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

Abstract

The invention relates to an internal resistance determination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a plurality of sampling data sets of a battery cell in a to-be-detected battery, determining a plurality of initial data variations according to the plurality of sampling data sets, and fitting each initial data variation to obtain a voltage fitting amount corresponding to a current variation in each initial data variation, and according to the voltage variation and the voltage fitting amount corresponding to the current variation in each initial data variation, screening each initial data variation to obtain a target data variation, thereby determining the internal resistance of the battery cell according to the target data variation. Wherein the sampling data set comprises sampling data at two moments, and the initial data variable quantity comprises current variable quantity and voltage variable quantity. By adopting the method, the dependence on the battery state can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of batteries, and particularly to a method and device for determining internal resistance, a computer device, and a storage medium. Background Art

[0002] Internal resistance is an important parameter in the field of batteries. Therefore, it is crucial to determine the accurate internal resistance.

[0003] Since the internal resistance is affected by factors such as the manufacturing materials of the battery, manufacturing processes, connection component structures, and battery usage behaviors, usually, the internal resistance of the battery is detected by means of direct current charging or direct current discharging. For example, when the battery is in a stable battery state, by controlling a current pulse, the battery generates multiple voltage rises and falls, and then the internal resistance is estimated by the ratio between the voltage change amount and the current change amount. However, the above method for determining the internal resistance has a high dependence on the battery state. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method and device for determining internal resistance, a computer device, and a storage medium that can reduce the dependence on the battery state.

[0005] In a first aspect, the present application provides a method for determining internal resistance, including:

[0006] Obtaining a plurality of sampling data groups of battery cells in a battery to be measured; the sampling data group includes sampling data at two moments;

[0007] Determining a plurality of initial data change amounts according to the plurality of sampling data groups; the initial data change amount includes a current change amount and a voltage change amount;

[0008] Fitting each initial data change amount to obtain a voltage fitting amount corresponding to the current change amount in each initial data change amount;

[0009] Screening each initial data change amount according to the voltage change amount and the voltage fitting amount corresponding to the current change amount in each initial data change amount to obtain a target data change amount;

[0010] Determining the internal resistance of the battery cell according to the target data change amount.

[0011] In the above internal resistance determination method, since the sampling data set includes sampling data at two moments, after obtaining multiple sampling data sets of the battery cells to be measured, multiple initial data change amounts including current change amounts and voltage change amounts can be determined according to the multiple sampling data sets. Further, since each initial data change amount is fitted to obtain a voltage fitting amount corresponding to the current change amount in each initial data change amount, and each initial data change amount is screened according to the voltage change amount and the voltage fitting amount corresponding to the current change amount in each initial data change amount to obtain a target data change amount, the influence degree of the battery state on the initial data change amount can be reduced, so as to improve the stability and accuracy of the obtained target data change amount. Based on this, in the process of determining the internal resistance in this embodiment, it is not necessary to wait for the battery to be in a stable battery state, but the internal resistance of the battery cells can be determined relatively stably and accurately according to the target data change amount, further reducing the dependence on the battery state, expanding the application scenarios for determining the internal resistance, and having higher flexibility.

[0012] In one embodiment, screening each initial data change amount according to the voltage change amount and the voltage fitting amount corresponding to the current change amount in each initial data change amount to obtain a target data change amount includes:

[0013] Determining the absolute value of the residual between the voltage change amount corresponding to the current change amount in each initial data change amount and the voltage fitting amount;

[0014] Taking the initial data change amount corresponding to the absolute value not less than the reference residual as the target data change amount.

[0015] In the above embodiment, since the initial data change amount corresponding to the absolute value not less than the reference residual is taken as the target data change amount, the accuracy of the target data change amount is improved.

[0016] In one embodiment, determining the internal resistance of the battery cells according to the target data change amount includes:

[0017] Determining the internal resistance of the battery cells according to the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount;

[0018] Wherein, the sampling parameters include at least one of environmental parameters and state of charge.

[0019] In the above embodiment, since the sampling parameters include at least one of environmental parameters and state of charge, according to the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount, at least one of environmental parameters and state of charge can be considered in the determination of the internal resistance of the battery cells, which is conducive to reducing the influence of the state of the battery to be measured and improving the accuracy of the determined internal resistance.

[0020] In one embodiment, determining the internal resistance of the battery cell according to the target data change amount includes:

[0021] Performing fitting on the data to be fitted to obtain a fitting curve; the data to be fitted includes the target data change amount, or the data to be fitted includes the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount;

[0022] Determining the internal resistance of the battery cell according to the slope of the fitting curve.

[0023] In the above embodiment, since the target data change amount with better accuracy and the sampling parameters corresponding to the target data change amount can be used, when determining the internal resistance of the battery cell, the data to be fitted includes the target data change amount, or the data to be fitted includes the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount. Therefore, after performing fitting on the data to be fitted to obtain a fitting curve, the internal resistance of the battery cell can be determined according to the slope of the fitting curve, thereby improving the determination efficiency of the internal resistance. Moreover, since at least one of the environmental parameters and the state of charge is considered, the dependence on the battery state can also be reduced, and the accuracy of the determined internal resistance can be improved.

[0024] In one embodiment, the method further includes:

[0025] Grouping the target data change amounts of each battery cell of the battery to be tested according to a time window to obtain the target data change amounts corresponding to each time window;

[0026] 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;

[0027] Detecting each battery cell based on the internal resistance of each battery cell corresponding to each time window to obtain a detection result.

[0028] In the above embodiment, since the target data change amounts of each battery cell of the battery to be tested can be grouped according to a time window to obtain the target data change amounts corresponding to each time window, and the internal resistance of the battery cell corresponding to each time window is determined based on the target data change amounts corresponding to each time window. Then, based on the internal resistance of each battery cell corresponding to each time window, each battery cell can be detected to obtain a detection result. Since the determined internal resistance is relatively stable and accurate, the accuracy of the detection result determined based on the internal resistance is also improved.

[0029] In one embodiment, detecting each battery cell based on the internal resistance of each battery cell corresponding to each time window to obtain a detection result includes:

[0030] Determining a reference internal resistance based on the internal resistances of each battery cell corresponding to the same time window;

[0031] Determine the internal resistance difference between the internal resistance of each battery cell corresponding to the same time window and the reference internal resistance;

[0032] For each battery cell, determine the relative internal resistance vector of the battery cell according to the internal resistance difference of the battery cell corresponding to each time window;

[0033] Detect each battery cell according to the relative internal resistance vector of each battery cell to obtain a detection result.

[0034] In the above embodiments, since the reference internal resistance can be determined based on the internal resistance of each battery cell corresponding to the same time window, and the internal resistance difference between the internal resistance of each battery cell corresponding to the same time window and the reference internal resistance can be determined, then for each battery cell, according to the internal resistance difference of the battery cell corresponding to each time window, the relative internal resistance vector of the battery cell can be determined. Then, according to the relative internal resistance vector of each battery cell, detecting each battery cell can obtain a relatively accurate detection result.

[0035] In one of the embodiments, detecting each battery cell according to the relative internal resistance vector of each battery cell to obtain a detection result includes:

[0036] Determine the abnormal threshold of each battery cell according to the relative internal resistance vector of each battery cell;

[0037] Detect each battery cell based on the relative internal resistance vector and the abnormal threshold of each battery cell to obtain a detection result.

[0038] In the above embodiments, since the abnormal threshold of each battery cell is determined according to the relative internal resistance vector of each battery cell, the accuracy of the abnormal threshold of each battery cell is improved. Furthermore, detecting each battery cell based on the relative internal resistance vector and the abnormal threshold of each battery cell can obtain an accurate detection result.

[0039] In one of the embodiments, determining the abnormal threshold of each battery cell according to the relative internal resistance vector of each battery cell includes:

[0040] For each battery cell, determine the first quantile and the 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;

[0041] Determine the target interquartile range according to the second difference between the first quantile and the second quantile;

[0042] Determine the average value of each internal resistance difference in the relative internal resistance vector of the battery cell;

[0043] Determine the abnormal threshold of the battery cell according to the summation result 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 interquartile range.

[0044] In the above embodiments, since the abnormal threshold of each battery cell is determined according to the relative internal resistance vector of each battery cell, the accuracy of the abnormal threshold of each battery cell can be improved.

[0045] In one of the embodiments, based on the relative internal resistance vector and the abnormal threshold of each battery cell, each battery cell is detected to obtain a detection result, including:

[0046] For each battery cell, if there is an internal resistance difference greater than the abnormal threshold of the battery cell in the relative internal resistance vector of the battery cell, it is determined that the detection result of the battery cell is an abnormal detection result.

[0047] In the above embodiments, since for each battery cell, when there is an internal resistance difference greater than the abnormal threshold of the battery cell in the relative internal resistance vector of the battery cell, it is determined that the detection result of the battery cell is an abnormal detection result, the efficiency of determining the abnormal detection result is improved.

[0048] In one of the embodiments, determining that the detection result of the battery cell is an abnormal detection result includes:

[0049] 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;

[0050] wherein, N is an integer greater than 1.

[0051] In the above embodiments, since it is determined that the detection result of the battery cell is 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.

[0052] In one of the embodiments, the method further includes:

[0053] When the detection result of the battery cell is an abnormal detection result, according to the abnormal parameters and the abnormal threshold of the battery cell, the risk quantification value of the battery cell is determined;

[0054] wherein, the abnormal parameters include abnormal time, target internal resistance difference, 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.

[0055] In the above embodiments, since the abnormal parameters include abnormal time, target internal resistance difference, and time span; the abnormal time is determined according to the time window closest to the current time among the time windows of 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. Therefore, when the detection result of the battery cell is an abnormal detection result, a relatively accurate risk quantification value can be determined according to the abnormal parameters and abnormal thresholds of the battery cell.

[0056] In one embodiment, determining the risk quantification value of the battery cell according to the abnormal parameters and abnormal thresholds of the battery cell includes:

[0057] Determining the abnormal degree 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;

[0058] Determining the time expansion factor according to the preset power of the natural base; 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;

[0059] Determining the risk quantification value of the battery cell according to the second product between the abnormal degree of the battery cell and the time expansion factor.

[0060] In the above embodiments, 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, therefore, a relatively accurate risk quantification value of the battery cell can be determined according to the second product between the abnormal degree of the battery cell and the time expansion factor.

[0061] In one embodiment, the method further includes:

[0062] Determining the early warning fault level of the battery cell according to the risk quantification value of the battery cell and the second preset relationship;

[0063] Wherein, the second preset relationship is used to represent the corresponding relationship between different risk quantification values and different early warning fault levels.

[0064] In the above embodiments, since the second preset relationship is used to represent the corresponding relationship between different risk quantification values and different early warning fault levels, therefore, it is possible to efficiently and accurately 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

[0065] In one embodiment, the method further includes:

[0066] Obtaining the first sampling data of the battery cell from the server;

[0067] Preprocess the first sampling data to obtain the second sampling data;

[0068] 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;

[0069] Use the data in the third sampling data where the change in adjacent currents is greater than the first threshold and the sampling interval of adjacent currents is less than the second threshold as the sampling data.

[0070] After obtaining the first sampling data of the battery cell from the server in the above embodiment, preprocess the first sampling data to obtain the second sampling data. Since 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 is used as the third sampling data, and the data in the third sampling data where the change in adjacent currents is greater than the first threshold and the sampling interval of adjacent currents is less than the second threshold is used as the sampling data, the accuracy of the sampling data is improved.

[0071] In a second aspect, the present application also provides an internal resistance determination device, including:

[0072] A first acquisition module, configured to acquire multiple sampling data groups of the battery cell in the battery to be measured; each sampling data group includes sampling data at two moments;

[0073] A first determination module, configured to determine multiple initial data change amounts according to the multiple sampling data groups; the initial data change amounts include current change amounts and voltage change amounts;

[0074] A fitting module, configured to fit each initial data change amount to obtain the voltage fitting amount corresponding to the current change amount in each initial data change amount;

[0075] A screening module, configured to screen each initial data change amount according to the voltage change amount and the voltage fitting amount corresponding to the current change amount in each initial data change amount to obtain the target data change amount;

[0076] A second determination module, configured to determine the internal resistance of the battery cell according to the target data change amount.

[0077] In a third aspect, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of any of the above methods are implemented.

[0078] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0079] Fifth aspect, the present application further provides a computer program product, including a computer program which, when executed by a processor, implements the steps of any of the above methods.

[0080] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically exemplified below. Description of the Drawings

[0081] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0082] Figure 1 It is an application environment diagram of the internal resistance determination method in the embodiment of the present application;

[0083] Figure 2 It is a schematic flow chart of the internal resistance determination method in the embodiment of the present application;

[0084] Figure 3 It is a schematic flow chart of a process for obtaining the change amount of target data in the embodiment of the present application;

[0085] Figure 4 It is a schematic flow chart of a process for determining the internal resistance in the embodiment of the present application;

[0086] Figure 5 It is a schematic flow chart of a process for obtaining the detection result in the embodiment of the present application;

[0087] Figure 6 It is a schematic flow chart of another process for obtaining the detection result in the embodiment of the present application;

[0088] Figure 7 It is a schematic flow chart of another process for obtaining the detection result in the embodiment of the present application;

[0089] Figure 8 It is a schematic flow chart of a process for determining the abnormal threshold in the embodiment of the present application;

[0090] Figure 9 It is a schematic process diagram of a process for determining the abnormal detection result in the embodiment of the present application;

[0091] Figure 10 It is a schematic flow chart of a process for determining the risk quantification value in the embodiment of the present application;

[0092] Figure 11 Schematic diagram of time expansion in an embodiment of this application;

[0093] Figure 12 Schematic flow chart of obtaining sampled data in an embodiment of this application;

[0094] Figure 13 Schematic diagram of an effect in an embodiment of this application;

[0095] Figure 14 Schematic process diagram of a method for determining internal resistance in an embodiment of this application;

[0096] Figure 15 Block diagram of the structure of a device for determining and adjusting internal resistance in an embodiment of this application;

[0097] Figure 16 Block diagram of the structure of a screening module in an embodiment of this application;

[0098] Figure 17 Block diagram of the structure of a second determination module in an embodiment of this application;

[0099] Figure 18 Block diagram of the structure of another device for determining and adjusting internal resistance in an embodiment of this application;

[0100] Figure 19 Block diagram of the structure of a detection module in an embodiment of this application;

[0101] Figure 20 Block diagram of the structure of a detection unit in an embodiment of this application;

[0102] Figure 21 Block diagram of the structure of another device for determining and adjusting internal resistance in an embodiment of this application;

[0103] Figure 22 Block diagram of the structure of a fourth determination module in an embodiment of this application;

[0104] Figure 23 Block diagram of the structure of another device for determining and adjusting internal resistance in an embodiment of this application;

[0105] Figure 24 Block diagram of the structure of another device for determining and adjusting internal resistance in an embodiment of this application;

[0106] Figure 25 Internal structure diagram of a computer device in an embodiment of this application. Detailed implementation manners

[0107] The embodiments of the technical solution of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solution of the present application more clearly, so they are only examples and cannot be used to limit the protection scope of the present application.

[0108] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field 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 drawings are intended to cover non-exclusive inclusion.

[0109] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of the present application, "a plurality of" means more than two, unless otherwise specifically defined.

[0110] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0111] In the description of the embodiments of the present application, the term "a plurality of" means more than two (including two). Similarly, "a plurality of groups" means more than two groups (including two groups), and "a plurality of pieces" means more than two pieces (including two pieces).

[0112] Internal resistance is an important parameter in the field of batteries. Taking the application of batteries in automobiles as an example, if the resistance difference of each battery cell in the battery increases, it may lead to driving faults. For example, a battery cell with abnormal internal resistance will reach the charging cut-off voltage in advance, resulting in the premature termination of the charging process. Or, too large a difference in the internal resistance of the battery cells will lead to frequent fault alarms of too large a pressure difference during driving.

[0113] Currently, the internal resistance is determined by the method of DC charging or DC discharging. That is, when the battery is in a stable battery state, by controlling the current pulse, the battery generates multiple voltage rises and falls, and then the internal resistance is estimated by using the ratio dU / dI between the voltage change and the current change. Among them, the battery being in a stable battery state is, for example: the SOC and temperature of the battery are within a preset range, the duration of the charging current is greater than a certain threshold, the change value of the charging current is less than the preset limit, and the charging current should be greater than the preset current threshold, and moreover, there is no fault warning for the battery.

[0114] It can be seen that the current method for determining the internal resistance has a high dependence on the battery state. On the one hand, since the determination of the internal resistance depends relatively much 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. And, due to measurement errors, fluctuations in temperature and current, inaccurate SOC, and loss of collected data, etc., it is easy to introduce errors when calculating the internal resistance at a single point currently, resulting in unstable distribution variance of the single-point calculated value of the internal resistance. If the mean smoothing method is used, the final estimated result of the internal resistance is also easily affected by outliers. Therefore, the stability and accuracy of the currently determined internal resistance are relatively poor.

[0115] On the other hand, for the current method of determining the internal resistance, since it is necessary to actively control the battery through a current pulse, it is not applicable to some big data scenarios where the battery cannot be actively controlled and only the usage process of the battery is recorded. In other words, in the big data scenario of the battery, it cannot be guaranteed that the internal resistance identification opportunities that meet the battery state conditions will occur at different time points. And, due to noise in data collection, processing, and transmission, there will still be problems of low stability and accuracy of the determined internal resistance in the big data scenario of the battery.

[0116] Based on this, it is necessary to provide an internal resistance determination method that can reduce the dependence on the battery state for the above technical problems. The following introduces this internal resistance determination method.

[0117] The battery disclosed in the embodiments of the present application can be but is not limited to being used in power-consuming devices such as vehicles, ships, or aircraft. The power supply system of the power-consuming device can be composed of the battery disclosed in the present application. In this way, it is beneficial to reduce the dependence on the battery state during the process of determining the internal resistance, and thus improve the stability and accuracy of the internal resistance determination.

[0118] An embodiment of the present application provides an electrical device using a battery as a power source. The electrical device can be, but is not limited to, a mobile phone, a tablet computer, a laptop computer, an electric toy, an electric tool, a battery car, an electric vehicle, a ship, a spacecraft, and so on. Among them, the electric toy can include a fixed or mobile electric toy, for example, a game console, an electric vehicle toy, an electric ship toy, an electric aircraft toy, and so on. The spacecraft can include an airplane, a rocket, a space shuttle, a spaceship, and so on.

[0119] For the convenience of description, the following embodiments take a vehicle 101, which is an electrical device in an embodiment of the present application, as an example for description. Figure 1 It is an application environment diagram of the internal resistance determination method in an embodiment of the present application. As Figure 1 shown, the vehicle 101 communicates with the computer device 103.

[0120] Among them, the vehicle 101 can be a fuel vehicle, a gas vehicle or a new energy vehicle. The new energy vehicle can be a pure electric vehicle, a hybrid electric vehicle or an extended-range electric vehicle, etc. A battery 102 is provided inside the vehicle 101. The battery 102 can be arranged at the bottom, head or tail of the vehicle 101. The battery 102 can be used to supply power to the vehicle 101. For example, the battery 102 can be used as the operating power source of the vehicle 101. The vehicle 101 may also include a controller and a motor ( Figure 1 not shown in the figure). The controller is used to control the battery 102 to supply power to the motor. For example, it is used for the working power requirements during the start, navigation and driving of the vehicle 101.

[0121] The computer device 103 can be arranged outside the vehicle 101. It can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things device can be a smart speaker, a smart TV, a smart air conditioner, a smart vehicle-mounted device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. Of course, the computer device 103 can also be implemented by an independent server or a server cluster composed of multiple servers.

[0122] The computer device 103 can also be arranged inside the vehicle 101. It includes, but is not limited to, a Central Processing Unit (CPU), and can also include a Digital Signal Processing (DSP), a Field-Programmable Gate Array (FPGA) or other programmable logic devices.

[0123] In some embodiments of the present application, the battery 102 can not only serve as the operating power source of the vehicle 101, but also as the driving power source of the vehicle 101, replacing or partially replacing fuel or natural gas to provide driving power for the vehicle 101.

[0124] Figure 2 FIG. is a schematic flowchart of the internal resistance determination method in the embodiments of the present application. In an exemplary embodiment, as Figure 2 shown, a method for determining internal resistance is provided. Taking the case where this method is applied to Figure 1 the computer device in as an example for illustration, it includes the following S201 to S203.

[0125] S201, obtain multiple sampling data groups of the battery cells to be measured; the sampling data group includes sampling data at two moments.

[0126] Among them, the battery to be measured includes at least one battery cell. The sampling data includes the voltage and current of the battery cell at different time points. Optionally, the sampling data can be data obtained by the computer device from devices such as servers and cloud platforms, or data stored in advance in the computer device.

[0127] The sampling data can be a sequence related to time, and the sampling data includes current and voltage. Taking voltage as an example, the sampling data can include the voltage 1 of the battery cell at time point 1, the voltage 2 at time point 2, the voltage 3 at time point 3, and so on. The length of the sampling data can be set according to requirements. For example, the computer device can obtain the sampling data of the battery cell in the previous 1 month before the current time.

[0128] Furthermore, the computer device can obtain multiple sampling data groups of the battery cell. Among them, the sampling data group includes sampling data at two moments, and the sampling data at the two moments can be the data at two adjacent time points in the sampling data. For example, the sampling data group 1 includes the sampling data at time point 1 and the sampling data at time point 2, the sampling data group 2 includes the sampling data at time point 2 and the sampling data at time point 3, the sampling data group 3 includes the sampling data at time point 3 and the sampling data at time point 4, and so on.

[0129] In some embodiments, the sampling data at the two moments can also be the data at two non - adjacent time points in the sampling data. For example, the sampling data group 1 includes the sampling data at time point 1 and the sampling data at time point 3, the sampling data group 2 includes the sampling data at time point 2 and the sampling data at time point 4, the sampling data group 3 includes the sampling data at time point 3 and the sampling data at time point 5, and so on.

[0130] S202, determine multiple initial data change amounts according to the multiple sampling data groups; the initial data change amount includes current change amount and voltage change amount.

[0131] Furthermore, the computer device can determine multiple initial data change amounts according to multiple sampling data groups. That is to say, for a sampling data group, the difference between the currents at two moments in the sampling data group is calculated to obtain the current change amount, and the difference between the voltages at two moments in the sampling data is calculated to obtain the voltage change amount, then the initial data change amount corresponding to the sampling data group can be obtained. It can be understood that each sampling data group corresponds to an initial data change amount.

[0132] Continuing with the above example, the initial data change amount 1 is obtained according to the sampling data group 1. The initial data change amount 1 includes the current change amount 1 and the voltage change amount 1. The current change amount 1 is determined according to the current at time point 1 and the current at time point 2, and the voltage change amount 1 is determined according to the voltage at time point 1 and the voltage at time point 2.

[0133] The initial data change amount 2 is obtained according to the sampling data group 2. The initial data change amount 2 includes the current change amount 2 and the voltage change amount 2. The current change amount 2 is determined according to the current at time point 2 and the current at time point 3, and the voltage change amount 2 is determined according to the voltage at time point 2 and the voltage at time point 3, and so on.

[0134] S203. Fit each initial data change amount to obtain the voltage fitting amount corresponding to the current change amount in each initial data change amount.

[0135] Since the state of the battery under test will change, and the state of the battery under test will also cause errors in the initial data change amount, therefore, in this embodiment, each initial data change amount will be fitted to obtain the voltage fitting amount corresponding to the current change amount in each initial data change amount.

[0136] In this embodiment, assuming that the computer device obtains 200 initial data change amounts, denoted as the initial data change amount 1 to the initial data change amount 200, then the computer device can fit the initial data change amount 1 to the initial data change amount 200 to obtain the voltage fitting amount 1* corresponding to the current change amount 1 in the initial data change amount 1, the voltage fitting amount 2* corresponding to the current change amount 2, …… the voltage fitting amount 200* corresponding to the current change amount 200.

[0137] Optionally, the computer device can calculate the goodness of fit during the process of fitting the initial data change amount, and stop fitting when the goodness of fit reaches the preset goodness of fit, so as to obtain the voltage fitting amount corresponding to the current change amount in each initial data change amount. The goodness of fit reaching the preset goodness of fit means that there are main components with similar battery working condition influences among the initial data change amounts, so that it helps to improve the accuracy of the target data change amount.

[0138] S204. Screen the initial data change amounts according to the voltage change amounts and voltage fitting amounts corresponding to the current change amounts in the initial data change amounts to obtain target data change amounts.

[0139] Further, for each initial data change amount, there is a corresponding voltage change amount and voltage fitting amount for the current change amount. For example, in the initial data change amount 1, the current change amount 1 corresponds to the voltage fitting amount 1* and the voltage change amount 1, and in the initial data change amount 2, the current change amount 2 corresponds to the voltage fitting amount 2* and the voltage change amount 2.

[0140] After that, the computer device can screen the initial data change amounts according to the voltage change amounts and voltage fitting amounts corresponding to the current change amounts in the initial data change amounts to obtain target data change amounts. Exemplarily, the computer device can calculate the difference between the voltage change amounts and voltage fitting amounts corresponding to the current change amounts in the initial data change amounts, and use the initial data change amounts with differences less than the preset difference as the target data change amounts.

[0141] Exemplarily, assume that the computer device obtains 200 initial data change amounts based on two adjacent sampling data of the battery cell, and after fitting and screening the initial data change amounts, 100 target data change amounts are obtained. Then the 100 target data change amounts can be sequentially recorded in chronological order as , , …… .

[0142] S205. Determine the internal resistance of the battery cell according to the target data change amounts.

[0143] In this embodiment, after obtaining the target data change amounts, the computer device can determine the internal resistance of the battery cell according to the target data change amounts. Among them, the internal resistance of the battery cell includes but is not limited to the direct current resistance (DCR).

[0144] Optionally, the computer device can determine the internal resistance of the battery cell according to the following formula (1), where represents the voltage change amount, represents the current change amount, and DCR represents the internal resistance. Exemplarily, the computer device can fit the target data change amounts to obtain a fitting curve, and use the slope of the fitting curve as the internal resistance of the battery cell.

[0145] (1)

[0146] In the above internal resistance determination method, since the sampling data set includes sampling data at two moments, after obtaining multiple sampling data sets of the battery cells to be measured, multiple initial data change amounts including current change amounts and voltage change amounts can be determined according to the multiple sampling data sets. Further, since each initial data change amount is fitted to obtain the voltage fitting amount corresponding to the current change amount in each initial data change amount, and each initial data change amount is screened according to the voltage change amount and the voltage fitting amount corresponding to the current change amount in each initial data change amount to obtain the target data change amount, the influence degree of the battery state on the initial data change amount can be reduced, so as to improve the stability and accuracy of the obtained target data change amount. Based on this, in the process of determining the internal resistance in this embodiment, it is not necessary to wait for the battery to be in a stable battery state, but the internal resistance of the battery cells can be determined to be relatively stable and accurate according to the target data change amount, further reducing the dependence on the battery state, expanding the application scenario of determining the internal resistance, and having higher flexibility.

[0147] Figure 3 FIG. 4 is a schematic flowchart of a process for obtaining a target data change amount in an embodiment of the present application. In an exemplary embodiment, as Figure 3 shown, S204 includes S301 to S302.

[0148] S301, determine the absolute value of the residual between the voltage change amount corresponding to the current change amount in each initial data change amount and the voltage fitting amount.

[0149] In this embodiment, continuing the above example, the computer device can determine the absolute values 1 to 200 corresponding to the initial data change amounts 1 to 200 respectively. The absolute value 1 is the absolute value of the residual between the voltage fitting amount 1* and the voltage change amount 1, the absolute value 2 is the absolute value of the residual between the voltage fitting amount 2* and the voltage change amount 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 amount 200.

[0150] S302, use the initial data change amount corresponding to the absolute value not less than the reference residual as the target data change amount.

[0151] Among them, the reference residual can be a value determined by the computer device in response to an input operation, or a value sent by other devices to the computer device, or a value determined by the computer device according to the absolute value of the residual corresponding to each initial data change amount.

[0152] Exemplarily, the computer device may use the 75th percentile of the residuals of each initial data change amount as the reference residual. Suppose the absolute values from 51 to 150 are greater than or equal to the reference residual, while the absolute values from 1 to 50 and from 151 to 200 are not greater than the reference residual, then the computer device takes the initial data change amounts from 51 to 150 as the target data change amounts in sequence , , …… .

[0153] In this embodiment, the absolute value of the residual between the voltage change amount corresponding to the current change amount and the voltage fitting amount among the initial data change amounts is determined. Since the initial data change amounts corresponding to the absolute values not less than the reference residual are used as the target data change amounts, the accuracy of the target data change amounts is improved.

[0154] In an exemplary embodiment, S205 may be implemented in the following manner:

[0155] According to the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount, determine the internal resistance of the battery cell; wherein, the sampling parameters include at least one of environmental parameters and state of charge.

[0156] In this embodiment, the sampling data further includes the sampling parameters of the battery cell, and the sampling parameters include at least one of environmental parameters and state of charge (SOC), and the environmental parameters include but are not limited to the temperature of the battery cell.

[0157] Taking the target data change amount as an example, suppose is determined based on the voltage of the battery cell at time point 50 and the voltage at time point 49, is determined based on the current of the battery cell at time point 50 and the current at time point 49, then the corresponding sampling parameters can be determined according to the sampling parameters corresponding to any one time point in [time point 49, time point 50]. For example, the computer device takes the temperature and SOC of the battery cell at time point 49 as the corresponding sampling parameters.

[0158] In some embodiments, the computer device may also determine the corresponding sampling parameters according to the sampling parameters corresponding to at least two time points in [time point 49, time point 50]. For example, the computer device takes the average value of the temperatures and the average value of the SOCs corresponding to time points 49 to 50 as the corresponding sampling parameters.

[0159] Further, the computer device can determine the internal resistance of the battery cell according to the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount. Exemplarily, the computer device can perform principal component analysis on the target data change amount and the sampling data corresponding to the target data change amount to obtain an analysis result, fit the analysis result to obtain a fitting curve, and determine the internal resistance of the battery cell according to the slope of the fitting curve.

[0160] Since the sampling parameters in this embodiment include at least one of the environmental parameter and the state of charge, therefore, according to the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount, at least one of the environmental parameter and the state of charge can be considered in determining the internal resistance of the battery cell, thereby facilitating reducing the influence of the state of the battery under test and improving the accuracy of the determined internal resistance.

[0161] Figure 4 FIG. is a schematic flowchart of a method for determining internal resistance in an embodiment of the present application. In an exemplary embodiment, as Figure 4 shown, S205 includes S401 to S403.

[0162] S401, fitting the data to be fitted to obtain a fitting curve; the data to be fitted includes the target data change amount, or the data to be fitted includes the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount.

[0163] To more clearly illustrate the internal resistance determination method of the present application, the derivation principle is described herein. At a given time point , the dynamic voltage of any battery cell in the battery under test can be expressed as the following formula (2). In formula (2), and are the voltages of the battery cell at time point and time point respectively, and are the currents of the battery cell at time point and time point respectively, and are the internal resistances of the battery cell at time point and time point respectively, and are the open circuit voltages of the battery cell at time point and time point respectively.

[0164] , (2)

[0165] According to formula (2), the battery cell at time point and time point The pressure difference between is as shown in Equation (3).

[0166] (3)

[0167] Since the sampling parameters such as temperature and SOC do not change much in a very short time, therefore, restricting , making the time interval between two time points as small as possible, the following Equation (4) can be obtained.

[0168] , (4)

[0169] Based on Equation (3) and Equation (4), the following Equation (5) can be obtained. Where and are and infinitesimals of the difference between DCR and OCV between

[0170] (5)

[0171] For the convenience of description, denote . Since the internal resistance of the battery cell is affected by factors such as temperature, current change, and cumulative charging time, and the open-circuit voltage of the battery cell is also affected by factors such as temperature and cumulative charging time, therefore, the uncertainty of the working condition where the battery cell is located will cause to change. At the same time, the process of data acquisition, transmission, and acquisition will also bring certain errors. With multiple uncertain factors superimposed and influencing each other, it can be assumed that the disturbance term obeys a certain normal distribution , and then the following Equation (6) can be obtained. Where, , = .

[0172] (6)

[0173] It can be seen that in a very short time, the current change amount and voltage change amount of the battery cell approximately satisfy the above Equation (6). and are disturbance factors, which are related to the working condition of the battery cell. For different working conditions, the corresponding and are also different. Further, combining Equation (6), the battery states corresponding to the change amounts of each initial data are different, and There are also differences. When the influence of the battery state on the initial data change amount is relatively large, the linear relationship formed by the initial data change amounts will become small. Therefore, in the above embodiments, performing principal component extraction on each initial data change amount helps to reduce the influence of battery state differences on the internal resistance.

[0174] Based on this, the computer device also determines the impedance extraction model shown in Equation (6). Among them, the computer device can determine the target perturbation factor according to the sampling parameters and the first preset relationship, and determine the first difference between the voltage change amount and the target perturbation factor in the target data change amount, so as to determine the ratio between the first difference and the current change amount in the target data change amount, and determine the impedance extraction model based on the ratio. Among them, the first preset relationship is used to represent the corresponding relationship between different sampling parameters and different perturbation factors. Exemplarily, assume The corresponding sampling parameters include temperature 1 and SOC1, and in the first preset relationship, temperature 1 and SOC1 correspond to 1 and , then the computer device determines that the target perturbation factor is 1 and , and so on.

[0175] In this way, in one embodiment, after the computer device obtains the target data change amount and the sampling parameters corresponding to the target data change amount, it can fit the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount to obtain a fitting curve. That is to say, the computer device will fit the target data change amount and the corresponding sampling parameters, , , temperature, and the relationship between SOC to obtain a fitting curve that satisfies Equation (7).

[0176] In one embodiment, the computer device can also perform fitting on each target data change amount according to Equation (1) to obtain a fitting curve.

[0177] S402. Determine the internal resistance of the battery cell according to the slope of the fitting curve.

[0178] Furthermore, based on Equation (1) or Equation (6), it can be known that according to the slope of the fitting curve obtained in S601, the internal resistance of the battery cell can be determined. Exemplarily, the computer device can directly use the slope of the fitting curve obtained in S601 as the internal resistance of the battery cell.

[0179] In this embodiment, since the data to be fitted includes the target data change amount, or the data to be fitted includes the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount, after fitting the data to be fitted to obtain a fitting curve, the internal resistance of the battery cell can be determined according to the slope of the fitting curve, thereby improving the determination efficiency of the internal resistance. Moreover, since the target data change amount with better accuracy and the sampling parameters corresponding to the target data change amount can be used, at least one of the environmental parameters and the state of charge is considered in the determination of the internal resistance of the battery cell, and the dependence on the battery state can also be reduced, and the accuracy of the determined internal resistance can be improved.

[0180] Figure 5 FIG. is a schematic flowchart of a process for obtaining a detection result in an embodiment of the present application. In an exemplary embodiment, as Figure 5 shown, the above internal resistance determination method further includes S501 to S503.

[0181] S501. For the target data change amounts of each battery cell of the battery to be tested, group the target data change amounts according to a time window to obtain the target data change amounts corresponding to each time window.

[0182] In this embodiment, it is assumed that the battery to be tested includes a total of 3 battery cells, namely battery cell A to battery cell C. Then, according to the method of S201 to S205, the computer device can perform fitting and screening on multiple sampling data groups of battery cell A to obtain the target data change amount of battery cell A. Similarly, the computer device can also obtain the target data change amounts of battery cell B and battery cell C.

[0183] Furthermore, for the target data change amounts of each battery cell of the battery to be tested, the computer device can group the target data change amounts according to a time window to obtain the target data change amounts corresponding to each time window. Herein, the time window can be understood as a certain time length, which can be set according to requirements. For example, if the time window is 7 days, the computer device will divide the target data change amount of each battery cell into a group every 7 days according to time.

[0184] Continuing with the above example, it is assumed that the target data change amounts of battery cell A are ~ in order from far to near in time. After grouping the target data change amounts of battery cell A according to the time window, ~ is group 1, ~ is group 2, ~ For Group 3. Among them, the target data change amount corresponding to the first time window for Cell A is in Group 1, the target data change amount corresponding to the second time window for Cell A is in Group 2, and the target data change amount corresponding to the third time window for Cell A is in Group 3.

[0185] It can be understood that among the above three time windows, 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, which will not be elaborated here.

[0186] S502. Based on the target data change amounts corresponding to each time window, determine the internal resistance of the cell corresponding to each time window.

[0187] Furthermore, the computer device can determine the internal resistance of the cell corresponding to each time window based on the target data change amounts corresponding to each time window. Optionally, the computer device can determine the internal resistance of the cell based on the target data change amounts corresponding to each time window and the sampling parameters in the sampling data corresponding to the target data change amounts.

[0188] Exemplarily, the computer device can ~ and the corresponding sampling data to obtain a fitting curve, and use the slope of the fitting curve as the internal resistance A1 of Cell A in the first time window. By analogy, the computer device can obtain the internal resistance A2 of Cell A in the second time window and the internal resistance A3 of Cell A in the third time window. Similarly, the computer device can also obtain the internal resistance B1 of Cell B in the first time window, the internal resistance B2 of Cell B in the second time window, the internal resistance B3 of Cell B in the third time window, and the internal resistance C1 of Cell C in the first time window, the internal resistance C2 of Cell C in the second time window, and the internal resistance C3 of Cell C in the third time window.

[0189] Among them, the process of determining the internal resistance of the cell corresponding to each time window can refer to the above embodiments, which will not be elaborated here.

[0190] S503. Based on the internal resistances of each cell corresponding to each time window, perform detection on each cell to obtain a detection result.

[0191] Furthermore, the computer device can perform detection on each cell based on the internal resistances of each cell corresponding to each time window to obtain a detection result. Among them, the detection result can include a normal detection result and an abnormal detection result. The normal detection result indicates that the cell is normal, and the abnormal detection result indicates that the cell is abnormal.

[0192] Exemplarily, the computer device may determine a reference internal resistance based on the internal resistances of each battery cell corresponding to each time window. If the number of internal resistances of the same battery cell corresponding to each time window that are greater than the reference internal resistance is a preset number, it is determined that the detection result of this battery cell is abnormal. Herein, the preset number is an integer greater than 0. The computer device may also analyze the change conditions of each battery cell of the battery under test within all time windows, and determine the detection results of each battery cell according to the change conditions, and so on.

[0193] In this embodiment, since it is possible to group the target data change amounts of each battery cell of the battery under test according to time windows, obtain the target data change amounts corresponding to each time window, and determine the internal resistance of each battery cell corresponding to each time window based on the target data change amounts corresponding to each time window. Furthermore, based on the internal resistances of each battery cell corresponding to each time window, the detection results of each battery cell can be obtained through detection. Since the determined internal resistance is relatively stable and accurate, the accuracy of the detection results determined based on the internal resistance is also improved.

[0194] It can be understood that S501 to S503 list the process of directly grouping the target data change amounts to obtain the target data change amounts corresponding to each time window. In some embodiments, the computer device may also first group the initial data change amounts to obtain the initial data change amounts corresponding to each time window, and then perform fitting and screening on the initial data change amounts corresponding to each time window to obtain the target data change amounts corresponding to each time window.

[0195] Figure 6 This is a schematic flowchart of another process for obtaining a detection result in the embodiments of the present application. In an exemplary embodiment, as Figure 6 shown, S503 includes S601 to S604.

[0196] S601, determine a reference internal resistance based on the internal resistances of each battery cell corresponding to the same time window.

[0197] In this embodiment, for the same battery under test, the computer device can determine a reference internal resistance based on the internal resistances of each battery cell corresponding to the same time window. Continuing with the example where the battery under test includes battery cells A, B, and 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, which will not be elaborated here. It can be understood that the reference internal resistance of each time window may be different.

[0198] Among them, the reference internal resistance can be the average value or weighted average value of the internal resistances of each battery cell corresponding to the same time window, or the median value of the internal resistances of each battery cell corresponding to the same time window, etc. Exemplarily, taking the first time window as an example, the computer device can use the median value among the internal resistances A1, B1, and C1 as the reference internal resistance of the first time window.

[0199] S602. Determine the internal resistance difference between the internal resistance of each battery cell corresponding to the same time window and the reference internal resistance.

[0200] In this embodiment, continuing with the above example, if the reference internal resistance of the first time window is denoted as 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.

[0201] Similarly, assuming that the reference internal resistance of the second time window is S2, then 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.

[0202] Assuming that the reference internal resistance of the second time window is S3, then 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 of each battery cell corresponding to the same time window and the reference internal resistance is determined.

[0203] S603. For each battery cell, determine the relative internal resistance vector of the battery cell according to the internal resistance differences of the battery cell corresponding to each time window.

[0204] In this embodiment, for the same battery cell, the computer device can arrange the internal resistance differences of the battery cell corresponding to each time window in the order of the time window to obtain the relative internal resistance vector of the battery cell.

[0205] 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).

[0206] S604. According to the relative internal resistance vectors of each battery cell, perform detection on each battery cell to obtain a detection result.

[0207] Optionally, the computer device may detect each battery cell based on the relative internal resistance vectors of the battery cells and a preset risk threshold to obtain a detection result. For example, if there are consecutive internal resistance differences greater than the preset risk threshold in the relative internal resistance vector of battery cell A, the computer device determines that the detection result of battery cell A is an abnormal detection result.

[0208] In this embodiment, since the reference internal resistance can be determined based on the internal resistances of the battery cells corresponding to the same time window, and the internal resistance differences between the internal resistances of the battery cells corresponding to the same time window and the reference internal resistance can be determined, and then for each battery cell, according to the internal resistance differences of the battery cell corresponding to each time window, the relative internal resistance vector of the battery cell can be determined. Then, based on the relative internal resistance vectors of the battery cells, more accurate detection results can be obtained by detecting each battery cell.

[0209] In other words, considering that the working conditions affected by the fitted internal resistance corresponding to different target data change amounts are not exactly the same, therefore, if the internal resistances between the battery cells are directly compared to judge the abnormal battery cells, the influence of working condition noise will exist. Therefore, under the assumption that the working conditions experienced by different battery cells are similar, the internal resistance differences between the internal resistances of multiple battery cells and the reference internal resistance are extracted, and through the relative internal resistance vector, the influence difference of the working conditions between different target data change amounts can be further reduced, and the working condition dimension for comparing the internal resistance changes is unified.

[0210] Figure 7 For another flowchart of obtaining the detection result in the embodiment of the present application, in an exemplary embodiment, as Figure 7 shown, S604 includes S701 to S702.

[0211] S701, determine the abnormal threshold of each battery cell according to the relative internal resistance vector of each battery cell.

[0212] In this embodiment, optionally, for the same battery cell, the computer device may use the quantile, average value, weighted average value, etc. between the internal resistance differences of the relative internal resistance vector of the battery cell as the abnormal threshold of the battery cell.

[0213] Continuing with the above example, the computer device determines the abnormal threshold of battery cell A according to the relative internal resistance vector of battery cell A. Determines the abnormal threshold of battery cell C according to the relative internal resistance vector of battery cell B, and determines the abnormal threshold of battery cell C according to the relative internal resistance vector of battery cell C. That is to say, the abnormal thresholds corresponding to the battery cells of the battery under test may be different.

[0214] S702, detect each battery cell based on the relative internal resistance vector and the abnormal threshold of each battery cell to obtain a detection result.

[0215] In this embodiment, for each battery cell, the computer device can detect the battery cell based on the relative internal resistance vector and the abnormal threshold of the same battery cell to obtain a detection result. For example, according to the relative internal resistance vector of battery cell A and the abnormal threshold of battery cell A, if there are consecutive internal resistance differences greater than the preset risk threshold in the relative internal resistance vector of battery cell A, the computer device determines that the detection result of battery cell A is an abnormal detection result.

[0216] In this embodiment, since the abnormal threshold of each battery cell is determined according to the relative internal resistance vector of each battery cell, the accuracy of the abnormal threshold of each battery cell is improved. Furthermore, based on the relative internal resistance vector and the abnormal threshold of each battery cell, accurate detection results can be obtained by detecting each battery cell.

[0217] Figure 8 It is a schematic flowchart of a process for determining an abnormal threshold in an embodiment of the present application. In an exemplary embodiment, as Figure 8 shown, S701 includes S801 to S804.

[0218] S801, for each battery cell, determine the first quantile and the second quantile 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.

[0219] Taking battery cell A as an example, the computer device determines the first quantile among |A1 - S1|, |A2 - S2|, |A3 - S3|, denoted as , and determines the second quantile among |A1 - S1|, |A2 - S2|, |A3 - S3|, denoted as .

[0220] Among them, represents the absolute value of each internal resistance difference in the relative internal resistance vector of battery cell A, that is, |A1 - S1|, |A2 - S2|, |A3 - S3|. and are respectively the multi - quantiles of a given distribution, . Exemplarily, , that is, the first quantile can be the third quartile of the first quartile of

[0221] S802, determine the target inter - quantile range according to the second difference between the first quantile and the second quantile.

[0222] Continuing to take battery cell A as an example, the computer device can determine the target inter - quantile range according to the following formula (7) .

[0223] (7)

[0224] S803. Determine the average value of the internal resistance differences in the relative internal resistance vector of the battery cell.

[0225] Continuing to take battery cell A as an example, the computer device determines the average value of the internal resistance differences in the relative internal resistance vector of battery cell A .

[0226] S804. Determine the abnormal threshold of the battery cell according to the summation result 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 interquartile range.

[0227] Furthermore, continuing to take battery cell A as an example, the computer device determines according to the first preset multiple and the target interquartile range to determine the first product , and determines the abnormal threshold of the battery cell according to the summation result of the average value and the first product .

[0228] For example, the computer device can determine the abnormal threshold of battery cell A according to the following formula (8) . In some embodiments, the computer device can also multiply by an empirical coefficient on the basis of formula (8) to determine the abnormal threshold of battery cell A , and the empirical coefficient can be between 0 and 1.

[0229] (8)

[0230] It can be understood that the above takes the calculation of the abnormal threshold of battery cell A as an example for illustration. The calculation principle of the abnormal thresholds of other battery cells is the same and will not be elaborated here.

[0231] In this embodiment, for each battery cell, based on the absolute values of the internal resistance differences in the relative internal resistance vector of the battery cell, the first quartile and the second quartile are determined, and the target interquartile range is determined according to the second difference between the first quartile and the second quartile. Furthermore, the average value of the internal resistance differences in the relative internal resistance vector of the battery cell is determined, so as to determine the abnormal threshold of the battery cell according to the summation result of the average value and the first product. Among them, the first quartile is greater than the second quartile, and the first product is determined according to the product of the first preset multiple and the target interquartile range. In this way, the accuracy of the abnormal thresholds of each battery cell can be improved.

[0232] In an exemplary embodiment, S802 can be implemented in the following manner:

[0233] For each battery cell, 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, then determine that the detection result of the battery cell is an abnormal detection result.

[0234] 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 , that is, there is an internal resistance difference in A1 - S1, A2 - S2, or A3 - S3 that is greater than the abnormal threshold of cell A in internal resistance difference, then the computer device determines that the detection result of cell A is an abnormal detection result.

[0235] Conversely, if there is no internal resistance difference in the relative internal resistance vector of the cell that is greater than the abnormal threshold of the cell, then the computer device can determine that the detection result of the cell is a normal detection result.

[0236] In this embodiment, for each cell, when there is an internal resistance difference in the relative internal resistance vector of the cell that is greater than the abnormal threshold of the cell, the detection result of the cell is determined to be an abnormal detection result, improving the efficiency of determining the abnormal detection result.

[0237] In an exemplary embodiment, the above "determining that the detection result of the cell is an abnormal detection result" can also be achieved in the following manner:

[0238] 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, then the detection result of the cell is determined to be an abnormal detection result; where N is an integer greater than 1.

[0239] In this embodiment, N is set according to requirements and is an integer greater than 1. Among them, showing an increasing trend can mean that the internal resistance difference in the latter time window is always greater than that in the previous time window, or a certain proportion of the internal resistance differences are always greater than those in the previous time window.

[0240] Exemplarily, taking N = 2 as an example, if in the relative internal resistance vector (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, it indicates that in the relative internal resistance vector of cell A, there are 2 consecutive internal resistance differences greater than the abnormal threshold, and the 2 internal resistance differences greater than the abnormal threshold show an increasing trend. In this case, the computer device can determine that the detection result of cell A is an abnormal detection result.

[0241] If in the relative internal resistance vector (A1 - S1, A2 - S2, A3 - S3) of cell A, both A1 - S1 and A3 - S3 are greater than the internal resistance difference of the abnormal threshold of cell A, but A2 - S2 is less than the abnormal threshold of cell A, it indicates that in the relative internal resistance vector of cell A, there are no 2 consecutive internal resistance differences greater than the abnormal threshold. In this case, the computer device can determine that the detection result of cell A is not an abnormal detection result.

[0242] In this embodiment, 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, the detection result of the battery cell is determined to be an abnormal detection result; since N is an integer greater than 1, the accuracy of the abnormal detection result is improved.

[0243] Figure 9 It is a schematic diagram of a process for determining an abnormal detection result in an embodiment of the present application. In Figure 9 it, the horizontal axis represents the time window, and the vertical axis represents the internal resistance difference of the battery cell. As Figure 9 shown, the internal resistance vector of the battery cell is arranged in the order of the time window as the relative internal resistance characteristic of the battery cell. The computer device can determine the abnormal threshold of the battery cell according to a certain proportion of the relative internal resistance vectors of the battery cell before the current time. For example, the abnormal threshold is determined according to the relative internal resistance vector of the time window on September 3, 2023. Further, when the internal resistance difference in the relative internal resistance vector continuously exceeds the abnormal threshold and shows an increasing trend, the computer device determines that the battery cell has shown a continuous outlier abnormality, and thus determines that the detection result of the battery cell is an abnormal detection result.

[0244] In an exemplary embodiment, the above internal resistance determination method further includes the following steps:

[0245] When the detection result of the battery cell is an abnormal detection result, determine the risk quantification value of the battery cell according to the abnormal parameters and the abnormal threshold of the battery cell; wherein, the abnormal parameters include the abnormal time, the target internal resistance difference, and the time span; the abnormal time is based on the time point of the last internal resistance difference greater than the abnormal threshold among the 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 the N consecutive internal resistance differences greater than the abnormal threshold, and the time span is the time span between the last time point corresponding to the N consecutive internal resistance differences greater than the abnormal threshold and the sampling data.

[0246] 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.

[0247] The abnormal parameters include the abnormal time, the target internal resistance difference, and the time span. Among them, the abnormal time is determined according to the time window closest to the current time among 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 among the time windows of N internal resistance differences as the abnormal time. Taking cell A as an example, assume that the relative internal resistance vector of cell A includes internal resistance differences 1 to 8, and the time windows corresponding to internal resistance differences 1 to 8 are arranged in ascending order of time from far to near. If internal resistance differences 1, 4 to 6, and 8 are all greater than the abnormal threshold of cell A, and internal resistance differences 4 to 6 show an increasing trend, then the computer device can use any time point in the time window corresponding to internal resistance difference 6 as the abnormal time.

[0248] The target internal resistance difference is any one of the N internal resistance differences. Optionally, the target internal resistance difference can 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 differences 1, 4 to 6, and 8 are all greater than the abnormal threshold of cell A, and internal resistance differences 4 to 6 show an increasing trend, then the computer device can use internal resistance difference 6 as the target internal resistance difference.

[0249] 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. The maximum sampling time of the sampling data can be the maximum sampling time point of the original sampling data obtained by the computer device, or the maximum sampling time point of the sampling data after processing and screening the original sampling data.

[0250] Exemplarily, assume that the computer device obtains the sampling data of cell A between January 1, 2021 and December 31, 2021, and determines that the abnormal time of cell A is August 1, 2023 according to the steps of the above embodiment based on this sampling data. 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 duration between August 1, 2023 and December 31, 2021.

[0251] Furthermore, the computer device can determine the risk quantification value of the cell according to the abnormal parameters and the abnormal threshold of the cell.

[0252] Optionally, the computer device may determine the degree of cell abnormality based on the ratio between the third difference and the abnormality threshold of the cell, and determine the time expansion factor of the 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 cell based on the degree of cell abnormality and the time expansion factor of the cell. Among them, the third difference may be the difference between the target internal resistance difference and the abnormality threshold of the cell. The computer device may also analyze and calculate the abnormal parameters and abnormality thresholds of the cell through a preset model to determine the risk quantification value of the cell.

[0253] In this embodiment, since the abnormal parameters include the abnormal time, the target internal resistance difference, and the time span; the abnormal time is determined according to the time window closest to the current time among the N internal resistance difference time windows, 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. Therefore, when the detection result of the cell is an abnormal detection result, a relatively accurate risk quantification value can be determined according to the abnormal parameters and abnormality thresholds of the cell.

[0254] Figure 10 It is a schematic flowchart of a process for determining a risk quantification value in an embodiment of the present application. In an exemplary embodiment, as Figure 10 shown, the above "determining the risk quantification value of the cell according to the abnormal parameters and abnormality thresholds of the cell" includes S1001 to S1003.

[0255] S1001, determine the degree of cell abnormality according to the ratio between the third difference and the abnormality threshold of the cell; the third difference is the difference between the target internal resistance difference and the abnormality threshold of the cell.

[0256] In this embodiment, continuing to take cell A as an example, during the process of determining the risk quantification value of cell A, the computer device will determine the target internal resistance difference and the abnormality threshold of cell A .

[0257] Furthermore, the computer device determines the ratio between the third difference and the abnormality threshold of cell A.

[0258] After that, the computer device determines the degree of cell abnormality of cell A. Optionally, the computer device may directly use as the degree of cell abnormality of cell A.

[0259] S1002. Determine a time expansion factor 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.

[0260] Continuing to take cell A as an example, the computer device determines the time span and the abnormal time to obtain the fourth difference and determines the preset power . Here, M is the second preset multiple. When M > 0, it means the computer device is pessimistic about the risk expansion trend, and when M < 0, it means the computer device is optimistic about the risk expansion trend. That is to say, when M > 0, it is more likely to be evaluated as a risk by the computer device, and when M < 0, it is less likely to be evaluated as a risk by the computer device.

[0261] Furthermore, the computer device determines the time expansion factor according to . For example, the computer device can directly use as . .

[0262] S1003. Determine the risk quantification value of the cell according to the second product between the cell abnormality degree and the time expansion factor.

[0263] Even further, the computer device determines the risk quantification value of the cell according to the second product between and .

[0264] Exemplarily, the computer device determines the risk quantification value of cell A according to the following formula (9) .

[0265] (9)

[0266] It can be understood that the above takes the calculation of the risk quantification value of cell A as an example for illustration. The calculation principle of the risk quantification values of other cells is the same and will not be elaborated here.

[0267] Figure 11 This is a schematic diagram of time expansion in an embodiment of this application, as shown in Figure 11As shown, taking the battery cell A as an example, the abnormal time of the battery cell A is August 1, 2023, while the maximum sampling time of the sampling data is December 31, 2021. That is to say, due to reasons such as sampling processing, extraction, and noise, the data of the battery cell A cannot be used for abnormal detection during the period from August 1, 2023 to December 31, 2021. Therefore, in this embodiment, the time expansion factor can be used to expand the time of the risk quantification value corresponding to the abnormal time, reduce the error lost during the period from August 1, 2023 to December 31, 2021, and thus output a relatively accurate risk quantification value of the battery cell.

[0268] In this embodiment, according to the ratio between the third difference and the abnormal threshold of the battery cell, the abnormal degree of the battery cell is determined, and the time expansion factor is determined according to the preset power of the natural base. 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, therefore, according to the second product between the abnormal degree of the battery cell and the time expansion factor, a relatively accurate risk quantification value of the battery cell can be determined.

[0269] In an exemplary embodiment, the above internal resistance determination method further includes the following steps:

[0270] According to the risk quantification value of the battery cell and the second preset relationship, the 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.

[0271] In this embodiment, continuing to take the battery cell A as an example, the second preset relationship may include the following formula (10). Wherein, can be a value pre-stored in the computer device, i takes values from 1 to N, and 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.

[0272] (10)

[0273] After that, the computer device can determine the warning fault level of the battery cell A according to the risk quantification value of the battery cell A and the above formula (10). Exemplarily, assume = 0, = 5, = 10, etc. If = 5, then = 5 , and then the computer device will determine that the warning fault level of the battery cell A is 2.

[0274] Further optionally, the computer device may output a prompt message according to the early warning fault level, and the prompt message includes but is not limited to at least one of voice broadcast, display pop-up window, text message prompt, and phone call prompt.

[0275] In this embodiment, since the second preset relationship is used to represent the corresponding relationship between different risk quantification values and different early warning fault levels, it is possible to efficiently and accurately 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.

[0276] In an exemplary embodiment, at least one of the reference residual and the first preset multiple may be determined according to at least one of the detection results, risk quantification values, and early warning fault levels of the sampling data of the abnormal battery cell under different candidate values.

[0277] Taking the reference residual as an example, the computer device may obtain the sampling data of each battery cell in the abnormal battery that has already shown abnormalities, and use the method of the above embodiment to predict the early warning fault level of the abnormal battery under different candidate residuals, and use the predicted early warning fault level of the abnormal battery and the actual abnormal situation of the abnormal battery to select the candidate residual corresponding to the case of high prediction accuracy or low false alarm rate as the reference residual. The same applies to the first preset multiple. In this way, it helps to improve the accuracy of the reference residual or the first preset multiple.

[0278] Figure 12 This is a schematic flow chart of obtaining sampling data in an embodiment of the present application. In an exemplary embodiment, as Figure 12 shown, the above internal resistance determination method further includes S1201 to S1203.

[0279] S1201, obtain the first sampling data of the battery cell from the server.

[0280] In this embodiment, the server includes but is not limited to the server of the cloud platform. The first sampling data may include the current, voltage, and sampling parameters of each battery cell in the battery under test at each time point. Among them, the server may periodically send the first sampling data of the battery cell to the computer device, and the computer device may also request the first sampling data within a preset time period from the server.

[0281] Exemplarily, the computer device may request the first sampling data of each battery cell in the previous 3 months from the server. The first sampling data includes the voltage 1, current 1, temperature 1, and SOC1 of each battery cell at time point 1, the voltage 2, current 2, temperature 2, and SOC2 at time point 2, the voltage 3, current 3, temperature 3, and SOC3 at time point 3, etc.

[0282] S1202, preprocess the first sampling data to obtain the second sampling data.

[0283] The preprocessing may include, but is not limited to, cleaning the outliers in the first sampled data, and the outliers in the first sampled data include, but are not limited to, at least one of jump values, null values, and out-of-range values. Exemplarily, the computer device may delete the outliers in the first sampled data to obtain the second sampled data. Among them, if the voltage 1 at time point 1 is abnormal, the computer device may also need to delete the current 1, temperature 1, and SOC1 at time point 1.

[0284] S1203: Use the sampled data in the second sampled 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 sampled data.

[0285] Among them, the preset temperature threshold and the preset range can be set according to requirements. Exemplarily, the preset temperature threshold can be 10°C, and the preset range is [30%, 80%]. Then, the computer device uses 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.

[0286] S1204: Use the data in the third sampled data where the change in adjacent currents is greater than the first threshold and the sampling interval of adjacent currents is less than the second threshold as the sampled data.

[0287] Furthermore, due to factors such as transmission errors, jitters, and noises between the computer device and the server, in order to improve the accuracy of the sampled data, the computer device will further screen the third sampled data.

[0288] Among them, the change in adjacent currents being greater than the first threshold is to improve the variability of the current. The sampling interval of adjacent currents being less than the second threshold is to reduce the impact of sampling information loss. The first threshold and the second threshold can be set according to requirements.

[0289] Exemplarily, the first threshold can be 10 A, and the second threshold can be 5 minutes. Then, the computer device will retain the data in the second sampled data where the change in adjacent currents is greater than 10 A and the sampling interval of adjacent currents is less than 5 minutes as the sampled data for subsequent use, and eliminate the data in the third sampled data with small current switching differences and too large sampling time intervals.

[0290] After obtaining the first sampled data of the battery cell from the server in this embodiment, the first sampled data is preprocessed to obtain the second sampled data. Since the sampled data in the second sampled data where 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 sampled data, and the data in the third sampled data where the change in adjacent currents is greater than the first threshold and the sampling interval of adjacent currents is less than the second threshold is used as the sampled data, the accuracy of the sampled data is improved.

[0291] To more clearly introduce the internal resistance determination method in this application, the following will be described in conjunction with Figure 13 and Figure 16 for illustration.

[0292] Figure 13 This is a schematic diagram of the effects in an embodiment of this application. As Figure 13 shown, Figure 13 (a) shows the change amounts of the initial data of each battery cell. Figure 13 (b) shows the process of performing principal component extraction on the change amounts of the initial data in Figure 13 (a) to obtain the change amounts of each target data within the rectangular frame. Figure 13 (c) shows the process of fitting the change amounts of the target data in Figure 13 (b) to obtain a linear curve. Figure 13 (d) shows the schematic diagram of the relative internal resistance vectors of each battery cell obtained based on the internal resistance determined in Figure 14 (c). Figure 13 (e) shows the schematic diagram of the relative internal resistance vectors of each battery cell obtained by directly determining the internal resistance according to the change amounts of the initial data in Figure 13 (a).

[0293] Among them, in Figure 13 (a) to Figure 13 (b), the horizontal axis represents the change amount of current, and the vertical axis represents the change amount of voltage. In Figure 13 (d) to Figure 13 (e), the horizontal axis represents time, and the vertical axis represents the relative internal resistance vector. By comparing Figure 13 (d) and Figure 13 (e), it can be seen that since the change amounts of each initial data are first subjected to principal component extraction in this embodiment to obtain relatively accurate change amounts of target data, the obtained relative internal resistance vectors are relatively robust and accurate, and the effect is better.

[0294] Figure 14 This is a schematic process diagram of an internal resistance determination method in an embodiment of this application. As Figure 14 shown, the computer device can execute this method according to the following process.

[0295] S1401, obtain the first sampling data of the battery cell from the server.

[0296] S1402, preprocess the first sampling data to obtain the second sampling data.

[0297] S1403, 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.

[0298] S1404, use, as sampling data, data in the third sampling data where the adjacent current change amounts are greater than the first threshold and the sampling intervals of the adjacent currents are less than the second threshold.

[0299] S1405, obtain multiple sampling data groups of the battery cells in the battery under test. Among them, a sampling data group includes sampling data at two moments.

[0300] S1406, determine multiple initial data change amounts according to the multiple sampling data groups. Among them, the initial data change amount includes a current change amount and a voltage change amount.

[0301] S1407, perform fitting on each initial data change amount to obtain a voltage fitting amount corresponding to the current change amount in each initial data change amount.

[0302] S1408, determine the absolute value of the residual between the voltage change amount corresponding to the current change amount in each initial data change amount and the voltage fitting amount.

[0303] S1409, use the initial data change amount corresponding to the absolute value not less than the reference residual as the target data change amount.

[0304] S1410, for the target data change amount of each battery cell of the battery under test, group the target data change amount according to a time window to obtain the target data change amount corresponding to each time window.

[0305] S1411, based on the target data change amount corresponding to each time window, determine the internal resistance of the battery cell corresponding to each time window.

[0306] For each time window, S1411 includes S1 and S2 (not shown in the figure). S1: Fit the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount to obtain a fitting curve. S2: Determine the internal resistance of the battery cell according to the slope of the fitting curve. Among them, the sampling parameters include at least one of environmental parameters and state of charge.

[0307] S1412, determine a reference internal resistance based on the internal resistances of the battery cells corresponding to the same time window.

[0308] S1413, determine the internal resistance difference between the internal resistances of the battery cells corresponding to the same time window and the reference internal resistance.

[0309] S1414, for each battery cell, determine the relative internal resistance vector of the battery cell according to the internal resistance difference of the battery cell corresponding to each time window.

[0310] 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. Among them, the first quantile is greater than the second quantile.

[0311] S1416. Determine the target quantile range according to the second difference between the first quantile and the second quantile.

[0312] S1417. Determine the average value of the internal resistance differences in the relative internal resistance vector of the battery cell.

[0313] S1418. Determine the abnormal threshold of the battery cell according to the summation result of the average value and the first product. Wherein, the first product is determined according to the product of the first preset multiple and the target quantile range.

[0314] 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 show an increasing trend, then determine the detection result of the battery cell as an abnormal detection result. Wherein, N is an integer greater than 1.

[0315] S1420. When the detection result of the battery cell is an abnormal detection result, determine the abnormal degree of the battery cell according to 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.

[0316] S1421. Determine the time expansion factor according to the preset power of the natural base. Wherein, 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.

[0317] S1422. Determine the risk quantification value of the battery cell according to the second product between the abnormal degree of the battery cell and the time expansion factor.

[0318] S1423. 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. Wherein, the second preset relationship is used to represent the corresponding relationship between different risk quantification values and different early warning fault levels.

[0319] The processes of S1401 to S1423 can refer to the above embodiments and will not be elaborated here. It can be seen that in the internal resistance determination method provided in this embodiment, based on the sampling data of the battery, through the characteristic working condition filtering mechanism, the principal component extraction is performed on each of the initial data change amounts to obtain the target data change amount. Then, through multi-feature regression, the relationship between the target data change amount and the corresponding sampling parameters is fitted at multiple points to determine the internal resistance. After that, the internal resistance can be used to accurately identify the faults of abnormal internal resistance outliers and perform continuous and stable safety warnings. In this way, through a feature extraction mechanism, the reliability and stability of the DC internal resistance identification result can be improved, the sensitivity of the identification result to the battery state can be weakened, the accuracy and stability of the identification result can be improved, and continuous safety warnings can be realized.

[0320] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0321] Based on the same inventive concept, an embodiment of the present application further provides an internal resistance determination device for implementing the internal resistance determination method described above. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the internal resistance determination device provided below can refer to the limitations on the internal resistance determination method in the above text, and will not be repeated here.

[0322] Figure 15 It is a structural block diagram of an internal resistance determination and adjustment device in an embodiment of the present application. In an exemplary embodiment, as Figure 15 shown, an internal resistance determination device 1500 is provided, including: a first acquisition module 1501, a first determination module 1502, a fitting module 1503, a screening module 1504, and a second determination module 1505, where:

[0323] The first acquisition module 1501 is configured to acquire multiple sampling data groups of the battery cells in the battery to be measured; the sampling data group includes sampling data at two moments.

[0324] The first determination module 1502 is configured to determine multiple initial data change amounts according to the multiple sampling data groups; the initial data change amounts include current change amounts and voltage change amounts.

[0325] The fitting module 1503 is 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.

[0326] The screening module 1504 is 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 a target data change amount.

[0327] The second determination module 1505 is configured to determine the internal resistance of the battery cell according to the target data change amount.

[0328] In the above internal resistance determination device, since the sampling data includes voltage and current, after obtaining the initial data change amount between two adjacent sampling data of the battery cell to be measured, the initial data change amount will include the current change amount and the voltage change amount. Further, since the principal component extraction can be performed on each initial data change amount to obtain the target data change amount, the influence degree of the battery state on the initial data change amount can be reduced, so as to improve the stability and accuracy of the obtained target data change amount. Based on this, in the process of determining the internal resistance in this embodiment, it is not necessary to wait for the battery to be in a stable battery state, but the internal resistance of the battery cell that is relatively stable and accurate can be determined according to the target data change amount, further reducing the dependence on the battery state, expanding the application scenario of determining the internal resistance, and having higher flexibility.

[0329] Figure 16 This 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:

[0330] The first determination unit 1601 is configured to determine the absolute value of the residual between the voltage change amount corresponding to the current change amount in each initial data change amount and the voltage fitting amount.

[0331] The second determination unit 1602 is configured to use the initial data change amount corresponding to the absolute value not less than the reference residual as the target data change amount.

[0332] Optionally, the second determination module 1505 is further configured to determine the internal resistance of the battery cell 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 the environmental parameters and the state of charge.

[0333] Figure 17 This 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:

[0334] The first fitting unit 1701 is configured to perform fitting on the data to be fitted to obtain a fitting curve; the data to be fitted includes the target data change amount, or the data to be fitted includes the target data change amount and the sampling parameters in the sampling data corresponding to the target data change amount.

[0335] The third determination unit 1702 is configured to determine the internal resistance of the battery cell according to the slope of the fitting curve.

[0336] Figure 18 This is a structural block diagram of another internal resistance determination and adjustment device in an embodiment of the present application. In an exemplary embodiment, the internal resistance determination device 1500 further includes:

[0337] A grouping module 1801, configured to group the target data change amounts of each battery cell of the battery to be tested according to a time window, so as to obtain the target data change amounts corresponding to each time window.

[0338] A third determination module 1802, configured to determine the internal resistance of each battery cell corresponding to each time window based on the target data change amounts corresponding to each time window.

[0339] A detection module 1803, configured to detect each battery cell based on the internal resistance of each battery cell corresponding to each time window to obtain a detection result.

[0340] Figure 19 This 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:

[0341] A fourth determination unit 1901, configured to determine a reference internal resistance based on the internal resistances of each battery cell corresponding to the same time window.

[0342] A fifth determination unit 1902, configured to determine the internal resistance difference between the internal resistance of each battery cell corresponding to the same time window and the reference internal resistance.

[0343] A sixth determination unit 1903, configured to determine a relative internal resistance vector of each battery cell according to the internal resistance differences of each battery cell corresponding to each time window.

[0344] A detection unit 1904, configured to detect each battery cell according to the relative internal resistance vectors of each battery cell to obtain a detection result.

[0345] Figure 20 This is a structural block diagram of a detection unit in an embodiment of the present application. In an exemplary embodiment, the detection unit 1904 includes:

[0346] A first determination subunit 2001, configured to determine an abnormal threshold for each battery cell according to the relative internal resistance vectors of each battery cell.

[0347] A detection subunit 2002, configured to detect each battery cell based on the relative internal resistance vectors and the abnormal threshold of each battery cell to obtain a detection result.

[0348] Optionally, the first determination subunit 2001 is further configured to, for each battery cell, determine 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; the first quantile is greater than the second quantile; determine a target interquartile range according to a second difference between the first quantile and the second quantile; determine the average value of the internal resistance differences in the relative internal resistance vector of the battery cell; determine the abnormal threshold of the battery cell 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 interquartile range.

[0349] Optionally, the detection subunit 2002 is further configured to, for each battery cell, 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, determine that the detection result of the battery cell is an abnormal detection result.

[0350] Optionally, the detection subunit 2002 is further configured to, 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, determine that the detection result of the battery cell is an abnormal detection result; where N is an integer greater than 1.

[0351] Figure 21 This is a structural block diagram of another internal resistance determination and adjustment device in an embodiment of the present application. In an exemplary embodiment, the internal resistance determination device 1500 further includes:

[0352] A fourth determination module 2101, configured to, when the detection result of the battery cell is an abnormal detection result, determine the risk quantification value of the battery cell according to the abnormal parameters and the abnormal threshold of the battery cell; where the abnormal parameters include the abnormal time, the target internal resistance difference, and the 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 internal resistance difference in the relative internal resistance vector of the battery cell that is the last one 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 sampled data.

[0353] Figure 22 This 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:

[0354] A seventh determination unit 2201, configured to determine the abnormal degree 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.

[0355] An eighth determination unit 2202, configured to determine the time expansion factor according to a preset power of the natural base; 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.

[0356] A ninth determination unit 2203, configured to determine the risk quantification value of the battery cell according to the second product between the abnormal degree of the battery cell and the time expansion factor.

[0357] Figure 23 This is a structural block diagram of another internal resistance determination and adjustment device in an embodiment of the present application. In an exemplary embodiment, the internal resistance determination device 1500 further includes:

[0358] A fifth determination module 2301, configured to determine a warning fault level of the battery cell according to a risk quantification value of the battery cell and a second preset relationship, where the second preset relationship is used to represent a corresponding relationship between different risk quantification values and different warning fault levels.

[0359] Figure 24 It is a structural block diagram of another internal resistance determination and adjustment device in the embodiments of the present application. In an exemplary embodiment, the internal resistance determination device 1500 further includes:

[0360] A second acquisition module 2401, configured to acquire first sampling data of the battery cell from a server.

[0361] A preprocessing module 2402, configured to preprocess the first sampling data to obtain second sampling data.

[0362] A sixth determination module 2403, configured to use the sampling data in the second sampling data with a temperature not less than a preset temperature threshold and a state of charge within a preset range as third sampling data.

[0363] A seventh determination module 2404, configured to use the data in the third sampling data with an adjacent current change amount greater than a first threshold and an adjacent current sampling interval less than a second threshold as sampling data.

[0364] Each module in the above internal resistance determination device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0365] Figure 25 It is an internal structure diagram of a computer device in the embodiments of the present application. In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as Figure 25As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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 the 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 external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements an internal resistance determination method.

[0366] Those skilled in the art can understand that Figure 25 the structure shown in the figure is only a block diagram of some structures 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 those shown in the figure, or combine some components, or have different component arrangements.

[0367] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0368] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0369] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0370] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0371] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope recorded in this specification.

[0372] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for determining internal resistance, characterized in that, the method includes: Obtaining multiple sampling data groups of the battery cells to be measured; each sampling data group includes sampling data at two moments; Determining multiple initial data change amounts according to the multiple 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; 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, screening 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, characterized in that, the screening each of the initial data change amounts to obtain target 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 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 amount corresponding to the absolute value of the residual not less than the reference residual as the target data change amount.

3. The method according to claim 1 or 2, characterized in that, the 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, characterized in that, the 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, characterized in that, the method further includes: Grouping the target data change amounts of each battery cell of the battery to be measured according to a time window to obtain the 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, characterized in that, the 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 the relative internal resistance vector of the battery cell according to the internal resistance differences of the battery cell corresponding to each time window. Detect each of the battery cells according to the relative internal resistance vectors of the battery cells to obtain a detection result.

7. The method according to claim 6, wherein, the detecting each of the battery cells according to the relative internal resistance vectors of the battery cells to obtain a detection result includes: determining an abnormality threshold for each of the battery cells according to the relative internal resistance vectors of the battery cells; detecting each of the battery cells based on the relative internal resistance vectors and the abnormality thresholds of the battery cells to obtain a detection result.

8. The method according to claim 7, wherein, the determining an abnormality threshold for each of the battery cells according to the relative internal resistance vectors of the battery cells includes: for each of the battery cells, determining 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; the first quantile is greater than the second quantile; determining a target quantile distance according to a second difference between the first quantile and the second quantile; determining an average value of the internal resistance differences in the relative internal resistance vector of the battery cell; determining the abnormality threshold of the battery cell according to a summation result of the average value and a first product; the first product is determined according to a 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 the abnormality thresholds of the battery cells to obtain a detection result includes: for each of the battery cells, if there is an internal resistance difference greater than the abnormality threshold of the battery cell in the relative internal resistance vector of the battery cell, determining 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 abnormality threshold and the N internal resistance differences greater than the abnormality threshold show an increasing trend, determining that the detection result of the battery cell is an abnormal detection result; wherein, N is an integer greater than 1.

11. The method according to claim 9 or 10, wherein, the method further includes: in the case that the detection result of the battery cell is an abnormal detection result, determining a risk quantification value of the battery cell according to the abnormal parameter and the abnormality threshold of the battery cell; wherein, the abnormal parameter includes 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 windows 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 a risk quantification value of the battery cell according to the abnormal parameter and the abnormality threshold of the battery cell includes: determining the abnormality degree of the battery cell according to a ratio between a 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. Determine a time expansion factor 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; Determine the risk quantification value of the battery cell according to a second product between the degree of abnormality of the battery cell and the time expansion factor.

13. The method according to claim 11 or 12, characterized in that, the method further includes: 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 corresponding relationship between different risk quantification values and different early warning fault levels.

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

15. An internal resistance determination device, characterized in that, the device includes: A first acquisition module, configured to acquire multiple sampling data groups of the battery cell in the battery to be measured; each sampling data group includes sampling data at two moments; A first determination module, configured to determine multiple initial data change amounts according to the multiple sampling data groups; 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 a target data change amount; 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, including a memory and a processor, the memory stores a computer program, characterized in that, 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, on which a computer program is stored, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 14 are implemented.

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

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