-abnorm The fourth difference T between A-end -T A-abnorm , and determine the preset power M ( T A-end -T A-abnorm Where M is a second preset multiple. When M > 0, it indicates that the computer device is pessimistic about the risk expansion trend. When M < 0, it indicates that the computer device is optimistic about the risk expansion trend. In other words, when M > 0, the computer device is more likely to assess the risk, and when M < 0, the computer device is less likely to assess the risk.

[0256] In some embodiments, the computer device is configured to Determine the time expansion factor F_expand(T A-end ,T A-abnorm ). For example, a computer device can directly As F_expand(T A-end ,T A-abnorm ).

[0257] S1003 , determining a risk quantification value of the battery cell according to a second product of the abnormality degree of the battery cell and the time expansion factor.

[0258] In some embodiments, the computer device is configured to With F_expand(T A-end ,T A-abnorm ) determines the risk quantification value of the battery cell.

[0259] For example, the computer device determines the risk quantification value score of the battery cell A according to the following formula (9): A .

[0260] It is understandable that the above description is based on the calculation of the risk quantification value of battery cell A. The calculation principle of the risk quantification value of other battery cells is the same and will not be repeated here.

[0261] Figure 11 is a schematic diagram of time expansion in an embodiment of the present application. As shown in Figure 11, taking battery cell A as an example, the abnormal time of battery cell A is August 1, 2023, and the maximum sampling time of the sampling data is December 31, 2021. That is to say, due to sampling processing, extraction, noise and other reasons, the data of battery cell A cannot be used for abnormality detection during the period from August 1, 2023 to December 31, 2021. Therefore, this embodiment uses the time expansion factor to time expand the risk quantification value corresponding to the abnormal time, reduce the error due to the loss from August 1, 2023 to December 31, 2021, and thus output a more accurate risk quantification value for the battery cell.

[0262] This embodiment determines the cell abnormality level of the battery cell based on the ratio between the third difference and the cell abnormality threshold, and determines the time expansion factor based on a preset power of the natural base. Since the third difference is the difference between the target internal resistance difference and the cell abnormality threshold, and the fourth difference is the difference between the time span and the abnormal time, a relatively accurate cell risk quantification value can be determined based on the second product of the cell abnormality level and the time expansion factor.

[0263] In an exemplary embodiment, the internal resistance determination method further includes the following steps:

[0264] The early warning fault level of the battery cell is determined according to the risk quantification value of the battery cell and the second preset relationship; wherein the second preset relationship is used to characterize the correspondence between different risk quantification values ​​and different early warning fault levels.

[0265] In this embodiment, continuing to take cell A as an example, the second preset relationship may include the following formula (10). i It can be a value stored in the computer device in advance, i takes 1 to N, N is an integer greater than 0. It can be understood that the larger i is, the higher the warning fault level of the battery cell and the greater the risk. A =i|(score i ≤score A ≤score i+1 ) (10)

[0266] Afterwards, the computer device can determine the warning fault level of cell A based on the risk quantification value of cell A and the above formula (10). For example, assuming score1 = 0, score2 = 5, score3 = 10, etc. A =5, then score2≤score A =5≤score3, and then the computer device will determine that the warning fault level of battery cell A is 2.

[0267] In some embodiments, the computer device may output prompt information according to the warning fault level, and the prompt information may include but is not limited to at least one of a voice broadcast, a pop-up window, a text message prompt, and a telephone prompt.

[0268] In this embodiment, since the second preset relationship is used to characterize the correspondence between different risk quantification values ​​and different warning fault levels, the warning fault level of the battery cell can be determined efficiently and accurately based on the risk quantification value of the battery cell and the second preset relationship.

[0269] In an exemplary embodiment, at least one of the reference residual and the first preset multiple can be determined based on at least one of the detection results of the sampling data of the abnormal battery cell under different candidate values, the risk quantification value, and the warning fault level.

[0270] Taking the reference residual as an example, the computer device can obtain sampling data from each cell in an abnormal battery that has already experienced an abnormality, and use the method of the above embodiment to predict the warning fault level of the abnormal battery under different candidate residuals. Using the predicted warning fault level of the abnormal battery and the actual abnormality of the abnormal battery, the computer device selects the candidate residual corresponding to the situation with high prediction accuracy or low false alarm rate as the reference residual. The same applies to the first preset multiple. This helps to improve the accuracy of the reference residual or the first preset multiple.

[0271] FIG12 is a flow chart of obtaining sampling data in an embodiment of the present application. In an exemplary embodiment, as shown in FIG12 , the internal resistance determination method further includes steps S1201 to S1203 .

[0272] S1201: Obtain first sampling data of a battery cell from a server.

[0273] In this embodiment, the server includes, but is not limited to, a server on a cloud platform. The first sampled data may include the current, voltage, and sampling parameters of each cell in the battery under test at each time point. The server may periodically send the first sampled data of the cells to the computer device, and the computer device may also request the first sampled data within a preset time period from the server.

[0274] For example, the computer device can request the server for the first sampling data of each battery cell three months before the current time point. The first sampling data includes the voltage 1, current 1, temperature 1, SOC1 of each battery cell at time point 1, the voltage 2, current 2, temperature 2, SOC2 at time point 2, and the voltage 3, current 3, temperature 3, SOC3 at time point 3, etc.

[0275] S1202: Preprocess the first sampled data to obtain second sampled data.

[0276] Preprocessing may include, but is not limited to, cleaning out abnormal values ​​from the first sampled data. Abnormal values ​​in the first sampled data include, but are not limited to, at least one of a jump value, a null value, and an out-of-range value. For example, the computer device may delete the abnormal value from the first sampled data to obtain the second sampled data. If voltage 1 at time point 1 is abnormal, the computer device may also delete current 1, temperature 1, and SOC 1 at time point 1.

[0277] S1203 : Taking the sampling data in the second sampling data, in which the temperature is not less than a preset temperature threshold and the state of charge is within a preset range, as the third sampling data.

[0278] The preset temperature threshold and the preset range can be set as needed. For example, the preset temperature threshold can be 10°C and the preset range can be [30%, 80%]. Then, the computer device will use the sampled data in the second sampled data where the temperature is not less than 10°C and the SOC is within [30%, 80%] as the third sampled data.

[0279] S1204 , taking data in the third sampling data in which adjacent current changes are greater than the first threshold and adjacent current sampling intervals are less than the second threshold as sampling data.

[0280] In some embodiments, due to factors such as errors, jitter, and noise in the transmission between the computer device and the server, the computer device may further filter the third sampling data in order to improve the accuracy of the sampling data.

[0281] The reason why the adjacent current changes are greater than the first threshold is to improve the variability of the current. The reason why the adjacent current sampling intervals are less than the second threshold is to reduce the impact of sampling information loss. The first threshold and the second threshold can be set as needed.

[0282] For example, the first threshold value can be 10A and the second threshold value can be 5 minutes. The computer device will then retain the data in the second sampling data where the adjacent current changes are greater than 10A and the adjacent current sampling intervals are less than 5 minutes as sampling data for subsequent use, and eliminate the data in the third sampling data where the current switching difference is small and the sampling time interval is too large.

[0283] After obtaining the first sampling data of the battery cell from the server, this embodiment preprocesses the first sampling data to obtain the second sampling data. Since the sampling data in the second sampling data in which the temperature is not less than the preset temperature threshold and the charge state is within the preset range is used as the third sampling data, and the data in the third sampling data in which the adjacent current changes are greater than the first threshold and the adjacent current sampling intervals are less than the second threshold are used as the sampling data, the accuracy of the sampling data is improved.

[0284] In order to more clearly introduce the internal resistance determination method in the present application, it is explained here with reference to FIG. 13 and FIG. 16 .

[0285] Figure 13 is a schematic diagram of an effect in an embodiment of the present application. As shown in Figure 13, Figure 13(a) shows the initial data changes of the battery cells. Figure 13(b) shows the process of extracting the principal components of the initial data changes in Figure 13(a) to obtain the target data changes in the rectangular frame. Figure 13(c) shows the process of fitting the target data changes in Figure 13(b) to obtain a linear curve. Figure 13(d) shows the effect of the relative internal resistance vector of each battery cell obtained based on the internal resistance determined in Figure 14(c). Figure 13(e) shows the effect of determining the internal resistance directly based on the initial data changes in Figure 13(a) and obtaining the relative internal resistance vector of each battery cell.

[0286] In Figures 13(a) and 13(b), the horizontal axis represents the current change, and the vertical axis represents the voltage change. In Figures 13(d) and 13(e), the horizontal axis represents time, and the vertical axis represents the relative internal resistance vector. Comparing Figures 13(d) and 13(e), it can be seen that because this embodiment first performs principal component extraction on the initial data changes to obtain a more accurate target data change, the resulting relative internal resistance vector is more robust and accurate, achieving better results.

[0287] FIG14 is a process diagram of a method for determining internal resistance in an embodiment of the present application. As shown in FIG14 , a computer device may execute the method according to the following process.

[0288] S1401: Obtain first sampling data of a battery cell from a server.

[0289] S1402: Preprocess the first sampled data to obtain second sampled data.

[0290] S1403 : Taking the sampling data in the second sampling data, in which the temperature is not less than a preset temperature threshold and the state of charge is within a preset range, as the third sampling data.

[0291] S1404 , taking data in the third sampling data in which adjacent current changes are greater than the first threshold and adjacent current sampling intervals are less than the second threshold as sampling data.

[0292] S1405: Acquire multiple sampling data sets of the cells in the battery to be tested, wherein the sampling data sets include sampling data at two moments.

[0293] S1406: Determine a plurality of initial data variations based on the plurality of sampled data groups, wherein the initial data variations include current variations and voltage variations.

[0294] S1407 , fitting each initial data variation to obtain a voltage fitting value corresponding to the current variation in each initial data variation.

[0295] S1408 , determining the absolute value of the residual between the voltage variation corresponding to the current variation in each initial data variation and the voltage fitting value.

[0296] S1409: Taking the initial data variation corresponding to the absolute value not less than the reference residual as the target data variation.

[0297] S1410 , for the target data variation of each cell of the battery to be tested, group the target data variation according to the time window to obtain the target data variation corresponding to each time window.

[0298] S1411 , determining the internal resistance of the battery cell corresponding to each time window based on the target data variation corresponding to each time window.

[0299] For each time window, S1411 includes S1 and S2 (not shown). S1: Fits the target data change and the sampling parameters in the sampled data corresponding to the target data change to obtain a fitting curve. S2: Determines the internal resistance of the battery cell based on the slope of the fitting curve. The sampling parameters include at least one of an environmental parameter and a state of charge.

[0300] S1412: Determine a reference internal resistance based on the internal resistance of each battery cell in the same time window.

[0301] S1413 , determining the internal resistance difference between the internal resistance corresponding to each battery cell in the same time window and the reference internal resistance.

[0302] S1414 : For each battery cell, determine the relative internal resistance vector of the battery cell according to the internal resistance difference corresponding to the battery cell in each time window.

[0303] S1415: For each battery cell, determine a first quantile and a second quantile based on the absolute value of each internal resistance difference in the relative internal resistance vector of the battery cell, wherein the first quantile is greater than the second quantile.

[0304] S1416: Determine a target quantile range based on a second difference between the first quantile and the second quantile.

[0305] S1417, determining an average value of each internal resistance difference in the relative internal resistance vector of the battery cell.

[0306] S1418: Determine an abnormal threshold value of the battery cell based on a sum of the average value and the first product, wherein the first product is determined based on the product of the first preset multiple and the target percentile range.

[0307] S1419: For each battery cell, if there are N consecutive internal resistance differences greater than the abnormal threshold, and the N internal resistance differences greater than the abnormal threshold are increasing, then the battery cell detection result is determined to be an abnormal detection result, where N is an integer greater than 1.

[0308] S1420: If the detection result of the battery cell is an abnormal detection result, determine the abnormality level of the battery cell based on the ratio between the third difference and the abnormal threshold of the battery cell, wherein the third difference is the difference between the target internal resistance difference and the abnormal threshold of the battery cell.

[0309] S1421: Determine a time expansion factor according to a preset power of a natural base, wherein the preset power is the product of the second preset multiple and a fourth difference, and the fourth difference is the difference between the time span and the abnormal time.

[0310] S1422: Determine a risk quantification value of the battery cell based on a second product of the abnormality degree of the battery cell and the time expansion factor.

[0311] S1423: Determine the early warning fault level of the battery cell according to the risk quantification value of the battery cell and a second preset relationship, wherein the second preset relationship is used to represent the correspondence between different risk quantification values ​​and different early warning fault levels.

[0312] The processes of S1401 to S1423 can refer to the above-mentioned embodiments and will not be described here in detail. It can be seen that in the internal resistance determination method provided in this embodiment, based on the sampling data of the battery, the characteristic operating condition filtering mechanism is used to extract the principal components of each of the initial data changes to obtain the target data change, and then the internal resistance is determined by multi-feature regression and multi-point fitting of the relationship between the target data change and the corresponding sampling parameters. Afterwards, the internal resistance can be used to accurately identify abnormal and outlier internal resistance faults and to provide continuous and stable safety warnings. In this way, through a feature extraction mechanism, the reliability and stability of the DC internal resistance identification results can be improved, the sensitivity of the identification results to the battery status can be reduced, the accuracy and stability of the identification results can be improved, and continuous safety warnings can be achieved.

[0313] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0314] Based on the same inventive concept, embodiments of the present application also provide an internal resistance determination device for implementing the aforementioned internal resistance determination method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the internal resistance determination device provided below can be found in the above-described limitations of the internal resistance determination method and will not be further elaborated here.

[0315] FIG15 is a structural block diagram of an internal resistance determination and adjustment device according to an embodiment of the present application. In an exemplary embodiment, as shown in FIG15 , an internal resistance determination device 1500 is provided, comprising: a first acquisition module 1501, a first determination module 1502, a fitting module 1503, a screening module 1504, and a second determination module 1505, wherein:

[0316] The first acquisition module 1501 is configured to acquire multiple sampling data groups of cells in the battery to be tested; the sampling data groups include sampling data at two moments.

[0317] The first determining module 1502 is configured to determine a plurality of initial data variation amounts according to the plurality of sampling data groups; the initial data variation amounts include current variation amounts and voltage variation amounts.

[0318] The fitting module 1503 is used to fit each of the initial data variations to obtain a voltage fitting value corresponding to the current variation in each of the initial data variations.

[0319] The screening module 1504 is configured to screen each of the initial data variations to obtain a target data variation according to the voltage variation and the voltage fitting value corresponding to the current variation in each of the initial data variations.

[0320] The second determining module 1505 is configured to determine the internal resistance of the battery cell according to the target data variation.

[0321] In the above-mentioned internal resistance determination device, since the sampling data includes voltage and current, after obtaining the initial data change between two adjacent sampling data of the battery cell in the battery to be tested, the initial data change will include the current change and the voltage change. In addition, since the principal component extraction can be performed on each initial data change to obtain the target data change, the influence of the battery state on the initial data change can be reduced to improve the stability and accuracy of the obtained target data change. Based on this, in the process of determining the internal resistance, this embodiment does not need to wait for the battery to be in a stable battery state, but can determine a relatively stable and accurate internal resistance of the battery cell based on the target data change, further reducing the dependence on the battery state, expanding the application scenarios for determining the internal resistance, and being more flexible.

[0322] FIG16 is a structural block diagram of a screening module in an embodiment of the present application. In an exemplary embodiment, the screening module 1504 includes:

[0323] The first determining unit 1601 is configured to determine an absolute value of a residual between a voltage variation corresponding to a current variation in each initial data variation and a voltage fitting value.

[0324] The second determining unit 1602 is configured to use the initial data variation corresponding to the absolute value not less than the reference residual as the target data variation.

[0325] In some embodiments, the second determination module 1505 is further used to determine the internal resistance of the battery cell based on the target data change and sampling parameters in the sampling data corresponding to the target data change; wherein the sampling parameters include at least one of environmental parameters and charge status.

[0326] FIG17 is a structural block diagram of a second determination module in an embodiment of the present application. In an exemplary embodiment, the second determination module 1505 includes:

[0327] The first fitting unit 1701 is used to fit the data to be fitted to obtain a fitting curve; the data to be fitted includes the target data variation, or the data to be fitted includes the target data variation and sampling parameters in the sampling data corresponding to the target data variation.

[0328] The third determining unit 1702 is configured to determine the internal resistance of the battery cell according to the slope of the fitting curve.

[0329] FIG18 is a structural block diagram of another internal resistance determination and adjustment device according to an embodiment of the present application. In an exemplary embodiment, the internal resistance determination device 1500 further includes:

[0330] The grouping module 1801 is configured to group the target data variation of each cell of the battery to be tested according to the time window to obtain the target data variation corresponding to each time window.

[0331] The third determining module 1802 is configured to determine the internal resistance of the battery cell corresponding to each time window based on the target data variation corresponding to each time window.

[0332] The detection module 1803 is configured to detect each battery cell based on the internal resistance of each battery cell in each time window to obtain a detection result.

[0333] FIG19 is a structural block diagram of a detection module in an embodiment of the present application. In an exemplary embodiment, the detection module 1803 includes:

[0334] The fourth determining unit 1901 is configured to determine a reference internal resistance based on the internal resistance of each battery cell in the same time window.

[0335] The fifth determining unit 1902 is configured to determine an internal resistance difference between an internal resistance corresponding to each battery cell in the same time window and a reference internal resistance.

[0336] The sixth determining unit 1903 is configured to determine, for each battery cell, a relative internal resistance vector of the battery cell according to the internal resistance difference corresponding to the battery cell in each time window.

[0337] The detection unit 1904 is configured to detect each battery cell according to the relative internal resistance vector of each battery cell to obtain a detection result.

[0338] FIG20 is a block diagram of a detection unit according to an embodiment of the present application. In an exemplary embodiment, the detection unit 1904 includes:

[0339] The first determining subunit 2001 is configured to determine an abnormality threshold of each battery cell according to a relative internal resistance vector of each battery cell.

[0340] The detection subunit 2002 is configured to detect each battery cell based on the relative internal resistance vector and the abnormality threshold of each battery cell to obtain a detection result.

[0341] In some embodiments, the first determination subunit 2001 is also used to determine, for each battery cell, a first quantile and a second quantile based on the absolute value of each internal resistance difference in the relative internal resistance vector of the battery cell; the first quantile is greater than the second quantile; the target quantile range is determined based on the second difference between the first quantile and the second quantile; the average value of each internal resistance difference in the relative internal resistance vector of the battery cell is determined; the abnormal threshold of the battery cell is determined based on the sum of the average value and the first product; the first product is determined based on the product of the first preset multiple and the target quantile range.

[0342] In some embodiments, the detection subunit 2002 is further configured to determine, for each battery cell, if there is an internal resistance difference greater than an abnormal threshold value of the battery cell in the relative internal resistance vector of the battery cell, then determine that the detection result of the battery cell is an abnormal detection result.

[0343] In some embodiments, the detection subunit 2002 is also used to determine that the detection result of the battery cell is an abnormal detection result if there are N consecutive internal resistance differences greater than the abnormal threshold, and the N internal resistance differences greater than the abnormal threshold show an increasing trend; wherein N is an integer greater than 1.

[0344] FIG21 is a structural block diagram of another internal resistance determination and adjustment device according to an embodiment of the present application. In an exemplary embodiment, the internal resistance determination device 1500 further includes:

[0345] The fourth determination module 2101 is used to determine the risk quantification value of the battery cell based on the abnormal parameters and abnormal threshold of the battery cell when the detection result of the battery cell is an abnormal detection result; wherein the abnormal parameters include abnormal time, target internal resistance difference, and time span; the abnormal time is based on the last time point in the relative internal resistance vector of the battery cell that is greater than the abnormal threshold, the target internal resistance difference includes the last internal resistance difference in the relative internal resistance vector of the battery cell that is greater than the abnormal threshold, and the time span is the time span between the last abnormal time and the last time point corresponding to the sampling data.

[0346] FIG22 is a structural block diagram of a fourth determination module in an embodiment of the present application. In an exemplary embodiment, the fourth determination module 2101 includes:

[0347] The seventh determining unit 2201 is configured to determine the abnormality degree of the battery cell according to the ratio between the third difference and the abnormality threshold of the battery cell; the third difference is the difference between the target internal resistance difference and the abnormality threshold of the battery cell.

[0348] The eighth determining unit 2202 is configured to determine a time expansion factor according to a preset power of a natural base number; the preset power is the product of the second preset multiple and the fourth difference, and the fourth difference is the difference between the time span and the abnormal time.

[0349] The ninth determining unit 2203 is configured to determine a risk quantification value of the battery cell according to a second product of the abnormality degree of the battery cell and the time expansion factor.

[0350] FIG23 is a structural block diagram of another internal resistance determination and adjustment device according to an embodiment of the present application. In an exemplary embodiment, the internal resistance determination device 1500 further includes:

[0351] The fifth determination module 2301 is used to determine the warning fault level of the battery cell according to the risk quantification value of the battery cell and a second preset relationship; wherein the second preset relationship is used to characterize the correspondence between different risk quantification values ​​and different warning fault levels.

[0352] FIG24 is a structural block diagram of another internal resistance determination and adjustment device according to an embodiment of the present application. In an exemplary embodiment, the internal resistance determination device 1500 further includes:

[0353] The second acquisition module 2401 is configured to acquire first sampling data of the battery cell from the server.

[0354] The preprocessing module 2402 is configured to preprocess the first sampled data to obtain second sampled data.

[0355] The sixth determining module 2403 is configured to use the sampling data in the second sampling data, in which the temperature is not less than a preset temperature threshold and the state of charge is within a preset range, as the third sampling data.

[0356] The seventh determining module 2404 is configured to take, in the third sampling data, data in which adjacent current changes are greater than the first threshold and adjacent current sampling intervals are less than the second threshold as sampling data.

[0357] Each module in the internal resistance determination device described above may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in hardware form, or may be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0358] Figure 25 is an internal structure diagram of a computer device in an embodiment of the present application. In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 25. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for determining internal resistance is implemented.

[0359] Those skilled in the art will understand that the structure shown in Figure 25 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0360] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0361] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0362] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0363] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, and the like.

[0364] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0365] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for determining internal resistance, wherein, The method includes: Obtaining a plurality of sampling data groups of the battery cells to be tested; the sampling data groups include sampling data at two moments; Determining a plurality of initial data change amounts according to the plurality of sampling data groups; the initial data change amounts include current change amounts and voltage change amounts; Fitting each of the initial data change amounts to obtain a voltage fitting amount corresponding to the current change amount in each of the initial data change amounts; Screening each of the initial data change amounts according to the voltage change amount and the voltage fitting amount corresponding to the current change amount in each of the initial data change amounts to obtain target data change amounts; Determining the internal resistance of the battery cell according to the target data change amounts; 2. The method according to claim 1, wherein, The step of screening each of the initial data change amounts according to the voltage change amount and the voltage fitting amount corresponding to the current change amount in each of the initial data change amounts to obtain target data change amounts includes: Determining the absolute value of the residual between the voltage change amount corresponding to the current change amount in each of the initial data change amounts and the voltage fitting amount; Taking the initial data change amounts corresponding to the absolute value of the residual not less than the reference residual as the target data change amounts; 3. The method according to claim 1 or 2, wherein The step of determining the internal resistance of the battery cell according to the target data change amounts includes: Determining the internal resistance of the battery cell according to the target data change amounts and the sampling parameters in the sampling data corresponding to the target data change amounts; Wherein, the sampling parameters include at least one of environmental parameters and state of charge; 4. The method according to claim 1 or 2, wherein The step of determining the internal resistance of the battery cell according to the target data change amounts includes: Fitting the data to be fitted to obtain a fitting curve; the data to be fitted includes the target data change amounts, or the data to be fitted includes the target data change amounts and the sampling parameters in the sampling data corresponding to the target data change amounts; Determining the internal resistance of the battery cell according to the slope of the fitting curve; 5. The method according to claim 1 or 2, wherein The method further includes: Grouping the target data change amounts of each battery cell of the battery to be tested according to a time window to obtain target data change amounts corresponding to each time window; Determining the internal resistance of the battery cell corresponding to each time window based on the target data change amounts corresponding to each time window; Detecting each battery cell based on the internal resistance of each battery cell corresponding to each time window to obtain a detection result; 6. The method according to claim 5, wherein, The step of detecting each battery cell based on the internal resistance of each battery cell corresponding to each time window to obtain a detection result includes: Determining a reference internal resistance based on the internal resistances of each battery cell corresponding to the same time window; Determining the internal resistance difference between the internal resistance of each battery cell corresponding to the same time window and the reference internal resistance; For each battery cell, determining a relative internal resistance vector of the battery cell according to the internal resistance differences of the battery cell corresponding to each time window; Detecting each battery cell according to the relative internal resistance vectors of each battery cell to obtain a detection result; 7. The method according to claim 6, wherein, The step of detecting each battery cell according to the relative internal resistance vectors of each battery cell to obtain a detection result includes: Determining an abnormal threshold for each battery cell according to the relative internal resistance vectors of each battery cell; Based on the relative internal resistance vectors and abnormal thresholds of each of the battery cells, each of the battery cells is detected to obtain a detection result.

8. The method according to claim 7, wherein The determining the abnormal threshold of each of the battery cells according to the relative internal resistance vectors of each of the battery cells includes: For each of the battery cells, a first quantile and a second quantile are determined based on the absolute values of the internal resistance differences in the relative internal resistance vector of the battery cell; the first quantile is greater than the second quantile; A target quantile distance is determined according to a second difference between the first quantile and the second quantile; The average value of the internal resistance differences in the relative internal resistance vector of the battery cell is determined; The abnormal threshold of the battery cell is determined according to the summation result of the average value and a first product; the first product is determined according to the product of a first preset multiple and the target quantile distance.

9. The method according to claim 7 or 8, wherein The detecting each of the battery cells based on the relative internal resistance vectors and abnormal thresholds of each of the battery cells to obtain a detection result includes: For each of the battery cells, if there is an internal resistance difference in the relative internal resistance vector of the battery cell that is greater than the abnormal threshold of the battery cell, it is determined that the detection result of the battery cell is an abnormal detection result.

10. The method according to claim 9, wherein The determining that the detection result of the battery cell is an abnormal detection result includes: If there are N consecutive internal resistance differences greater than the abnormal threshold, and the N internal resistance differences greater than the abnormal threshold show an increasing trend, it is determined that the detection result of the battery cell is an abnormal detection result; wherein, the N is an integer greater than 1.

11. The method according to claim 9 or 10, wherein The method further includes: In the case where the detection result of the battery cell is an abnormal detection result, according to the abnormal parameters and abnormal threshold of the battery cell, a risk quantification value of the battery cell is determined; wherein, the abnormal parameters include an abnormal time, a target internal resistance difference, and a time span; the abnormal time is determined according to the time window closest to the current time in the time window of the N internal resistance differences, the target internal resistance difference is any one of the N internal resistance differences, and the time span is the duration between the time window corresponding to the target internal resistance difference and the maximum sampling time of the sampling data.

12. The method according to claim 11, wherein, The determining the risk quantification value of the battery cell according to the abnormal parameters and abnormal threshold of the battery cell includes: According to the ratio between a third difference and the abnormal threshold of the battery cell, the abnormal degree of the battery cell is determined; the third difference is the difference between the target internal resistance difference and the abnormal threshold of the battery cell; A time expansion factor is determined according to a preset power of the natural base; the preset power is the product of a second preset multiple and a fourth difference, and the fourth difference is the difference between the time span and the abnormal time; According to a second product between the abnormal degree of the battery cell and the time expansion factor, the risk quantification value of the battery cell is determined.

13. The method according to claim 11 or 12, wherein, The method further includes: According to the risk quantification value of the battery cell and a second preset relationship, a warning fault level of the battery cell is determined; wherein, the second preset relationship is used to represent the corresponding relationship between different risk quantification values and different warning fault levels.

14. The method according to any one of claims 1-13, wherein, The method further includes: Obtaining first sampling data of the battery cell from a server; Performing preprocessing on the first sampling data to obtain second sampling data; Use the sampling data in the second sampling data where the temperature is not less than the preset temperature threshold and the state of charge is within the preset range as the third sampling data; Use the data in the third sampling data where the adjacent current change amount is greater than the first threshold and the sampling interval of the adjacent current is less than the second threshold as the sampling data.

15. An internal resistance determination device, wherein, The device includes: A first acquisition module, configured to acquire multiple sampling data sets of the battery cells in the battery under test; each sampling data set includes sampling data at two moments; A first determination module, configured to determine multiple initial data change amounts according to the multiple sampling data sets; the initial data change amounts include current change amounts and voltage change amounts; A fitting module, configured to fit each of the initial data change amounts to obtain a voltage fitting amount corresponding to the current change amount in each of the initial data change amounts; A screening module, configured to screen each of the initial data change amounts according to the voltage change amount and the voltage fitting amount corresponding to the current change amount in each of the initial data change amounts to obtain target data change amounts; A second determination module, configured to determine the internal resistance of the battery cell according to the target data change amount.

16. A computer device, comprising a memory and a processor, the memory storing a computer program, wherein, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 14 are implemented.

17. A computer-readable storage medium having a computer program stored thereon, wherein, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 14 are implemented.

18. A computer program product comprising a computer program, wherein, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 14 are implemented.

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