Method, device, equipment and storage medium for identifying locally severe lithium deposition battery cells

Abstract

The present invention discloses a method, device, equipment, and storage medium for identifying cells with localized severe lithium deposition, relating to the field of battery detection. The method comprises: performing charge and discharge event judgment on a battery pack, obtaining multiple target events that meet preset charge and discharge characteristics from the charge and discharge events; obtaining event results for each target event, wherein the event results for each target event are evenly divided into the relaxation voltage rebound amount corresponding to all cells in the battery pack; determining cells with localized severe lithium deposition from all cells based on the relaxation voltage rebound amount of each cell; wherein obtaining the event results for each target event comprises: obtaining the charge and discharge end voltage and the rest end voltage of each cell corresponding to the target event; and calculating the relaxation voltage rebound amount of all cells based on each charge and discharge end voltage and each rest end voltage. In this way, cells with localized severe lithium deposition are identified by obtaining the charge and discharge end voltage and the rest end voltage that meet the preset charge and discharge characteristics.

Description

Technical Field

[0001] The present invention relates to the field of battery detection, and in particular to a method, device, equipment and storage medium for identifying a battery cell with severe local lithium deposition. Background Art

[0002] During operation, electric vehicles detect abnormal voltage and temperature data within the battery pack to identify batteries with abnormal risks. In real-world scenarios, some cells with potential thermal runaway may not exhibit significant voltage and temperature differences from normal cells during charge and discharge, but may exhibit severe lithium deposition at local interfaces. With continued use, the thermal safety risk of the battery pack continues to increase, creating hidden dangers. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a method, device, equipment and storage medium for identifying batteries with local severe lithium deposition, which is used to determine whether there are batteries with abnormal lithium deposition by analyzing the relaxation voltage changes during the static process after charging or discharging the battery pack.

[0004] The present invention provides the following technical solutions:

[0005] In a first aspect, the present invention provides a method for identifying a battery cell with severe local lithium deposition, comprising:

[0006] Performing charge and discharge event judgment on the battery pack, and obtaining multiple target events that meet preset charge and discharge characteristics from the charge and discharge events; the target events include charging events and / or discharging events;

[0007] Obtaining an event result of each target event, where the event result of each target event evenly includes relaxation voltage rebound amounts corresponding to all cells in the battery pack;

[0008] Determining a battery cell with severe local lithium deposition from all the battery cells according to a relaxation voltage rebound amount of each of the battery cells;

[0009] The event result of each target event is obtained, including:

[0010] Obtaining the charge and discharge end voltage and the static end voltage of each of the battery cells corresponding to the target event;

[0011] The relaxation voltage rebound amounts of all the battery cells are calculated respectively according to the charge and discharge end voltages and the rest end voltages.

[0012] In one embodiment, the preset charge and discharge characteristics include a first event time characteristic, a charge cutoff characteristic, and a first rest time characteristic, and the event results of each target event are obtained, including

[0013] When the target event is a charging event, event results are obtained after the battery pack executes a round of charging events according to the charging cutoff feature and the first rest time feature at multiple first event times.

[0014] In one embodiment, determining a battery cell with severe local lithium deposition from each of the battery cells according to the relaxation voltage rebound amount of each of the battery cells includes:

[0015] When the target event is a charging event, all the relaxation voltage rebound amounts are sorted from large to small according to the first event times to obtain a first sorting result of the relaxation voltage rebound amounts at the first event times;

[0016] If the first sorting result of the relaxation voltage rebound amount corresponding to the i-th battery cell meets the preset sorting condition, the i-th battery cell is determined as the locally severely lithium-deposited battery cell, and the preset sorting condition is that the first sorting result gradually decreases to the first place, or the first sorting result remains in the first place.

[0017] In one embodiment, determining a battery cell with severe local lithium deposition from each of the battery cells according to the relaxation voltage rebound amount of each of the battery cells includes:

[0018] When the target event is a charging event, determining a rebound amount increment of each of the battery cells according to a relaxation voltage rebound amount at each of the first event times;

[0019] determining a maximum rebound amount increment from each of the rebound amount increments;

[0020] The battery cell corresponding to the maximum rebound amount increment is determined as the battery cell with local severe lithium deposition.

[0021] In one embodiment, the preset charge and discharge characteristics include a second event time characteristic, a discharge cut-off characteristic, and a second rest time characteristic, and the event results of each target event are obtained, including

[0022] When the target event is a discharge event, obtaining event results after the battery pack performs a round of discharge events according to the discharge cut-off feature and the second rest time feature at multiple second event times;

[0023] Wherein, at the nth second event time, the second static time length feature includes a plurality of preset static time lengths, and the static end voltage includes a static end voltage corresponding to each of the preset static time lengths.

[0024] In one embodiment, determining a battery cell with severe local lithium deposition from each of the battery cells according to the relaxation voltage rebound amount of each of the battery cells includes:

[0025] When the target event is a discharge event, determining a rebound amount change of all the battery cells at the nth second event time according to the relaxation voltage rebound amount corresponding to each of the preset rest periods at the nth second event time;

[0026] All relaxation voltage rebound amounts corresponding to the same rest time in the relaxation voltage rebound amounts of all the battery cells are used as rebound amounts to be analyzed;

[0027] sorting the rebound amounts to be analyzed from largest to smallest according to the second event times, to obtain second sorting results of the rebound amounts to be analyzed at the second event times;

[0028] The battery cell with severe local lithium deposition is determined according to each of the rebound amount changes and each of the second sorting results.

[0029] In one embodiment, determining the battery cell with severe local lithium deposition according to each rebound amount change and each second sorting result includes:

[0030] Determine a maximum rebound amount change from each of the rebound amount changes, and select a battery cell corresponding to the maximum rebound amount change as a first candidate battery cell;

[0031] If the second sorting result of the rebound amount to be analyzed corresponding to the j-th battery cell meets the preset sorting condition, the j-th battery cell is determined as the second candidate battery cell;

[0032] If the cell number corresponding to the first candidate cell is the same as the cell number corresponding to the second candidate cell, the first candidate cell is used as the cell with local severe lithium deposition.

[0033] In a second aspect, the present invention provides a device for identifying a battery cell with severe local lithium deposition, comprising:

[0034] A first acquisition module is configured to perform charge and discharge event judgment on the battery pack, and acquire multiple target events that meet preset charge and discharge characteristics from the charge and discharge events;

[0035] A second acquisition module is configured to acquire an event result of each target event, where the event result of each target event evenly includes relaxation voltage rebound amounts corresponding to all cells in the battery pack;

[0036] an identification module, configured to determine a battery cell with severe local lithium deposition from all the battery cells according to a relaxation voltage rebound amount of each of the battery cells;

[0037] The second acquisition module is further used to obtain the charge and discharge end voltage and the static end voltage of each battery cell corresponding to the target event; and calculate the relaxation voltage rebound amount of all the battery cells according to each charge and discharge end voltage and each static end voltage.

[0038] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for identifying a battery cell with severe local lithium deposition as described in the first aspect is implemented.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for identifying a battery cell with severe local lithium deposition as described in the first aspect.

[0040] The present invention discloses a method, apparatus, device and storage medium for identifying cells with local severe lithium deposition, which performs charge and discharge event judgment on a battery pack, obtains multiple target events that meet preset charge and discharge characteristics from the charge and discharge events; obtains event results for each target event, and the event results for each target event evenly include the relaxation voltage rebound amount corresponding to all cells in the battery pack; determines cells with local severe lithium deposition from all cells based on the relaxation voltage rebound amount of each cell; wherein obtaining the event result for each target event includes: obtaining the charge and discharge end voltage and the rest end voltage of each cell corresponding to the target event; and calculating the relaxation voltage rebound amount of all cells based on each charge and discharge end voltage and each rest end voltage. In this way, by obtaining the charge and discharge events and static events of the battery pack that meet the preset charge and discharge characteristics, the charge and discharge end voltage and the static end voltage of all battery cells are obtained, and then the relaxation voltage rebound amount of each battery cell is obtained according to the charge and discharge end voltage and the static end voltage, and then the lithium-depositing battery cells with local abnormal lithium deposition are analyzed according to the relaxation voltage rebound amount, thereby improving the efficiency of lithium-depositing battery cell identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope of protection of the present invention. In each of the drawings, similar components are numbered similarly.

[0042] Figure 1 A schematic diagram of a process of identifying a battery cell with severe local lithium deposition proposed in this embodiment is shown;

[0043] Figure 2 Another schematic diagram of the process of identifying a battery cell with severe local lithium deposition proposed in this embodiment is shown;

[0044] Figure 3 It shows an interface diagram of a battery cell with local abnormal lithium deposition proposed in this embodiment;

[0045] Figure 4Another flow chart of the method for identifying a battery cell with severe local lithium deposition proposed in this embodiment is shown;

[0046] Figure 5 Another flow chart of the method for identifying a battery cell with severe local lithium deposition proposed in this embodiment is shown;

[0047] Figure 6 A schematic diagram showing the rebound amount increment during the charging process proposed in this embodiment is shown;

[0048] Figure 7 A schematic diagram showing the change in rebound amount during the discharge process proposed in this embodiment is shown;

[0049] Figure 8 Another schematic diagram of the process of identifying a battery cell with severe local lithium deposition proposed in this embodiment is shown;

[0050] Figure 9 A structural schematic diagram of a device for identifying a battery cell with severe local lithium deposition proposed in this embodiment is shown.

[0051] Description of the accompanying drawings:

[0052] 900 - local severe lithium deposition battery cell identification device; 901 - first acquisition module; 902 - second acquisition module; 903 - identification module. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0054] The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the figures is not intended to limit the scope of the claimed invention, but rather merely represents selected embodiments of the present invention. All other embodiments derived by those skilled in the art based on the embodiments of the present invention without inventive effort are intended to be within the scope of protection of the present invention.

[0055] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present invention, are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0056] Furthermore, the terms “first,” “second,” “third,” etc., are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0057] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which the various embodiments of the present invention pertain. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meaning as in the context of the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning unless clearly defined in the various embodiments of the present invention.

[0058] Example 1

[0059] The embodiments of the present disclosure provide a method for identifying batteries with severe local lithium deposition, which is used to determine whether there are batteries with abnormal lithium deposition by analyzing the relaxation voltage changes during the static process of charging or discharging a battery pack.

[0060] See Figure 1 A method for identifying a battery cell with severe local lithium deposition includes steps S101 to S103, and each step is described in detail below.

[0061] Step S101 : performing charge and discharge event judgment on a battery pack, and obtaining a plurality of target events that meet preset charge and discharge characteristics from the charge and discharge events.

[0062] It should be noted that the battery pack will undergo multiple charge and discharge events during use, including charging events and / or discharging events, namely "charge-rest" and "discharge-rest" events, and the corresponding charge and discharge end voltage and rest end voltage are recorded to provide data basis for analyzing the health status of the battery pack, wherein the charge and discharge end voltage includes the charge end voltage and / or the discharge end voltage.

[0063] In this embodiment, charge and discharge events are judged based on preset charge and discharge characteristics to obtain multiple target events that meet the preset charge and discharge characteristics. The static data of the battery pack after charge and discharge is easy to obtain, and the data processing method is relatively simple, without the need for complex algorithms, making it feasible, applicable, and economical.

[0064] Step S102 : Acquire an event result of each target event, wherein the event result of each target event evenly includes relaxation voltage rebound amounts corresponding to all cells in the battery pack.

[0065] In this example, event results are obtained for each target event. Each event result includes the relaxation voltage rebound of all cells in the battery pack. Relaxation voltage rebound refers to the magnitude of the voltage rebound after a period of time during the battery's charge or discharge process. This parameter is important for assessing the performance and health of the battery cells, as well as potential problems.

[0066] See Figure 2 In a specific embodiment, obtaining the event result of each target event includes steps S201 to S202, and each step is described in detail below.

[0067] Step S201 : obtaining the charge / discharge end voltage and the rest end voltage of each battery cell corresponding to the target event.

[0068] It should be noted that when the charging process in a charging event is completed, the voltage at the time of charging completion, i.e., the charging completion voltage, will be recorded. When the battery pack is left to rest after charging is completed, the voltage at the time of rest completion, i.e., the rest completion voltage, will be recorded. Correspondingly, when the discharge process in a discharge event is completed, the voltage at the time of discharge completion, i.e., the discharge completion voltage, will be recorded. When the battery pack is left to rest after discharge is completed, the voltage at the time of rest completion, i.e., the rest completion voltage, will be recorded.

[0069] In this embodiment, the charge-discharge end voltage includes a charge end voltage and / or a discharge end voltage. After step S101 acquires multiple target events that meet the preset charge-discharge characteristics, the charge-discharge end voltage and the rest end voltage corresponding to each target event are directly called.

[0070] Step S202 , calculating the relaxation voltage rebound amount of each battery cell according to each charge and discharge end voltage and each rest end voltage.

[0071] In this embodiment, the corresponding relaxation voltage rebound is calculated for the end-of-charge / discharge voltage and the end-of-rest voltage of all cells under each target event. The relaxation voltage rebound calculation formula is: ΔV(i) = |V(i,t) - V(i)|, where ΔV(i) represents the absolute value of the difference between the end-of-rest voltage V(i,t) and the end-of-charge / discharge voltage V(i) of the i-th cell in the battery pack after resting for a period of time t.

[0072] Please see again Figure 1 In step S103 , a lithium deposition cell is determined from the cell groups according to the relaxation voltage rebound amount of each cell group.

[0073] In this embodiment, the identification of cells with local abnormal lithium deposition is performed based on the relaxation voltage rebound of all cells. Figure 3 shown.

[0074] The impact of the deterioration of local abnormal lithium deposition on the local interface concentration polarization will show certain differences in the relaxation voltage rebound amount of the battery cell during the static process. Therefore, it is more efficient and convenient to use the static data after charging and discharging to judge the local abnormal lithium deposition inside the battery cell.

[0075] In a specific embodiment, the preset charge and discharge characteristics include a first event time characteristic, a charge cutoff characteristic, and a first standstill time characteristic. Step S102 includes: when the target event is a charging event, obtaining the event results after the battery pack executes a round of charging events according to the charge cutoff characteristic and the first standstill time characteristic at multiple first event times.

[0076] In this embodiment, if it is necessary to obtain relevant quantities of charging events, the event results after the battery pack executes a round of charging events according to the charging cutoff characteristics and the first static time characteristics at multiple first event times are obtained. At this time, the event results of each round of target events respectively include the relaxation voltage rebound amount calculated based on the charging end voltage and its corresponding static end voltage.

[0077] It should be noted that two first event times are generally preset. At each first event time, there is a set of charge end voltages and rest end voltages corresponding to all battery cells, as well as a relaxation voltage rebound amount obtained according to the relaxation voltage rebound amount calculation formula. The corresponding relationship between the charge end voltage, rest end voltage, and relaxation voltage rebound amount at each first event time is shown in Table 1 below:

[0078]

[0079] It should be noted that the charge cutoff characteristic requires a SOC of 90% or higher, and the first static characteristic requires a static period of at least 30 minutes to observe the relaxation voltage changes of each cell during the static period. The data obtained is more accurate if the charge cutoff SOC of the battery pack is greater than or equal to 95% SOC.

[0080] See Figure 4 In a specific embodiment, step S103 includes steps S401 to S402, and each step is described in detail below.

[0081] Step S401 : When the target event is a charging event, all the relaxation voltage rebound amounts are sorted from large to small according to the first event times to obtain a first sorting result of the relaxation voltage rebound amounts at the first event times.

[0082] In this embodiment, for charging events, in one case, the relaxation voltage rebound amounts at each first event time are sorted from large to small according to the data. For example, if m=5, the first sorting results of the relaxation voltage rebound amounts corresponding to each battery cell may be: ΔV1(3)>ΔV1(5)>ΔV1(1)>ΔV1(4)>ΔV1(2), and ΔV2(3)>ΔV2(1)>ΔV2(5)>ΔV2(4)>ΔV2(2).

[0083] Step S402: If the first sorting result of the relaxation voltage rebound amount corresponding to the i-th battery cell meets the preset sorting condition, the i-th battery cell is determined as the local severe lithium deposition battery cell, and the preset sorting condition is that the first sorting result gradually decreases to the first place, or the first sorting result remains in the first place.

[0084] In this embodiment, if the first ranking result of the relaxation voltage rebound amount corresponding to a certain battery cell meets the preset ranking condition, the battery cell is determined to be a battery cell with local severe lithium deposition.

[0085] Exemplarily, the preset ranking condition may be that the first ranking result of ΔV(i) gradually decreases to the first place, or continuously remains at the first place. That is, as time passes, after the battery pack undergoes multiple charge and discharge cycles and is left at rest for the same time t at multiple first event times, if the ranking of ΔV(i) gradually decreases to the first place, or continuously remains at the first place, in descending order, then the i-th battery cell is a battery cell with severe local lithium plating deterioration.

[0086] See Figure 5 In a specific embodiment, step S103 includes steps S501 to S502, and each step is described in detail below.

[0087] Step S501 : When the target event is a charging event, determining a rebound amount increment of each battery cell according to a relaxation voltage rebound amount at each first event time.

[0088] In this embodiment, for a charging event, in another case, taking two first event times as an example, the rebound increment of each battery cell is determined based on each relaxation voltage rebound amount. The rebound increment Δn(i) of the i-th battery cell is calculated as follows: Δn(i) = ΔV2(i) - ΔV1(i), that is, the rebound increment Δn(i) of the i-th battery cell is the difference between the relaxation voltage rebound amount ΔV2(i) of the i-th battery cell at the second first event time T2 and the relaxation voltage rebound amount ΔV1(i) of the i-th battery cell at the first first event time T1.

[0089] Step S502 , determining a maximum rebound increment from each of the rebound increments, and determining the battery cell corresponding to the maximum rebound increment as the battery cell with severe local lithium deposition.

[0090] In this embodiment, the maximum rebound increment is determined from the rebound increments corresponding to each battery cell, and the battery cell corresponding to the maximum rebound increment is determined as a battery cell with local severe lithium deposition. This means that in the battery pack, as the usage time goes by, after several charge and discharge cycles, the increase in ΔV(i) at rest for the same time is greater than that of other battery cells.

[0091] See Figure 6 It is known that the cell numbered 7 is an abnormal lithium deposition cell. When the battery pack is left at 95% SOC for 2 hours before and after the two first event time cycles, the rebound amount increment of this cell is the largest among all the cells.

[0092] It should be noted that since the chemical reactions during the charging process are more complex and involve the deintercalation and embedding processes of lithium ions, the deterioration of local lithium deposition in the battery pack has a greater impact on the relaxation voltage rebound amount during the charging process. When the first sorting result of the relaxation voltage rebound amount corresponding to a certain battery cell meets the preset sorting conditions or the corresponding rebound amount increment is the largest, it can be determined as a lithium deposition battery cell with local lithium deposition.

[0093] In a specific embodiment, the preset charge and discharge characteristics include a second event time characteristic, a discharge cut-off characteristic and a second standstill time characteristic. Step S102 includes: when the target event is a discharge event, obtaining the event results after the battery pack executes a round of discharge events according to the discharge cut-off characteristic and the second standstill time characteristic at multiple second event times; wherein, at the nth second event time, the second standstill time characteristic includes multiple preset standstill time periods, and the standstill end voltage includes the standstill end voltage corresponding to each of the preset standstill time periods.

[0094] In this embodiment, for the discharge event, the event results are obtained after the battery pack executes a round of target events according to the discharge cut-off characteristics and the second rest time characteristics at multiple second event times. At this time, the event results under each round of target events include the relaxation voltage rebound amount calculated based on the discharge end voltage and its corresponding rest end voltage.

[0095] It should be noted that two second event times are generally preset. At each second event time, there is a set of discharge end voltage and static end voltage corresponding to each battery cell, as well as a relaxation voltage rebound amount obtained according to the relaxation voltage rebound amount calculation formula. The corresponding relationship between the discharge end voltage, static end voltage and relaxation voltage rebound amount at each second event time is shown in Table 2 below:

[0096]

[0097] It should be noted that, for a discharge event, there is a second event time at which the second rest period feature includes multiple rest periods, that is, in the target event at the second event time, there must be multiple rest end voltages corresponding to the rest periods, and thus the relaxation voltage rebound amount corresponding to each rest period needs to be calculated based on the rest end voltage corresponding to each rest period.

[0098] Exemplarily, the target event corresponding to the nth second event time includes: after the battery pack performs a discharge event, the battery is placed in a rest state, and the current rest end voltage is recorded at rest time t1. Then, the battery is placed in a rest state again, and the current rest end voltage is recorded at rest time t2. Accordingly, at the first second event time, for rest time t1, the jth battery cell has a corresponding relaxation voltage rebound amount ΔV1'(j, t1); for rest time t2, the jth battery cell has a corresponding relaxation voltage rebound amount ΔV1'(j, t2).

[0099] It should be noted that the discharge cutoff feature in the discharge event is recommended to be less than or equal to 15% SOC. When it is less than or equal to 10% SOC, data acquisition is more accurate. At the same time, the static time in the discharge time must also not be less than 30 minutes.

[0100] See Figure 7 , the standing time t1 can be 30 minutes, and the standing time t2 can be 9 hours.

[0101] See Figure 8 In a specific embodiment, step S103 includes steps S801 to S804, and each step is described in detail below.

[0102] Step S801, when the target event is a discharge event, determining the rebound amount change of all the battery cells at the nth second event time according to the relaxation voltage rebound amount corresponding to each preset static time at the nth second event time.

[0103] In this embodiment, the rebound change of each cell at the first second event time is determined based on the relaxation voltage rebound at the nth second event time. Taking two rest periods as an example, the rebound change of the jth cell Δz(j) = ΔV1'(j, t2) - ΔV1'(j, t1).

[0104] In step S802 , all relaxation voltage rebound amounts corresponding to the same rest time in the relaxation voltage rebound amounts of all the battery cells are used as rebound amounts to be analyzed.

[0105] In this embodiment, multiple relaxation voltage rebound values ​​corresponding to the same resting time for all cells in the battery pack are used as the rebound values ​​to be analyzed. For example, if the battery pack is rested for resting time t1 at all other second event times, the relaxation voltage rebound values ​​corresponding to resting time t1 at the nth second event time and the relaxation voltage rebound values ​​at all other second event times are used as the rebound values ​​to be analyzed, thereby controlling the variables.

[0106] Step S803 , sorting the rebound amounts to be analyzed from large to small according to the second event times, to obtain a second sorting result of the rebound amounts to be analyzed at the second event times.

[0107] In this embodiment, the rebound amounts to be analyzed at each second event time are sorted according to data size, and second sorting results of the rebound amounts to be analyzed of all battery cells at different second event times are obtained.

[0108] Step S804 , determining the battery cell with severe local lithium deposition according to each of the rebound amount changes and each of the second sorting results.

[0109] In this embodiment, since the chemical reaction during discharge is relatively stable, the local lithium deposition deterioration of the battery pack has a small impact on the relaxation voltage rebound during discharge. It is necessary to comprehensively consider the changes in various rebound amounts and the second sorting results to determine the lithium-deposited battery cell.

[0110] In a specific embodiment, step S804 includes: determining the maximum rebound amount change from each of the rebound amount changes, and using the battery cell corresponding to the maximum rebound amount change as the first candidate battery cell; if the second sorting result of the rebound amount to be analyzed corresponding to the j-th battery cell meets the preset sorting condition, then determining the j-th battery cell as the second candidate battery cell; if the battery cell number corresponding to the first candidate battery cell is the same as the battery cell number corresponding to the second candidate battery cell, then using the first candidate battery cell as the locally severely lithium-deposited battery cell.

[0111] In this embodiment, the maximum rebound amount change is determined from each rebound amount change, and the battery cell corresponding to the maximum rebound amount change is used as the first candidate battery cell; if the second sorting result of the rebound amount to be analyzed corresponding to the j-th battery cell meets the preset sorting condition, the j-th battery cell is determined as the second candidate battery cell; if the battery cell numbers of the first candidate battery cell and the second candidate battery cell are the same, the battery cell with that number is determined as a battery cell with local severe lithium deposition.

[0112] The preset sorting conditions for the discharge event are the same as those for the charge event. If, as time goes by, the battery pack undergoes several charge and discharge cycles, and after being left at rest for the same time t at multiple second event times, the second sorting result of ΔV(j) gradually decreases to the first place in descending order, or continues to remain at the first place, then the j-th battery cell is a battery cell with severe local lithium deposition.

[0113] Please see again Figure 7 In this figure, the first histogram, L1, shows the voltage rebound after 30 minutes of discharge, while the second histogram, L2, shows the voltage rebound after >9 hours of discharge. Cell 7# is known to have severe local lithium deposition, and this cell has the largest rebound difference, or relaxation voltage rebound.

[0114] It should be noted that by comparing the relaxation voltage data of the battery cells in the battery pack across time dimensions, the ranking change trend of the battery cells in the battery pack can be dynamically observed. The battery cells with abnormal local severe lithium deposition trends can be identified and dealt with in advance, thereby avoiding the continuous use of battery cells with abnormal local lithium deposition in the battery pack, which may lead to thermal runaway.

[0115] The method for identifying cells with severe local lithium deposition proposed in this embodiment performs charge and discharge event judgment on a battery pack, obtains multiple target events that meet preset charge and discharge characteristics from the charge and discharge events, obtains event results for each target event, and evenly distributes the event results of each target event including the relaxation voltage rebound amount corresponding to all cells in the battery pack; and determines cells with severe local lithium deposition from all cells based on the relaxation voltage rebound amount of each cell; wherein obtaining the event result for each target event includes: obtaining the charge and discharge end voltage and the static end voltage of each cell corresponding to the target event; and calculating the relaxation voltage rebound amount of all cells based on each charge and discharge end voltage and each static end voltage. In this way, by obtaining the charge and discharge end voltage and the static end voltage of all cells after the charge and discharge events and static events of the battery pack meet the preset charge and discharge characteristics, the relaxation voltage rebound amount of each cell is obtained based on the charge and discharge end voltage and the static end voltage, and then the lithium deposition cells with abnormal local lithium deposition are analyzed based on the relaxation voltage rebound amount, thereby improving the efficiency of lithium deposition cell identification.

[0116] Example 2

[0117] In addition, the present disclosure provides a device 900 for identifying a battery cell with severe local lithium deposition. Figure 9 ,include:

[0118] A first acquisition module 901 is configured to determine charge and discharge events of a battery pack and acquire multiple target events that meet preset charge and discharge characteristics from the charge and discharge events.

[0119] A second acquisition module 902 is configured to acquire an event result of each target event, where the event result of each target event evenly includes relaxation voltage rebound amounts corresponding to all cells in the battery pack;

[0120] An identification module 903 is configured to determine a battery cell with severe local lithium deposition from all the battery cells according to a relaxation voltage rebound amount of each of the battery cells;

[0121] The second acquisition module 902 is further configured to acquire the charge / discharge end voltage and the rest end voltage of each battery cell corresponding to the target event; and calculate the relaxation voltage rebound amount of all the battery cells based on each charge / discharge end voltage and each rest end voltage.

[0122] Optionally, the preset charging and discharging characteristics include a first event time characteristic, a charging cutoff characteristic and a first standing time characteristic. The second acquisition module 902 is used to obtain the event results after the battery pack performs a round of charging events according to the charging cutoff characteristic and the first standing time characteristic at multiple first event times when the target event is a charging event.

[0123] Optionally, the identification module 903 is used to, when the target event is a charging event, sort all the relaxation voltage rebound amounts from large to small according to each first event time, and obtain a first sorting result of each relaxation voltage rebound amount at each first event time; if the first sorting result of the relaxation voltage rebound amount corresponding to the i-th battery cell meets a preset sorting condition, then the i-th battery cell is determined to be the locally severely lithium-deposited battery cell, and the preset sorting condition is that the first sorting result gradually decreases to the first place, or the first sorting result remains in the first place.

[0124] Optionally, the identification module 903 is used to determine the rebound amount increment of each battery cell according to the relaxation voltage rebound amount at each first event time when the target event is a charging event; determine the maximum rebound amount increment from each rebound amount increment; and determine the battery cell corresponding to the maximum rebound amount increment as the locally severely lithium-deposited battery cell.

[0125] Optionally, the preset charge and discharge characteristics include a second event time characteristic, a discharge cutoff characteristic and a second standstill time characteristic. The second acquisition module 902 is used to obtain the event results after the battery pack executes a round of discharge events according to the discharge cutoff characteristic and the second standstill time characteristic at multiple second event times when the target event is a discharge event; wherein, at the nth second event time, the second standstill time characteristic includes multiple preset standstill time periods, and the standstill end voltage includes the standstill end voltage corresponding to each of the preset standstill time periods.

[0126] Optionally, the identification module 903 is used to determine, when the target event is a discharge event, the rebound amount change of all the battery cells at the nth second event time based on the relaxation voltage rebound amount corresponding to each preset rest time at the nth second event time; take all relaxation voltage rebound amounts corresponding to the same rest time in the relaxation voltage rebound amounts of all the battery cells as rebound amounts to be analyzed; sort each rebound amount to be analyzed from large to small according to each second event time, and obtain a second sorting result of each rebound amount to be analyzed at each second event time; determine the locally severely lithium-deposited battery cell based on each rebound amount change and each second sorting result.

[0127] Optionally, the identification module 903 is used to determine the maximum rebound amount change from each of the rebound amount changes, and use the battery cell corresponding to the maximum rebound amount change as the first candidate battery cell; if the second sorting result of the rebound amount to be analyzed corresponding to the j-th battery cell meets the preset sorting condition, then the j-th battery cell is determined as the second candidate battery cell; if the battery cell number corresponding to the first candidate battery cell is the same as the battery cell number corresponding to the second candidate battery cell, then the first candidate battery cell is used as the locally severely lithium-deposited battery cell.

[0128] The device provided in the embodiment of the present disclosure can execute the steps of the method for identifying a battery cell with severe local lithium deposition provided in Example 1, which will not be described again to avoid repetition.

[0129] The present embodiment proposes a device for identifying cells with severe local lithium deposition, which performs charge and discharge event judgment on a battery pack, obtains multiple target events that meet preset charge and discharge characteristics from the charge and discharge events, obtains event results for each target event, and evenly distributes the event results of each target event including the relaxation voltage rebound amount corresponding to all cells in the battery pack, and determines cells with severe local lithium deposition from all cells based on the relaxation voltage rebound amount of each cell. Acquiring the event results for each target event includes: obtaining the charge and discharge end voltage and the rest end voltage of each cell corresponding to the target event; and calculating the relaxation voltage rebound amount of all cells based on each charge and discharge end voltage and each rest end voltage. In this way, by obtaining the charge and discharge end voltage and rest end voltage of all cells after the charge and discharge events and rest events of the battery pack meet the preset charge and discharge characteristics, the relaxation voltage rebound amount of each cell is obtained based on the charge and discharge end voltage and rest end voltage, and then the lithium deposition cells with abnormal local lithium deposition are analyzed based on the relaxation voltage rebound amount, thereby improving the efficiency of identifying cells with abnormal lithium deposition.

[0130] Example 3

[0131] In addition, an embodiment of the present disclosure provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for identifying a battery cell with severe local lithium deposition as described in Example 1 is implemented.

[0132] The device provided in the embodiment of the present disclosure can execute the steps of the method for identifying a battery cell with severe local lithium deposition provided in Example 1, which will not be described again to avoid repetition.

[0133] Example 4

[0134] The embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for identifying a battery cell with severe local lithium deposition as described in the first embodiment is implemented.

[0135] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0136] The computer-readable storage medium provided in this embodiment can implement the method for identifying a battery cell with severe local lithium deposition provided in Example 1. To avoid repetition, it will not be described here.

[0137] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not limiting, and thus other examples of the exemplary embodiments may have different values.

[0138] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0139] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that variations and modifications are possible without departing from the scope of the present invention, and such variations and modifications are fully within the scope of protection of the present invention.

Claims

1. A method for identifying batteries with severe local lithium deposition, characterized in that: include: Performing charging event judgment on the battery pack, and obtaining multiple target events that meet preset charging characteristics from the charging events; The target event includes a charging event; Obtaining an event result of each target event, wherein the event result of each target event includes a relaxation voltage rebound amount corresponding to all cells in the battery pack; Determining a battery cell with severe local lithium deposition from all the battery cells according to a relaxation voltage rebound amount of each of the battery cells; The event result of each target event is obtained, including: Obtaining the charging end voltage and the rest end voltage of each of the battery cells corresponding to the target event; Calculating the relaxation voltage rebound amount of all the battery cells according to each of the charge end voltages and each of the rest end voltages; The preset charging characteristics include a first event time characteristic, a charging cutoff characteristic, and a first rest duration characteristic, and obtaining the event result of each target event includes: When the target event is a charging event, obtaining event results after the battery pack performs a round of charging events according to the charging cutoff feature and the first rest time feature at multiple first event times; Wherein, determining the battery cell with severe local lithium deposition from each of the battery cells according to the relaxation voltage rebound amount of each of the battery cells includes: When the target event is a charging event, determining a rebound amount increment of each of the battery cells according to a relaxation voltage rebound amount at each of the first event times; determining a maximum rebound amount increment from each of the rebound amount increments; The battery cell corresponding to the maximum rebound amount increment is determined as the battery cell with local severe lithium deposition.

2. The method for identifying a battery cell with severe local lithium deposition according to claim 1, wherein: The method of determining a battery cell with severe local lithium deposition from each of the battery cells according to the relaxation voltage rebound amount of each of the battery cells comprises: When the target event is a charging event, all the relaxation voltage rebound amounts are sorted from large to small according to the first event times to obtain a first sorting result of the relaxation voltage rebound amounts at the first event times; If the first sorting result of the relaxation voltage rebound amount corresponding to the i-th battery cell meets the preset sorting condition, the i-th battery cell is determined as the locally severely lithium-deposited battery cell, and the preset sorting condition is that the first sorting result gradually decreases to the first place, or the first sorting result remains in the first place.

3. A method for identifying batteries with severe local lithium deposition, characterized in that: include: Performing discharge event judgment on the battery pack, and obtaining multiple target events that meet preset discharge characteristics from the discharge events; The target event includes a discharge event; Obtaining an event result of each target event, wherein the event result of each target event includes a relaxation voltage rebound amount corresponding to all cells in the battery pack; Determining a battery cell with severe local lithium deposition from all the battery cells according to a relaxation voltage rebound amount of each of the battery cells; The event result of each target event is obtained, including: Obtaining the discharge end voltage and the static end voltage of each of the battery cells corresponding to the target event; Calculating the relaxation voltage rebound amount of all the battery cells according to each of the discharge end voltages and each of the static end voltages; The preset discharge characteristics include a second event time characteristic, a discharge cutoff characteristic, and a second static duration characteristic, and obtaining the event result of each target event includes: When the target event is a discharge event, obtaining event results after the battery pack executes a round of discharge events according to the discharge cut-off feature and the second rest time feature at multiple second event times; Wherein, at the nth second event time, the second static time characteristic includes a plurality of preset static time lengths, and the static end voltage includes a static end voltage corresponding to each of the preset static time lengths; Wherein, determining the battery cell with severe local lithium deposition from each of the battery cells according to the relaxation voltage rebound amount of each of the battery cells includes: When the target event is a discharge event, determining a rebound amount change of all the battery cells at the nth second event time according to the relaxation voltage rebound amount corresponding to each of the preset rest periods at the nth second event time; All relaxation voltage rebound amounts corresponding to the same rest time in the relaxation voltage rebound amounts of all the battery cells are used as rebound amounts to be analyzed; sorting the rebound amounts to be analyzed from largest to smallest according to the second event times, to obtain second sorting results of the rebound amounts to be analyzed at the second event times; Determine the locally severely lithium-deposited battery cell according to each of the rebound amount changes and each of the second sorting results; The step of determining the battery cell with severe local lithium deposition according to each of the rebound amount changes and each of the second sorting results includes: Determine a maximum rebound amount change from each of the rebound amount changes, and select a battery cell corresponding to the maximum rebound amount change as a first candidate battery cell; If the second sorting result of the rebound amount to be analyzed corresponding to the j-th battery cell meets the preset sorting condition, the j-th battery cell is determined as the second candidate battery cell; If the cell number corresponding to the first candidate cell is the same as the cell number corresponding to the second candidate cell, the first candidate cell is used as the cell with local severe lithium deposition.

4. A device for identifying batteries with severe local lithium deposition, characterized in that: The method for identifying a battery cell with severe local lithium deposition according to claim 1 or 2 comprises: A first acquisition module is configured to perform charging event judgment on the battery pack and acquire a plurality of target events that meet preset charging characteristics from the charging events; the target events include charging events; A second acquisition module is used to obtain an event result of each target event, where the event result of each target event includes a relaxation voltage rebound amount corresponding to all cells in the battery pack; an identification module, configured to determine a battery cell with severe local lithium deposition from all the battery cells according to a relaxation voltage rebound amount of each of the battery cells; The second acquisition module is further used to obtain the charging end voltage and the static end voltage of each of the battery cells corresponding to the target event; and calculate the relaxation voltage rebound amount of all the battery cells according to each of the charging end voltages and each of the static end voltages.

5. A device for identifying batteries with severe local lithium deposition, characterized in that: The method for identifying a battery cell with severe local lithium deposition according to claim 3 comprises: A first acquisition module is configured to perform discharge event determination on the battery pack and acquire a plurality of target events that meet preset discharge characteristics from the discharge events; the target events include discharge events; A second acquisition module is used to obtain an event result of each target event, where the event result of each target event includes a relaxation voltage rebound amount corresponding to all cells in the battery pack; an identification module, configured to determine a battery cell with severe local lithium deposition from all the battery cells according to a relaxation voltage rebound amount of each of the battery cells; The second acquisition module is further configured to acquire the discharge end voltage and the rest end voltage of each of the battery cells corresponding to the target event; and calculate the relaxation voltage rebound amounts of all of the battery cells based on each of the discharge end voltages and each of the rest end voltages.

6. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for identifying a battery cell with local severe lithium deposition as claimed in any one of claims 1 to 3 is implemented.

7. A computer-readable storage medium, characterized in that It stores a computer program, which, when executed by a processor, implements the method for identifying a battery cell with local severe lithium deposition as claimed in any one of claims 1 to 3.

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