Battery self-discharge abnormity detection method and device

Abstract

The invention provides a battery self-discharge abnormity detection method and device, and belongs to the technical field of batteries. The method comprises the following steps: acquiring charge data of a battery at a current time point and at a plurality of preset sampling time points before the current time point; determining first charge change data of the battery, the first charge change data being a difference value between the charge data at the first sampling time point and the charge data at the current time point, and the first sampling time point being any one of a plurality of preset sampling time points; and determining a self-discharge detection result of the battery based on a first interval duration between the current time point and the first sampling time point, a preset interval duration, the first charge change data and a preset charge change threshold. The current data is compared with the historical data, the corresponding first interval duration and the first charge change data are determined, whether self-discharge of the battery is abnormal or not is determined in time by combining the preset interval duration and the preset charge change threshold value, and continuous deterioration of the battery is avoided.

Description

Technical Field

[0001] The present application belongs to the field of battery technology, and specifically relates to a method and device for detecting abnormal battery self-discharge. Background Art

[0002] Battery self-discharge refers to the process of discharging a battery when not in use, even without an external load connected, causing the battery's stored charge to gradually decrease. Abnormal battery self-discharge can shorten driving range and potentially lead to safety hazards such as thermal runaway. Abnormal battery self-discharge is typically monitored by setting alarm thresholds, but this only determines current data and cannot be compared with historical data to determine battery deterioration. Consequently, potential problems cannot be detected promptly, and early signs of battery performance deterioration may be missed. Summary of the Invention

[0003] In response to the above problems, the present application provides a method and device for detecting abnormal battery self-discharge, which can be compared with historical data to determine whether the battery has deteriorated, thereby enabling potential problems to be discovered in a timely manner.

[0004] This application provides a method for detecting abnormal battery self-discharge, comprising:

[0005] Obtaining charge data of the battery at the current time point and multiple preset sampling time points before the current time point;

[0006] Determine first charge change data of the battery, where the first charge change data is a difference between charge data at a first sampling time point and charge data at a current time point, and the first sampling time point is any one of a plurality of preset sampling time points;

[0007] A self-discharge detection result of the battery is determined based on a first interval duration between the current time point and the first sampling time point, a preset interval duration, the first charge change data, and a preset charge change threshold.

[0008] In some embodiments, the self-discharge detection result includes normal self-discharge and abnormal self-discharge. Determining the battery self-discharge detection result based on a first interval duration between a current time point and a first sampling time point, a preset interval duration, the first charge change data, and a preset charge change threshold includes:

[0009] If the first charge change data is greater than or equal to the preset charge change threshold, and the first interval duration is greater than the preset interval duration;

[0010] Each preset sampling time point between the first sampling time point and the current time point is determined as the second sampling time point in sequence, and the second charge change data of the battery is determined, where the second charge change data is the difference between the charge data at the second sampling time point and the charge data at the current time point. Based on the second interval duration between the current time point and the second sampling time point, the preset interval duration, the second charge change data, and the preset charge change threshold, the self-discharge detection result of the battery is determined until it is determined that the battery self-discharge is abnormal or all the preset sampling time points are determined as the second sampling time points.

[0011] In some embodiments, the method further comprises:

[0012] If the first charge change data is less than the preset charge change threshold, it is determined that the battery self-discharge is normal.

[0013] In some embodiments, the method further comprises:

[0014] If the first charge change data is greater than or equal to a preset charge change threshold, and the first interval duration is less than or equal to a preset interval duration, it is determined that the battery self-discharge is abnormal.

[0015] In some embodiments, a method for determining a preset charge change threshold includes:

[0016] Determine self-discharge difference data of the test cell based on the self-discharge data set of the test cell;

[0017] Determining a corresponding open circuit voltage change rate of the test cell within the preset state of charge range based on the open circuit voltage of the test cell within the preset state of charge range;

[0018] A preset charge change threshold is determined based on the self-discharge difference data, the open circuit voltage change rate, and the preset interval time.

[0019] In some embodiments, a method for determining self-discharge difference data includes:

[0020] After removing abnormal data from the self-discharge dataset, the target self-discharge dataset is determined;

[0021] Determine the maximum value in the target self-discharge data set, where the maximum value includes a maximum value and a minimum value;

[0022] The self-discharge difference data is determined based on the maximum difference of the target self-discharge data set and a preset consistency coefficient.

[0023] In some embodiments, determining a preset charge change threshold based on the self-discharge difference data, the open circuit voltage change rate, and the preset interval duration includes:

[0024] determining a first threshold based on the self-discharge difference data, a preset interval duration, an open circuit voltage change rate, and a preset redundancy coefficient;

[0025] Determining a second threshold based on the absolute value of the fluctuation range of the voltage acquisition accuracy and the open circuit voltage change rate;

[0026] The maximum value of the first threshold value and the second threshold value is determined as the preset charge variation threshold value.

[0027] In some embodiments, a battery includes a plurality of battery cells; and a method for determining charge data of the battery includes:

[0028] Determine the state of charge of each battery cell;

[0029] Determine the maximum state of charge and the minimum state of charge from the state of charge corresponding to each battery cell;

[0030] The maximum difference between the maximum state of charge and the minimum state of charge is determined as the charge data of the battery.

[0031] In some embodiments, a battery includes a battery cell; and a method for determining charge data of the battery includes:

[0032] The state of charge of the cell is determined as charge data of the battery.

[0033] Accordingly, the present application also provides a device for detecting abnormal battery self-discharge, comprising:

[0034] The first module is used to obtain the charge data of the battery at the current time point and multiple preset sampling time points before the current time point;

[0035] The second module is used to determine first charge change data of the battery, where the first charge change data is the difference between the charge data at a first sampling time point and the charge data at a current time point, and the first sampling time point is any one of a plurality of preset sampling time points;

[0036] The third module is used to determine the self-discharge detection result of the battery based on the first interval duration between the current time point and the first sampling time point, the preset interval duration, the first charge change data and the preset charge change threshold.

[0037] The beneficial effect of the present application is that the present application provides a method for detecting abnormal self-discharge of a battery, the detection method comprising: obtaining charge data of the battery at a current time point and multiple preset sampling time points before the current time point; determining first charge change data of the battery, the first charge change data being the difference between the charge data at the first sampling time point and the charge data at the current time point, the first sampling time point being any one of the multiple preset sampling time points; and determining a self-discharge detection result of the battery based on a first interval duration, a preset interval duration, the first charge change data, and a preset charge change threshold between the current time point and the first sampling time point. The present application compares the current data with historical data by comparing the charge data at the current time point and multiple preset sampling time points before the current time point, determining the first interval duration and the first charge change data corresponding to the battery between the first sampling time point and the current time point, and combining the preset interval duration and the preset charge change threshold to promptly determine whether the battery has self-discharge abnormality, thereby promptly discovering potential problems and avoiding continuous deterioration of the battery.

[0038] The present application also provides a device for detecting abnormal battery self-discharge, including the method for detecting abnormal battery self-discharge in the above embodiment. Therefore, the device can have all the technical features and effects of the above method for detecting abnormal battery self-discharge, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 A flowchart of a method for detecting abnormal battery self-discharge provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of a process for determining a battery self-discharge detection result provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of the structure of a device for detecting abnormal battery self-discharge provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0044] In the description of the present application, it should be understood that the specific embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application. The terms "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "multiple" is two or more, and at least one means one, two or more, unless otherwise clearly and specifically defined. The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more features.

[0045] This application provides a method and device for detecting abnormal battery self-discharge, which are described in detail below. It should be noted that the order in which the following embodiments are described does not limit the preferred order of the embodiments of this application. In addition, in the following embodiments, the description of each embodiment has its own focus. For parts not detailed in one embodiment, please refer to the relevant description of other embodiments.

[0046] As the preamble to the embodiments of the present application, in recent years, lithium-ion batteries have been widely used in electric vehicles, hybrid new energy vehicles, plug-in hybrid new energy vehicles, energy storage, low-speed vehicles and other fields. Lithium-ion batteries have the characteristics of long cycle life, high energy density, no memory effect, low self-discharge rate, high operating voltage, and environmental friendliness. As a core component of new energy vehicles, power lithium-ion batteries need to improve performance such as energy density and charge and discharge rate, and safety monitoring is also particularly important. However, when there are single cells with abnormal self-discharge in the lithium-ion battery, as the use time increases, these cells will gradually show characteristics different from other batteries, resulting in an increase in the pressure difference of the entire battery system and a shortened cruising range. If these defective batteries cannot be handled in time, there will be a great safety hazard and may even cause a thermal runaway accident. At present, the battery self-discharge anomaly can be monitored by setting a pressure difference alarm threshold on the battery management system (BMS). When the pressure difference of the battery system exceeds the threshold, the vehicle side will issue an alarm and restrict the vehicle's usage scenario. It can also be monitored in real time through cloud background data. Vehicles with abnormal self-discharge cells will be reminded to arrange a car for processing. However, the pressure difference alarm threshold set by the BMS is usually large and can only judge the current data, and cannot be compared with historical operating data to determine whether the battery has deteriorated. Identifying abnormal cells with smaller pressure differences through historical data comparison requires after-sales personnel to arrange a car, but there may be a certain delay in the process of after-sales personnel making the arrangement. This is because after-sales personnel need to receive reminders, arrange time and resources, and negotiate with the car owner to agree on a processing time. During this delay, the deterioration of the battery cell may continue, increasing the risk of safety accidents.

[0047] In view of this, an embodiment of the present application provides a method for detecting abnormal battery self-discharge, aiming to solve at least one of the above technical problems.

[0048] See also Figure 1 As shown, Figure 1 A flowchart of a method for detecting abnormal battery self-discharge is provided in an embodiment of the present application. The present application implements a method for detecting abnormal battery self-discharge, comprising: obtaining charge data of the battery at a current time point and multiple preset sampling time points before the current time point; determining first charge change data of the battery, where the first charge change data is the difference between the charge data at the first sampling time point and the charge data at the current time point, where the first sampling time point is any one of the multiple preset sampling time points; and determining a self-discharge detection result of the battery based on a first interval duration between the current time point and the first sampling time point, the preset interval duration, the first charge change data, and a preset charge change threshold.

[0049] It should be understood that the battery is composed of a combination of cells; battery self-discharge means that when not in use, even if there is no external load connected, the battery will still discharge itself, causing the battery's stored power to gradually decrease. Abnormal battery self-discharge means that the self-discharge rate is abnormally high, which may cause problems such as battery capacity loss and shortened service life; the preset interval time and the preset charge change threshold are the boundary value settings corresponding to the battery self-discharge abnormality, which help to determine whether the battery has abnormal self-discharge. It should also be noted that the first charge change data is the absolute value of the corresponding difference;

[0050] For example, the charge data at the current time point and multiple preset sampling time points before the current time point, wherein the charge data can be taken after the vehicle meets the parking requirement of ≥ 2h and the vehicle battery SOC meets the requirement of between 60% and 90%, and the time D is recorded through the interaction between BMS, T box and gateway. i and charge data C i , time D i and charge data C i For a group of data, record n ​​groups of data. It should be noted that n groups of data will be updated as the recorded content increases. For example, the n+1th group of data will be assigned to the nth group of data, the nth group of data will be assigned to the n-1th group of data, and so on. The second group of data will be assigned to the first group of data; further, the first interval length is △D1=D n -D a , the first charge change data is △C1=C n -C a , where D n is the time corresponding to the current time point, D a is the time corresponding to the first sampling time point, C n is the charge data at the current time point, C a The charge data at the first sampling time point. It should be noted that the unit of the first interval duration can be designed according to the actual needs of the solution, and can be days or hours, and this application does not limit this. In addition, the preset interval duration can be greater than the duration corresponding to n groups of data, or less than the duration corresponding to n groups of data, or equal to the duration corresponding to n groups of data, and it can be designed according to the actual needs of the solution, and this application does not limit this.

[0051] Through the above technical solution, the present application compares the current data with the historical data by comparing the charge data at the current time point and multiple preset sampling time points before the current time point, and determines the first interval duration and the first charge change data corresponding to the battery between the first sampling time point and the current time point. In combination with the preset interval duration and the preset charge change threshold, it is possible to promptly determine whether the battery self-discharge is abnormal, thereby promptly discovering potential problems and avoiding continuous deterioration of the battery. In addition, the present application determines the self-discharge detection result of the battery based on the first interval duration, the preset interval duration, the first charge change data and the preset charge change threshold between the current time point and the first sampling time point. By combining the preset interval duration and the preset charge change threshold to determine the self-discharge detection result of the battery, the self-discharge span of the battery charge change can be more comprehensively considered to determine the self-discharge of the battery, avoiding the misjudgment that may result from relying solely on a single condition, thereby improving the accuracy of the detection; and, by adjusting the preset interval duration and the preset charge change threshold to adapt to different battery types and application scenarios, it can be flexibly adjusted according to actual conditions to improve the accuracy and applicability of the self-discharge detection. In addition, the setting of a preset interval length means that only a moderate amount of historical charge data needs to be stored and analyzed, which greatly reduces the complexity and storage requirements of data processing, and can significantly reduce the operating costs and resource consumption of the system; and because the amount of data is reduced, the detection process is simplified, thereby improving the response speed.

[0052] In some embodiments, the battery includes multiple battery cells; the method for determining the charge data of the battery includes: determining the charge state of each battery cell; determining the maximum charge state and the minimum charge state from the charge states corresponding to each battery cell; and determining the maximum value difference corresponding to the maximum charge state and the minimum charge state as the charge data of the battery.

[0053] For example, the battery includes multiple cells, and the battery charge data C i =SOC max -SOC min , among which, SOC max is the maximum SOC value of each cell, SOC min is the minimum SOC value of each cell.

[0054] It should be understood that the state of charge of a battery cell is SOC, which represents the ratio between the amount of charge currently stored in the battery and its full capacity. In this application, the abnormal scenario to be detected is the abnormal battery cell self-discharges too quickly, resulting in a large SOC difference in the entire pack, where SOC difference = maximum SOC - minimum SOC. Therefore, the change in maximum value - minimum value can be used to calculate the rate of abnormal self-discharge. Therefore, determining the maximum value difference corresponding to the maximum state of charge and the minimum state of charge as the battery charge data can better reflect the capacity imbalance between different battery cells in the battery, and monitoring the difference between the maximum state of charge and the minimum state of charge can provide more stable and accurate results, reduce the influence of noise in the monitoring process, and thus obtain more reliable charge data.

[0055] In some embodiments, the battery includes a battery cell; and the method for determining the charge data of the battery includes: determining the charge state of the battery cell as the charge data of the battery.

[0056] It should be understood that, based on the fact that batteries may include one or more cells in different applications and designs, for a battery containing only one cell, the state of charge of the cell is determined as the battery charge data; for a battery containing multiple cells, the difference between the maximum state of charge and the minimum state of charge is determined as the battery charge data; the method of the present application is applicable to batteries in these different situations. This makes the application more widely applicable and can meet various application requirements.

[0057] See also Figure 2 As shown, Figure 2 A flowchart for determining a battery self-discharge detection result is provided for an embodiment of the present application. In some embodiments, the self-discharge detection result includes normal self-discharge and abnormal self-discharge; based on a first interval duration, a preset interval duration, a first charge change data, and a preset charge change threshold between a current time point and a first sampling time point, the self-discharge detection result of the battery is determined, including: if the first charge change data is greater than or equal to the preset charge change threshold, and the first interval duration is greater than the preset interval duration; sequentially determining each preset sampling time point between the first sampling time point and the current time point as a second sampling time point, determining a second charge change data of the battery, the second charge change data being the difference between the charge data at the second sampling time point and the charge data at the current time point; and determining the battery self-discharge detection result based on the second interval duration, the preset interval duration, the second charge change data, and the preset charge change threshold between the current time point and the second sampling time point, until the battery self-discharge is determined to be abnormal or all preset sampling time points are determined to be second sampling time points.

[0058] For example, the preset interval length is 10 days, and the preset charge change threshold is 3%. When △D1>10 days and △C1≥3%, the interval length needs to be further shortened to determine whether the battery self-discharge is abnormal. Specifically, based on the order of the interval length from the current time point, the first sampling time point D a and the current time point D n Each preset sampling time point between is determined as the second sampling time point D b , and then determine the second charge change data of the battery △C2=C n -C b , where C b is the charge data at the second sampling time point, and the second interval duration is △D2=D n -D b ; Based on the second interval length between the current time point and the second sampling time point, the preset interval length, the second charge change data and the preset charge change threshold, the self-discharge detection result of the battery is determined. If △D2>10 days and △C2≥3%, the next second sampling time point is determined until the battery self-discharge abnormality is determined or the first sampling time point D a and the current time point D n By gradually shortening the interval and calculating the charge change, it is possible to more accurately determine whether the battery self-discharge is abnormal.

[0059] In some embodiments, the method further includes: if the first charge change data is less than a preset charge change threshold, determining that the battery self-discharge is normal.

[0060] For example, if the preset interval is 10 days and the preset charge change threshold is 3%, then when ΔC1 < 3%, the battery self-discharge can be directly determined to be normal without considering the specific interval. This simplifies the judgment process and allows for faster evaluation of battery self-discharge test results in practical applications.

[0061] In some embodiments, the method further includes: if the first charge change data is greater than or equal to a preset charge change threshold, and the first interval duration is less than or equal to a preset interval duration, determining that the battery self-discharge is abnormal.

[0062] For example, the preset interval duration is 10 days, and the preset charge change threshold is 3%. When △D1≤10 days and △C1≥3%, it means that the battery charge has changed significantly in a short period of time, exceeding the preset charge change threshold, and the battery self-discharge is determined to be abnormal.

[0063] Through the above technical solution, the present application determines the self-discharge detection result of the battery by comparing the relationship between the first interval duration and the preset interval duration, and the relationship between the first charge change data and the preset charge change threshold. On the one hand, it comprehensively considers the time and battery charge change span from multiple aspects to determine the self-discharge of the battery, avoiding misjudgment that may be caused by relying solely on a single condition, thereby improving the accuracy of the detection; on the other hand, by considering the comparison relationship between the first interval duration and the preset interval duration, the self-discharge of the battery within a specific time period can be judged more accurately, thereby promptly discovering potential problems and avoiding continuous deterioration of the battery.

[0064] In some embodiments, a method for determining a preset charge change threshold includes: determining the self-discharge difference data of the test cell based on the self-discharge data set of the test cell; determining the corresponding open circuit voltage change rate of the test cell within the preset state of charge range based on the open circuit voltage of the test cell within the preset state of charge range; and determining the preset charge change threshold based on the self-discharge difference data, the open circuit voltage change rate and the preset interval duration.

[0065] It should be understood that the self-discharge data set of the test cell includes the self-discharge data corresponding to multiple test cells. The open circuit voltage of the cell refers to the voltage of the cell when there is no load or current flowing; the self-discharge data of the test cell is mainly the change data of the cell voltage recorded when the cell loses its charge on its own when it is not connected to any load or external circuit. It should be noted that the test cells in this application are defaulted to cells without abnormalities, that is, the test cells are assumed to be operating normally within the standard or expected performance parameter range without obvious defects or faults. In addition, the preset state of charge interval can be 60% to 90%. The state of charge interval of 60% to 90% provides stable open circuit voltage change data, which can more reliably judge the self-discharge of the battery, and can eliminate the influence of the open circuit voltage change of the battery at extremely low or extremely high states of charge on the judgment of self-discharge abnormality, which can improve the accuracy of the judgment and avoid misjudgment.

[0066] For example, the open circuit voltage data of the battery cell is used to calculate the voltage change corresponding to each 1% change in the SOC of the battery cell in the range of 60% to 90% SOC. The open circuit voltage change rate V = (OCV1-OCV2) / (90-60), where OCV1 is the open circuit voltage of the battery cell at 90% state of charge, and OCV2 is the open circuit voltage of the battery cell at 60% state of charge. It should be noted that the open circuit voltage change rate can also be the voltage change corresponding to each 2% change in the SOC of the battery cell in the range of 60% to 90% SOC, and so on. It can be designed according to the actual needs of the solution, and this application does not limit this.

[0067] In some embodiments, a method for determining self-discharge difference data includes: determining a target self-discharge data set after removing abnormal data from the self-discharge data set; determining the maximum value in the target self-discharge data set, wherein the maximum value includes a maximum value and a minimum value; and determining the self-discharge difference data based on the maximum value difference of the target self-discharge data set and a preset consistency coefficient.

[0068] Furthermore, in some embodiments, abnormal data in the self-discharge data set refers to data that is significantly different from most of the data in the self-discharge data set. Abnormal data can be determined by methods such as, but not limited to, box plots, Z-Score, isolation forests, etc. Exemplarily, taking a box plot as an example, the box plot consists of five main parts: minimum value, first quartile, median, third quartile and maximum value, and in the box plot, data that is less than Q1-1.5*IQR or greater than Q3+1.5*IQR will be determined as abnormal data and divided outside the box plot interval, where Q1 is the first quartile, Q3 is the third quartile, and IQR is the interquartile range, i.e., IQR=Q3-Q1. The maximum and minimum values ​​in the box plot interval obtained by substituting the self-discharge data set into the box plot model are determined as the maximum values ​​in the target self-discharge data set. Furthermore, the self-discharge difference data k=(k max -k min )*a, where k max is the maximum value of self-discharge data within the box plot interval, k min is the minimum self-discharge data value within the box plot interval; a is the manufacturing consistency coefficient, which is related to the level of production consistency control and generally ranges from 1.2 to 1.5. It should be noted that the unit of the self-discharge difference data k can be mV / day or mV / hour, depending on the actual needs of the solution and is not limited in this application.

[0069] In some embodiments, the manufacturing consistency coefficient a is related to the production consistency control level and can be a production experience value. For example, the same type of battery cells produced on different production lines and at different times will have different self-discharge difference data levels. The consistency coefficient is obtained based on the distribution difference of self-discharge difference data of battery cells produced in large quantities on the production line. The empirical value of 1.2 to 1.5 can be taken. That is, the manufacturing consistency coefficient a can be any value among 1.2, 1.3, 1.4, 1.5 or a range value between any two values.

[0070] In other embodiments, the consistency coefficient a can also be determined by the following method:

[0071] Based on the self-discharge data sets corresponding to multiple production lines, determine the self-discharge difference data corresponding to each production line; the self-discharge data set corresponding to each production line includes the self-discharge data corresponding to each test cell produced by the production line;

[0072] Based on the self-discharge difference data corresponding to each production line, the manufacturing consistency coefficient is determined.

[0073] In some embodiments, a method for determining the self-discharge difference data corresponding to each production line includes:

[0074] After removing abnormal data from the self-discharge dataset corresponding to each production line, the target self-discharge dataset corresponding to each production line is determined;

[0075] Determine the maximum value in the target self-discharge data set corresponding to each production line, including the maximum and minimum values;

[0076] The maximum value difference in the target self-discharge data set corresponding to each production line is used as the self-discharge difference data corresponding to each production line.

[0077] Abnormal data in the self-discharge dataset corresponding to each production line refers to data that is significantly different from the majority of data in the self-discharge dataset. Abnormal data can be identified using methods such as, but not limited to, box plots, Z-scores, and isolation forests. For example, the self-discharge difference dataset corresponding to each production line is substituted into a box plot model to obtain the maximum and minimum values ​​within the box plot interval, which are then determined as the maximum values ​​in the target self-discharge dataset.

[0078] It should be noted that the self-discharge data set corresponding to each production line includes the self-discharge data corresponding to each test cell produced by the production line. Among them, all the cells produced by the corresponding production line can be included as test cells to obtain self-discharge data, and part of the cells produced by the corresponding production line can also be included as test cells to obtain self-discharge data. For example, the cells produced by the production line can be sampled according to predetermined rules; cells produced within a specific time period during the production process of the production line, cells from a specific batch, or cells with a specific numbering pattern can be selected based on the cell production serial number. The self-discharge data is obtained by performing a self-discharge test on the selected test cells. The test can measure the voltage change value of the test cell within a specified test cycle under specific environmental conditions and in accordance with the standard test process. And the production line includes but is not limited to a specified specific production line, an arbitrarily selected part of the production line, or all the production lines.

[0079] For example, if there are n production lines, the self-discharge difference data k1 calculated for the first production line is k1=(k1 max -k1 min ), where k1 max k1 is the maximum value of the self-discharge data set corresponding to the first production line within the box plot interval, min is the minimum value of the self-discharge data set corresponding to the first production line within the box plot interval. Similarly, the self-discharge difference data kn calculated for the nth production line, kn=(kn max -knmin ), where kn max kn is the maximum value of the self-discharge data set corresponding to the nth production line within the box plot interval, min is the minimum self-discharge data value within the box plot interval of the self-discharge data set corresponding to the n-th production line. Furthermore, based on the n self-discharge difference data (k1, k2…kn) corresponding to the n production lines, the maximum and minimum values ​​of the n self-discharge difference data can be obtained, and the ratio of the maximum and minimum values ​​of the n self-discharge difference data is determined as the production consistency coefficient a, that is, the production consistency coefficient a = max(k1, k2, k3…kn) / min(k1, k2, k3…kn). It can be understood that obtaining the production consistency coefficient a can intuitively reflect the degree of consistency of the self-discharge characteristics of each production line, that is, reflect the level of production consistency control, thereby further improving the accuracy of the self-discharge difference data, and thus improving the detection accuracy of the battery self-discharge detection.

[0080] In some embodiments, a preset charge change threshold is determined based on self-discharge difference data, an open circuit voltage change rate, and a preset interval duration, including: determining a first threshold based on self-discharge difference data, a preset interval duration, an open circuit voltage change rate, and a preset redundancy coefficient; determining a second threshold based on the absolute value of the fluctuation range of the voltage acquisition accuracy and the open circuit voltage change rate; and determining the maximum value of the first threshold and the second threshold as the preset charge change threshold.

[0081] Specifically, the first threshold can be (k*b*△D) / V, where b is a preset redundancy coefficient, that is, b is the abnormal redundancy coefficient of the cell self-discharge difference data k value. Considering factors such as temperature consistency and data fluctuation, the value is generally greater than 2; △D is the preset interval length; the second threshold can be V m / V, where V m It is the absolute value of the fluctuation range of the voltage acquisition accuracy, that is, it can be the absolute value of the fluctuation range of the voltage acquisition accuracy of the voltage sensor, and the unit is mV.

[0082] In some embodiments, the k-value level for self-discharge variance data derived from battery cell production data is based on data at the same temperature. However, temperatures at different locations within the actual battery pack can vary, and fluctuations in vehicle-collected data can also affect the calculation results. Furthermore, a redundancy factor can be determined by calculating the self-discharge variance data for individual battery cell voltages in the vehicle-uploaded data. The empirical value is generally no less than 2. For example, it can be any value among 2.1, 2.5, 3, 3.5, or a range between any two values, depending on the specific implementation.

[0083] In other embodiments, the preset redundancy coefficient b may also be determined by the following method:

[0084] Preset redundancy factor b=V T / V, where V T It is the maximum value of the open circuit voltage change rate within the preset state of charge range within the allowable operating temperature range of the battery cell. V is the open circuit voltage change rate, and V can be the open circuit voltage change rate at any temperature within the allowable operating temperature range of the battery cell, such as room temperature. It is understandable that under the same SOC state, the open circuit voltage of the battery at different temperatures will be different, V T Represents the extreme case of the influence of temperature on the rate of change of open circuit voltage, then the preset redundancy coefficient b can clearly reflect the multiple relationship of the rate of change of open circuit voltage under the influence of maximum temperature compared to the rate of change of open circuit voltage at a specific temperature, thereby accurately quantifying the degree of influence of temperature change on the rate of change of open circuit voltage. Therefore, k*b can more accurately reflect the change of pressure difference caused by self-discharge under different temperature conditions, and provides a key parameter related to temperature for judging abnormal self-discharge, so that the judgment result is more in line with the actual situation. Furthermore, in this application, k*b is the pressure difference increment after judging abnormal self-discharge of the battery cell, △D is the preset interval length, and the two are multiplied to obtain the pressure difference growth value caused by self-discharge within the △D time, and then the pressure difference growth value / V can be converted into the growth of SOC difference for unified calculation.

[0085] With the above technical solution, the first threshold is used to determine whether the battery's self-discharge exceeds the normal range; the second threshold is used to determine whether the voltage change exceeds the error range of the voltage acquisition accuracy. The maximum value of the first and second thresholds in the first threshold data is determined as the preset charge change threshold based on a comprehensive consideration of the battery's self-discharge and voltage change accuracy, avoiding misjudgments within the normal error range.

[0086] Furthermore, if the battery self-discharge detection result shows abnormal self-discharge, the BMS can issue an alarm, limit the battery's charge and discharge power and SOC operating window to a safe range, and remind customers to carry out repairs to ensure that defective batteries are processed as soon as possible.

[0087] See also Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of a battery self-discharge abnormality detection device provided in an embodiment of the present application. Correspondingly, the present application also provides a battery self-discharge abnormality detection device, including:

[0088] The first module is used to obtain the charge data of the battery at the current time point and multiple preset sampling time points before the current time point;

[0089] The second module is used to determine first charge change data of the battery, where the first charge change data is the difference between the charge data at a first sampling time point and the charge data at a current time point, and the first sampling time point is any one of a plurality of preset sampling time points;

[0090] The third module is used to determine the self-discharge detection result of the battery based on the first interval duration between the current time point and the first sampling time point, the preset interval duration, the first charge change data and the preset charge change threshold.

[0091] It should be noted that the present application provides a device for detecting abnormal battery self-discharge, including the method for detecting abnormal battery self-discharge as described in the above embodiment. Therefore, it can have all the technical features and technical effects of the above method for detecting abnormal battery self-discharge, which will not be repeated here.

[0092] The present application also provides an electronic device, comprising: at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor implements the method for detecting battery self-discharge abnormality in the above embodiment.

[0093] It should be noted that the present application provides an electronic device including the battery self-discharge abnormality detection method as described in the above embodiment. Therefore, it can have all the technical features and technical effects of the above battery self-discharge abnormality detection method, which will not be repeated here.

[0094] The present application also provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to execute the method for detecting abnormal battery self-discharge in the above embodiment.

[0095] It should be noted that the present application provides a computer-readable storage medium including the battery self-discharge abnormality detection method as described in the above embodiment. Therefore, it can have all the technical features and technical effects of the above battery self-discharge abnormality detection method, which will not be repeated here.

[0096] The above is a detailed introduction to a method and device for detecting abnormal battery self-discharge provided in an embodiment of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, based on the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for detecting abnormal battery self-discharge, characterized in that: include: Obtaining charge data of the battery at the current time point and multiple preset sampling time points before the current time point; Determine first charge change data of the battery, where the first charge change data is a difference between charge data at a first sampling time point and charge data at the current time point, and the first sampling time point is any one of the plurality of preset sampling time points; A self-discharge detection result of the battery is determined based on a first interval duration between the current time point and the first sampling time point, a preset interval duration, the first charge change data, and a preset charge change threshold.

2. The method for detecting abnormal battery self-discharge according to claim 1, wherein: The self-discharge detection result includes normal self-discharge and abnormal self-discharge; and determining the self-discharge detection result of the battery based on a first interval duration between the current time point and the first sampling time point, the preset interval duration, the first charge change data, and the preset charge change threshold includes: If the first charge change data is greater than or equal to the preset charge change threshold, and the first interval duration is greater than the preset interval duration; Each of the preset sampling time points between the first sampling time point and the current time point is sequentially determined as a second sampling time point, and second charge change data of the battery is determined, where the second charge change data is the difference between the charge data at the second sampling time point and the charge data at the current time point. Based on the second interval duration between the current time point and the second sampling time point, the preset interval duration, the second charge change data, and the preset charge change threshold, a self-discharge detection result of the battery is determined until it is determined that the battery self-discharge is abnormal or all the preset sampling time points are determined to be the second sampling time points.

3. The method for detecting abnormal battery self-discharge according to claim 2, wherein: The method further comprises: If the first charge change data is less than the preset charge change threshold, it is determined that the battery self-discharge is normal.

4. The method for detecting abnormal battery self-discharge according to claim 2, wherein: The method further comprises: If the first charge change data is greater than or equal to the preset charge change threshold, and the first interval duration is less than or equal to the preset interval duration, it is determined that the battery self-discharge is abnormal.

5. The method for detecting abnormal battery self-discharge according to claim 1, wherein: The method for determining the preset charge change threshold includes: Determining self-discharge difference data of the test cell based on a self-discharge data set of the test cell; Determining a corresponding open circuit voltage change rate of the test cell within the preset state of charge range based on the open circuit voltage of the test cell within the preset state of charge range; The preset charge change threshold is determined based on the self-discharge difference data, the open circuit voltage change rate, and the preset interval time.

6. The method for detecting abnormal battery self-discharge according to claim 5, characterized in that: The method for determining the self-discharge difference data includes: Determine the self-discharge data set as a target self-discharge data set after removing abnormal data; Determining a maximum value in the target self-discharge data set, wherein the maximum value includes a maximum value and a minimum value; The self-discharge difference data is determined based on the maximum difference of the target self-discharge data set and a preset consistency coefficient.

7. The method for detecting abnormal battery self-discharge according to claim 5, wherein: The determining the preset charge change threshold based on the self-discharge difference data, the open circuit voltage change rate, and the preset interval duration includes: determining a first threshold based on the self-discharge difference data, the preset interval duration, the open circuit voltage change rate, and a preset redundancy coefficient; Determining a second threshold based on the absolute value of the fluctuation range of the voltage acquisition accuracy and the open circuit voltage change rate; The maximum value of the first threshold and the second threshold is determined as the preset charge variation threshold.

8. The method for detecting abnormal battery self-discharge according to claim 1, wherein: The battery includes a plurality of battery cells; and the method for determining the charge data of the battery includes: determining a state of charge of each of the battery cells; Determining a maximum state of charge and a minimum state of charge from the states of charge corresponding to each of the battery cells; A maximum difference between the maximum state of charge and the minimum state of charge is determined as charge data of the battery.

9. The method for detecting abnormal battery self-discharge according to claim 1, wherein: The battery includes a battery cell; the method for determining the charge data of the battery includes: The state of charge of the cell is determined as charge data of the battery.

10. A device for detecting abnormal battery self-discharge, characterized in that: include The first module is used to obtain the charge data of the battery at the current time point and multiple preset sampling time points before the current time point; a second module, configured to determine first charge change data of the battery, where the first charge change data is a difference between charge data at a first sampling time point and charge data at the current time point, where the first sampling time point is any one of the plurality of preset sampling time points; The third module is used to determine the self-discharge detection result of the battery based on the first interval duration between the current time point and the first sampling time point, the preset interval duration, the first charge change data and the preset charge change threshold.

Images