A method and device for detecting lithium precipitation of a lithium ion battery

Abstract

The application discloses a lithium ion battery lithium precipitation detection method and device, the method comprises the following steps: obtaining historical charging data of a lithium ion battery in a preset historical time interval, screening a plurality of target charging processes from the historical charging data, the target charging process is a charging process with a SOC value not less than a preset threshold at the end of charging, and the lithium ion battery comprises a plurality of battery cells; determining the charging number of each target charging process under the charging mode to which each target charging process belongs according to the time sequence of each target charging process; determining whether a target group exists; if the target group exists, determining the lithium precipitation detection result according to the internal resistance value and the charging capacity value of each battery cell in each target charging process and the charging number of each target charging process, judging the lithium precipitation condition of the battery through the abnormal change of the internal resistance / capacity, qualitatively analyzing the severity of the lithium precipitation, and realizing the rapid and accurate detection of the lithium precipitation of the lithium ion battery.

Description

Technical Field

[0001] This application relates to the field of electric vehicle technology, and more specifically, to a method and apparatus for detecting lithium plating in lithium-ion batteries. Background Technology

[0002] As a core component of new energy vehicles, power lithium-ion batteries require not only improved energy density and charge / discharge rate, but also crucially safe and reliable operation. However, with increased usage time, charging cycles, and abnormal operating conditions, polarization can occur, and persistent polarization can easily lead to lithium plating. Lithium plating causes two problems: firstly, lithium ions are reduced to lithium metal and deposited on the negative electrode surface, reducing active material and drastically decreasing battery capacity; secondly, the deposited lithium metal on the negative electrode surface can form lithium dendrites, which may puncture the separator, causing internal short circuits, thermal runaway, and safety accidents. Therefore, lithium plating detection in power lithium-ion batteries and early identification of safety risks are of paramount importance for safety warnings.

[0003] In existing technologies, there are three main aspects to the detection of lithium plating: First, disassembling the battery cell and using nuclear magnetic resonance (NMR) technology to analyze the negative electrode material to detect whether lithium plating exists on the negative electrode surface. This method is destructive and irreversible. Second, testing the battery through external characteristic experiments such as cyclic voltammetry and analyzing the data to determine whether lithium plating exists. This method is non-destructive to the battery, but has low detection sensitivity and can only identify obvious lithium plating under extremely abnormal operating conditions. Third, data-based non-destructive testing methods. This method can monitor vehicles online, but it has excessively high data requirements, making it difficult to filter out data that meets the testing requirements. Furthermore, this method observes voltage changes, and the voltage changes caused by lithium plating are very small, making it difficult to observe voltage anomalies and resulting in low detection accuracy.

[0004] Therefore, how to quickly and accurately detect lithium plating in lithium-ion batteries is a technical problem that needs to be solved. Summary of the Invention

[0005] This application provides a method and apparatus for detecting lithium plating in lithium-ion batteries, enabling rapid and accurate detection of lithium plating in lithium-ion batteries.

[0006] In a first aspect, a method for detecting lithium plating in lithium-ion batteries is provided, the method comprising:

[0007] The historical charging data of the lithium-ion battery within a preset historical time interval is obtained, and multiple target charging processes are selected from the historical charging data. The target charging process is a charging process in which the SOC value is not less than a preset threshold at the end of charging. The lithium-ion battery includes multiple cells.

[0008] The charging count number of each target charging process under its own charging mode is determined according to the time sequence of each target charging process.

[0009] Determine whether a target group exists, wherein the target group consists of a target charging process of not less than a preset number and employing one of the charging methods;

[0010] If present, the result of lithium plating detection is determined based on the internal resistance and charging capacity of each cell in the target group during each target charging process, as well as the charging number of each cell.

[0011] Secondly, a detection device for lithium plating in lithium-ion batteries is provided, the device comprising:

[0012] The filtering module is used to acquire historical charging data of lithium-ion batteries within a preset historical time interval, and to filter out multiple target charging processes from the historical charging data. The target charging process is a charging process in which the SOC value is not less than a preset threshold at the end of charging. The lithium-ion battery includes multiple cells.

[0013] The first determining module is used to determine the charging number of each target charging process under its own charging mode according to the time sequence of each target charging process;

[0014] The judgment module is used to determine whether a target group exists, wherein the target group consists of a target charging process of not less than a preset number and using one of the charging methods;

[0015] The second determining module is used to determine the result of lithium plating detection based on the internal resistance value and charging capacity value of each cell in the target group during each target charging process, and the charging number of each cell, if present.

[0016] By applying the above technical solutions, historical charging data of lithium-ion batteries within a preset historical time interval is obtained. Multiple target charging processes are then selected from this data. A target charging process is defined as a charging process where the SOC value at the end of charging is not less than a preset threshold. A lithium-ion battery comprises multiple cells. The charging count number of each target charging process under its respective charging method is determined based on the time sequence of each target charging process. It is then determined whether a target group exists, where each target group consists of at least a preset number of target charging processes using a single charging method. If such a group exists, the lithium plating detection result is determined based on the internal resistance and charging capacity values ​​of each cell within the target group during each target charging process, as well as the charging count number. Abnormal changes in internal resistance / capacity are used to assess the lithium plating situation, qualitatively analyzing the severity of lithium plating. This enables rapid and accurate detection of lithium plating in lithium-ion batteries, improving their safety and reliability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic flowchart of a lithium-ion battery lithium plating detection method according to an embodiment of the present invention is shown.

[0019] Figure 2 A schematic flowchart of a lithium-ion battery lithium plating detection method according to another embodiment of the present invention is shown.

[0020] Figure 3 A schematic diagram of a lithium-ion battery lithium plating detection device according to an embodiment of the present invention is shown. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] This application provides a method for detecting lithium plating in lithium-ion batteries, such as... Figure 1 As shown, the method includes the following steps:

[0023] Step S101: Obtain historical charging data of the lithium-ion battery within a preset historical time interval, and filter out multiple target charging processes from the historical charging data. The target charging process is a charging process in which the SOC value is not less than a preset threshold at the end of charging. The lithium-ion battery includes multiple cells.

[0024] In this embodiment, the lithium-ion battery includes multiple cells. The historical charging data is data generated during each charging process within a preset historical time interval, and may include data such as time, current, voltage, SOC (State of Charge) value, temperature, and charging status. This data is collected by the vehicle's BMS (Battery Management System). The execution entity for the lithium-ion battery lithium plating detection method can be a server located in the cloud, which can wirelessly communicate with the BMS to obtain the historical charging data from the BMS. Alternatively, the execution entity can be a lithium-ion battery lithium plating detection device installed in the vehicle, which can be connected to the BMS via wired or wireless means to obtain the historical charging data from the BMS.

[0025] The system selects charging processes from historical charging data where the SOC value at the end of charging is not less than a preset threshold, and uses these as target charging processes. Optionally, the preset threshold is 95%.

[0026] To improve detection accuracy, in some embodiments of this application, after acquiring historical charging data of the lithium-ion battery within a preset historical time interval, the method further includes:

[0027] The historical charging data is cleaned to remove invalid data.

[0028] Step S102: Determine the number of times each target charging process is charged under its own charging mode according to the time sequence of each target charging process.

[0029] In this embodiment, the user can use different charging methods to charge. Each charging method may include multiple target charging processes. Since the charging data generated under different charging methods are quite different, the charging number of each target charging process under its own charging method is determined according to the time sequence of each target charging process. For example, the charging number of the first target charging process is 1, the charging number of the second target charging process is 2, and so on, with the charging number of the Nth target charging process being N.

[0030] To accurately determine the charging count number, in some embodiments of this application, the charging method includes fast charging or slow charging. The charging count number of each target charging process under its respective charging method is determined according to the time sequence of each target charging process, specifically as follows:

[0031] The target charging process using fast charging is divided into a fast charging group, and the target charging process using slow charging is divided into a slow charging group. The target charging processes in the fast charging group and the slow charging group are sorted according to time sequence, and the charging number of each target charging process in the fast charging group or the slow charging group is determined.

[0032] In this embodiment, the lithium-ion battery can be charged using either fast charging or slow charging. Since the charging data generated by the two methods differs significantly, they are processed separately. Target charging processes using fast charging are grouped into a fast charging group, and those using slow charging are grouped into a slow charging group. Each target charging process occurs at a different time; therefore, the target charging processes within each fast charging and slow charging group are sorted chronologically to determine the charging count number of each target charging process within the fast charging or slow charging group. The charging count number for the Nth target charging process within either the fast charging or slow charging group is denoted as N. For example, if the fast charging group and the slow charging group each include 30 target charging processes, in the fast charging group, the charging number of the first target charging process is 1, the charging number of the second target charging process is 2, and so on, with the charging number of the last target charging process being 30; in the slow charging group, the charging number of the first target charging process is 1, the charging number of the second target charging process is 2, and so on, with the charging number of the last target charging process being 30.

[0033] Step S103: Determine whether a target group exists, wherein the target group consists of a target charging process of not less than a preset number and using one of the charging methods.

[0034] In this embodiment, to improve detection accuracy, the number of target charging processes using the same charging method needs to reach a preset number to ensure sufficient data volume. Therefore, when the number of target charging processes using the same charging method is not less than the preset number, they are considered as a target group, and subsequent processing is performed based on this target group.

[0035] Step S104: If present, determine the result of lithium plating detection based on the internal resistance and charging capacity of each cell in the target group during each target charging process, and the charging number of each cell.

[0036] In this embodiment, the existence of a target group indicates that the amount of data within the target group is sufficient. As the number of charging cycles increases, the possibility of lithium plating in the battery increases. If lithium plating occurs, the internal resistance and charging capacity of the cell will change accordingly during each target charging process. Therefore, the result of lithium plating detection can be determined based on the internal resistance and charging capacity of each cell in the target group during each target charging process, as well as the number of charging cycles.

[0037] Understandably, if the target group does not exist, new historical charging data will continue to be acquired.

[0038] To accurately determine the results of lithium plating detection, in some embodiments of this application, the results are determined based on the internal resistance and charging capacity of each cell within the target group during each target charging process, as well as the charging cycle number. Specifically:

[0039] The internal resistance values ​​of each cell are sorted from largest to smallest during each target charging process to determine the internal resistance ranking of each cell during each target charging process.

[0040] The charging capacity values ​​are sorted from smallest to largest during each target charging process to determine the capacity ranking of each cell during each target charging process.

[0041] The ranking ratio of each cell in the target group during each target charging process is determined based on the ratio of the internal resistance ranking to the capacity ranking.

[0042] The results are determined based on the ranking ratios and the charging count numbers.

[0043] In this embodiment, the internal resistance values ​​are sorted from largest to smallest to determine the internal resistance ranking in each target charging process, and the charging capacity values ​​are sorted from smallest to largest to determine the capacity ranking in each target charging process. Then, for each cell, the ranking ratio of the cell in each target charging process is determined according to the ratio of the internal resistance ranking to the capacity ranking. The lithium plating detection result can be determined according to each ranking ratio and each charging number.

[0044] It should be noted that the above embodiments are only one specific implementation scheme proposed in this application. Other methods of determining the lithium plating detection results based on each internal resistance value, charging capacity value, and charging number are all within the protection scope of this application.

[0045] To accurately determine the results of lithium plating detection, in some embodiments of this application, the results are determined based on the ranking ratios and the number of charging cycles, specifically as follows:

[0046] The ranking ratios and charging number numbers are fitted using the least squares method to obtain a target fitting curve, which represents the correspondence between the ranking ratios and the charging number numbers.

[0047] If there is a first target ranking ratio among the ranking ratios that is equal to a preset safety threshold, the first target ranking ratio is input into the expression of the target fitting curve to obtain the predicted value of the charging number.

[0048] The result is determined based on the predicted value.

[0049] In this embodiment, the least squares method is used to fit each ranking ratio and each charging number to obtain a target fitting curve. When the ranking ratio is equal to a preset safety threshold, it is used as the first target ranking ratio. The first target ranking ratio is input into the expression of the target fitting curve to obtain the predicted value of the charging number. The result of lithium plating detection can be determined by the predicted value.

[0050] Those skilled in the art may also use other methods to determine the target fitting curve, which does not affect the scope of protection of this application.

[0051] To more accurately determine the results of lithium plating detection, in some embodiments of this application, the results are determined based on the predicted values, specifically as follows:

[0052] The predicted value is converted into the number of natural days based on a preset correspondence.

[0053] If the number of days is not less than the first preset number of days, no lithium plating warning will be issued;

[0054] If the number of days is not less than the second preset number of days and less than the first preset number of days, a first-level warning for lithium plating is issued;

[0055] If the number of days is not less than the third preset number of days and less than the second preset number of days, a level 2 lithium plating warning will be issued;

[0056] If the number of days is not less than zero and is less than the third preset number of days, a level three warning for lithium plating is issued.

[0057] The preset correspondence is determined based on the correspondence between different charging times and different natural days in the historical charging data.

[0058] In this embodiment, a correspondence exists between different charging times and different natural days in historical charging data. This correspondence is used as a preset correspondence, and the predicted value of the charging number is converted into the number of natural days based on this preset correspondence. If the number of days is not less than the first preset number of days, it indicates that the lithium-ion battery does not have lithium plating, and no lithium plating warning is issued; new historical charging data continues to be acquired for monitoring. If the number of days is not less than the second preset number of days and less than the first preset number of days, it indicates that there is mild lithium plating, and a level one lithium plating warning is issued. If the number of days is not less than the third preset number of days and less than the second preset number of days, it indicates that there is moderate lithium plating, and a level two lithium plating warning is issued. If the number of days is not less than zero and less than the third preset number of days, it indicates that lithium plating is relatively severe, and a level three lithium plating warning is issued. Optionally, warnings can be issued through audible and visual alarms, and different audible and visual alarm signals can be used for different levels of warnings. In the specific application scenario of this application, the first preset number of days is 30, the second preset number of days is 15, and the third preset number of days is 5.

[0059] Optionally, the predicted value may not be converted into the number of calendar days, and the decision to issue an alert and the corresponding level of alert may be made directly based on the different predicted values; or the predicted value may be converted into other time lengths, such as hours, and the decision to issue an alert and the corresponding level of alert may be made based on the different hours.

[0060] To ensure the accuracy of lithium plating detection, in some embodiments of this application, the method further includes:

[0061] If, during the last target charging process within the target group, there is a second target ranking ratio that is not less than the preset safety threshold, a level three lithium plating warning is issued.

[0062] In this embodiment, if there is a second target ranking ratio of not less than a preset safety threshold in the last target charging process within the target group, it indicates that lithium plating is relatively serious, and a level three lithium plating warning is issued.

[0063] To improve the accuracy of lithium plating detection, in some embodiments of this application, the expression for the target fitting curve is as follows:

[0064] η i =Aj B +C

[0065] Where, η i Let j be the ranking ratio, j be the number of charging times, and A, B, and C be constants.

[0066] Those skilled in the art may also use other expressions for fitting, which does not affect the scope of protection of this application.

[0067] To improve the accuracy of lithium plating detection, in some embodiments of this application, the preset safety threshold is one-quarter of the number of batteries in series.

[0068] In this embodiment, the lithium-ion battery is composed of multiple battery cells connected in series. Each battery cell is a string, and each battery cell includes at least one cell. The number of battery cells is the number of strings.

[0069] Those skilled in the art may also use other values ​​as preset security thresholds, which does not affect the scope of protection of this application.

[0070] To obtain an accurate internal resistance ranking, in some embodiments of this application, before sorting the internal resistance values ​​from largest to smallest during each target charging process to determine the internal resistance ranking of each cell during each target charging process, the method further includes:

[0071] The internal resistance value is determined according to Formula 1, which is:

[0072] Ri=(Vsi–Vei) / Is

[0073] Wherein, Ri is the internal resistance value, Vsi is the cell voltage in the last data before the end of the target charging process, Vei is the cell voltage in the first data after the end of the target charging process, and Is is the cell current in the last data.

[0074] Those skilled in the art may also use other methods to determine the internal resistance value, which does not affect the scope of protection of this application.

[0075] To obtain an accurate capacity ranking, in some embodiments of this application, before sorting the charging capacity values ​​in ascending order during each target charging process and determining the capacity ranking of each cell during each target charging process, the method further includes:

[0076] The target voltage range is determined based on the difference between the highest voltage at the start of charging and the lowest voltage at the end of charging during the target charging process;

[0077] The charging capacity of the battery cell in the target voltage range is determined based on the ampere-hour integration method.

[0078] In this embodiment, the formula corresponding to the ampere-hour integration method is:

[0079]

[0080] Where Ci is the charging capacity value, t1 is the time corresponding to the highest charging start voltage, t2 is the time corresponding to the lowest charging stop voltage, and I represents the current value during the charging process.

[0081] Those skilled in the art may also use other methods to determine the filling capacity value, which does not affect the scope of protection of this application.

[0082] By applying the above technical solutions, historical charging data of lithium-ion batteries within a preset historical time interval is obtained. Multiple target charging processes are then selected from this data. A target charging process is defined as a charging process where the SOC value at the end of charging is not less than a preset threshold. A lithium-ion battery comprises multiple cells. The charging count number of each target charging process under its respective charging method is determined based on the time sequence of each target charging process. It is then determined whether a target group exists, where each target group consists of at least a preset number of target charging processes using a single charging method. If such a group exists, the lithium plating detection result is determined based on the internal resistance and charging capacity values ​​of each cell within the target group during each target charging process, as well as the charging count number. Abnormal changes in internal resistance / capacity are used to assess the lithium plating situation, qualitatively analyzing the severity of lithium plating. This enables rapid and accurate detection of lithium plating in lithium-ion batteries, improving their safety and reliability.

[0083] To further illustrate the technical concept of this invention, the technical solution of this invention will now be described in conjunction with specific application scenarios.

[0084] This application also proposes a method for detecting lithium plating in lithium-ion batteries, such as... Figure 2 As shown, it includes the following steps:

[0085] Step 1: Obtain historical charging data.

[0086] Historical charging data refers to the data generated by the lithium-ion battery during each charging process within a preset historical time interval. This data may include time, current, voltage, SOC value, temperature, charging status, etc. This data is collected by the BMS in the vehicle. The historical charging data is cleaned to remove invalid data.

[0087] Step 2: Check if the SOC value is ≥95% when charging is finished. If so, save it as the target charging process; otherwise, delete this charging data.

[0088] Step 3: Divide the charging groups into fast charging groups and slow charging groups, and determine the number of charging cycles within each group.

[0089] The target charging process using fast charging is divided into fast charging group and the target charging process using slow charging is divided into slow charging group. The target charging processes in the fast charging group and slow charging group are sorted according to time sequence, and the charging number of each target charging process in the fast charging group or slow charging group is determined.

[0090] Step 4: Does a target group exist? If a target group exists, calculate the internal resistance value and internal resistance ranking of each cell in the target group, calculate the charging capacity value and capacity ranking of each cell in the target group, and calculate the ranking ratio ηi. If no target group exists, continue to acquire historical charging data.

[0091] A group is considered a target group if the number of target charging processes within the fast charging group or slow charging group is not less than a preset number.

[0092] The internal resistance value of each cell in the target group during the charging process is determined according to Formula 1. Formula 1 is:

[0093] Ri=(Vsi–Vei) / Is

[0094] Where Ri is the internal resistance value, Vsi is the cell voltage in the last data before the end of the target charging process, Vei is the cell voltage in the first data after the end of the target charging process, and Is is the cell current in the last data.

[0095] The internal resistance values ​​are sorted from largest to smallest during each target charging process to determine the ranking of the internal resistance of each cell during each target charging process.

[0096] The target voltage range is determined based on the difference between the highest voltage at the start of charging and the lowest voltage at the end of charging during the target charging process;

[0097] The charging capacity of the battery cell in the target voltage range is determined based on the ampere-hour integration method.

[0098] In this embodiment, the formula corresponding to the ampere-hour integration method is:

[0099]

[0100] Where Ci is the charging capacity, t1 is the time corresponding to the highest voltage at the start of charging, t2 is the time corresponding to the lowest voltage at the end of charging, and I represents the current value during the charging process.

[0101] The charging capacity values ​​are sorted from smallest to largest during each target charging process to determine the capacity ranking of each cell during each target charging process.

[0102] The ranking ratio ηi of each cell in the target group during each target charging process is determined based on the ratio of internal resistance ranking to capacity ranking.

[0103] Step 5: In the last target charging process, does ηi≥1 / 4*S exist? If so, issue a level 3 lithium plating warning. Otherwise, perform curve fitting on the ranking ratio and charging number to obtain the target fitting curve.

[0104] Where S is the number of lithium-ion batteries connected in series.

[0105] The expression for the target fitted curve is:

[0106] η i =Aj B +C

[0107] Where, η i Let j be the ranking ratio, j be the number of charging cycles, and A, B, and C be constants.

[0108] By using the ranking ratio η i By substituting the charging number j into the above expression, the values ​​of A, B, and C can be obtained, and the target fitting curve can be determined.

[0109] Step 6: Predict the number of charging cycles when ηi = 1 / 4*S, and convert it into the number of natural days D.

[0110] Substitute ηi into the above expression to determine the predicted value of the charging number j, and convert the charging number j into the number of natural days D.

[0111] Step seven, handle the following situations:

[0112] If D≥30, no lithium plating warning will be issued, and historical charging data will continue to be acquired;

[0113] If 15 ≤ D < 30, a Level 1 warning for lithium plating is issued.

[0114] If 5 ≤ D < 15, a level 2 lithium plating warning will be issued.

[0115] If 0 ≤ D < 5, a level 3 warning for lithium plating is issued.

[0116] By applying the above technical solutions, it is possible to detect whether lithium-ion batteries are plating lithium and the degree of lithium plating online. The data requirements are low, and the data during the charging process is easy to monitor and extract. Moreover, this method qualitatively judges whether lithium plating is occurring and the severity of lithium plating by abnormal changes in internal resistance / capacity. The detection speed is fast and the detection accuracy is high, which improves the safety and reliability of lithium-ion batteries.

[0117] This application also proposes a detection device for lithium plating in lithium-ion batteries, such as... Figure 3 As shown, the device includes:

[0118] The filtering module 301 is used to acquire historical charging data of lithium-ion batteries within a preset historical time interval, and to filter out multiple target charging processes from the historical charging data. The target charging process is a charging process in which the SOC value is not less than a preset threshold at the end of charging. The lithium-ion battery includes multiple cells.

[0119] The first determining module 302 is used to determine the charging number of each target charging process under its own charging mode according to the time sequence of each target charging process;

[0120] The judgment module 303 is used to determine whether a target group exists, wherein the target group consists of a target charging process of not less than a preset number and using one of the charging methods;

[0121] The second determining module 304 is used to determine the result of lithium plating detection based on the internal resistance value and charging capacity value of each cell in the target group during each target charging process, and the charging number of each cell, if present.

[0122] In specific application scenarios, the charging method includes fast charging or slow charging. The first determining module 302 is specifically used for:

[0123] The target charging process using the fast charging method is divided into a fast charging group, and the target charging process using the slow charging method is divided into a slow charging group. The target charging processes in the fast charging group and the slow charging group are sorted according to time sequence, and the charging number of each target charging process in the fast charging group or the slow charging group is determined.

[0124] In specific application scenarios, the second determining module 304 is specifically used for:

[0125] The internal resistance values ​​of each cell are sorted from largest to smallest during each target charging process to determine the internal resistance ranking of each cell during each target charging process.

[0126] The charging capacity values ​​are sorted from smallest to largest during each target charging process to determine the capacity ranking of each cell during each target charging process.

[0127] The ranking ratio of each cell in the target group during each target charging process is determined based on the ratio of the internal resistance ranking to the capacity ranking.

[0128] The results are determined based on the ranking ratios and the charging count numbers.

[0129] In specific application scenarios, the second determining module 304 is also specifically used for:

[0130] The ranking ratios and charging number numbers are fitted using the least squares method to obtain a target fitting curve, which represents the correspondence between the ranking ratios and the charging number numbers.

[0131] If there is a first target ranking ratio among the ranking ratios that is equal to a preset safety threshold, the first target ranking ratio is input into the expression of the target fitting curve to obtain the predicted value of the charging number.

[0132] The result is determined based on the predicted value.

[0133] In specific application scenarios, the second determining module 304 is also specifically used for:

[0134] The predicted value is converted into the number of natural days based on a preset correspondence.

[0135] If the number of days is not less than the first preset number of days, no lithium plating warning will be issued;

[0136] If the number of days is not less than the second preset number of days and less than the first preset number of days, a first-level warning for lithium plating is issued;

[0137] If the number of days is not less than the third preset number of days and less than the second preset number of days, a level 2 lithium plating warning will be issued;

[0138] If the number of days is not less than zero and is less than the third preset number of days, a level three warning for lithium plating is issued.

[0139] The preset correspondence is determined based on the correspondence between different charging times and different natural days in the historical charging data.

[0140] In specific application scenarios, the second determining module 304 is also specifically used for:

[0141] If, during the last target charging process within the target group, there is a second target ranking ratio that is not less than the preset safety threshold, a level three lithium plating warning is issued.

[0142] In specific application scenarios, the expression for the target fitting curve is:

[0143] η i =Aj B +C

[0144] Where, η i Let j be the ranking ratio, j be the number of charging times, and A, B, and C be constants.

[0145] In specific application scenarios, the preset safety threshold is one-quarter of the number of batteries connected in series.

[0146] In specific application scenarios, the device further includes a first computing module, used for:

[0147] The internal resistance value is determined according to Formula 1, which is:

[0148] Ri=(Vsi–Vei) / Is

[0149] Wherein, Ri is the internal resistance value, Vsi is the cell voltage in the last data before the end of the target charging process, Vei is the cell voltage in the first data after the end of the target charging process, and Is is the cell current in the last data.

[0150] In specific application scenarios, the device further includes a second computing module, used for:

[0151] The target voltage range is determined based on the difference between the highest voltage at the start of charging and the lowest voltage at the end of charging during the target charging process;

[0152] The charging capacity of the battery cell in the target voltage range is determined based on the ampere-hour integration method.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for detecting lithium plating in lithium-ion batteries, characterized in that, The method includes: The historical charging data of the lithium-ion battery within a preset historical time interval is obtained, and multiple target charging processes are selected from the historical charging data. The target charging process is a charging process in which the SOC value is not less than a preset threshold at the end of charging. The lithium-ion battery includes multiple cells. The charging count number of each target charging process under its own charging mode is determined according to the time sequence of each target charging process. Determine whether a target group exists, wherein the target group consists of a target charging process of not less than a preset number and employing one of the charging methods; If present, the lithium plating detection result is determined based on the internal resistance and charging capacity values ​​of each cell in the target group during each target charging process, as well as the charging count number. Specifically, the internal resistance values ​​are sorted from largest to smallest during each target charging process to determine the internal resistance ranking of each cell in each target charging process; the charging capacity values ​​are sorted from smallest to largest during each target charging process to determine the capacity ranking of each cell in each target charging process; the ranking ratio of each cell in the target group during each target charging process is determined based on the ratio of the internal resistance ranking to the capacity ranking; and the result is determined based on the ranking ratio and the charging count number.

2. The method as described in claim 1, characterized in that, The charging method includes fast charging or slow charging. The charging count number for each target charging process under its respective charging method is determined based on the time sequence of each target charging process. Specifically: The target charging process using the fast charging method is divided into a fast charging group, and the target charging process using the slow charging method is divided into a slow charging group. The target charging processes in the fast charging group and the slow charging group are sorted according to time sequence, and the charging number of each target charging process in the fast charging group or the slow charging group is determined.

3. The method as described in claim 1, characterized in that, The results are determined based on the ranking ratios and the charging count numbers, specifically as follows: The ranking ratios and charging number numbers are fitted using the least squares method to obtain a target fitting curve, which represents the correspondence between the ranking ratios and the charging number numbers. If there is a first target ranking ratio among the ranking ratios that is equal to a preset safety threshold, the first target ranking ratio is input into the expression of the target fitting curve to obtain the predicted value of the charging number. The result is determined based on the predicted value.

4. The method as described in claim 3, characterized in that, The result is determined based on the predicted value, specifically as follows: The predicted value is converted into the number of natural days based on a preset correspondence. If the number of days is not less than the first preset number of days, no lithium plating warning will be issued; If the number of days is not less than the second preset number of days and less than the first preset number of days, a first-level warning for lithium plating is issued; If the number of days is not less than the third preset number of days and less than the second preset number of days, a level 2 lithium plating warning will be issued; If the number of days is not less than zero and is less than the third preset number of days, a level three warning for lithium plating is issued. The preset correspondence is determined based on the correspondence between different charging times and different natural days in the historical charging data.

5. The method as described in claim 4, characterized in that, The method further includes: If, during the last target charging process within the target group, there is a second target ranking ratio that is not less than the preset safety threshold, a level three lithium plating warning is issued.

6. The method as described in claim 3, characterized in that, The expression for the target fitted curve is: η i =Ay B + C Where, η i Let j be the ranking ratio, j be the number of charging times, and A, B, and C be constants.

7. The method as described in claim 2, characterized in that, Before sorting the internal resistance values ​​from largest to smallest during each of the target charging processes to determine the internal resistance ranking of each cell during each of the target charging processes, the method further includes: The internal resistance value is determined according to Formula 1, which is: Ri=(Vsi–Vei) / Is Wherein, Ri is the internal resistance value, Vsi is the cell voltage in the last data before the end of the target charging process, Vei is the cell voltage in the first data after the end of the target charging process, and Is is the cell current in the last data.

8. The method as described in claim 2, characterized in that, Before sorting the charging capacity values ​​in ascending order during each of the target charging processes to determine the capacity ranking of each cell during each of the target charging processes, the method further includes: The target voltage range is determined based on the difference between the highest voltage at the start of charging and the lowest voltage at the end of charging during the target charging process; The charging capacity of the battery cell in the target voltage range is determined based on the ampere-hour integration method.

9. A detection device for lithium plating in lithium-ion batteries, characterized in that, The device includes: The filtering module is used to acquire historical charging data of lithium-ion batteries within a preset historical time interval, and to filter out multiple target charging processes from the historical charging data. The target charging process is a charging process in which the SOC value is not less than a preset threshold at the end of charging. The lithium-ion battery includes multiple cells. The first determining module is used to determine the charging number of each target charging process under its own charging mode according to the time sequence of each target charging process; The judgment module is used to determine whether a target group exists, wherein the target group consists of a target charging process of not less than a preset number and using one of the charging methods; The second determining module is used to determine the lithium plating detection result based on the internal resistance value and charging capacity value of each cell in the target group during each target charging process, and the charging number of each cell, if present. Specifically, it involves: sorting the internal resistance values ​​in each target charging process from largest to smallest to determine the internal resistance ranking of each cell in each target charging process; sorting the charging capacity values ​​in each target charging process from smallest to largest to determine the capacity ranking of each cell in each target charging process; determining the ranking ratio of each cell in the target group during each target charging process based on the ratio of the internal resistance ranking to the capacity ranking; and determining the result based on the ranking ratio and the charging number of each cell.

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