Local serious lithium precipitation battery cell identification method, apparatus and device, and storage medium

Abstract

The invention discloses a local serious lithium precipitation battery cell identification method, device and equipment and a storage medium, and relates to the field of battery detection. The method comprises the following steps: performing charging and discharging event judgment on a battery pack, and obtaining a plurality of target events meeting preset charging and discharging characteristics from the charging and discharging events; obtaining an event result of each target event, wherein the event result of each target event equally comprises relaxation voltage rebound amounts corresponding to all battery cells in the battery pack; according to the relaxation voltage rebound amount of each battery cell, determining a local serious lithium precipitation battery cell from all battery cells; wherein the step of obtaining the event result of each target event comprises the substeps of obtaining the charging and discharging end voltage and the standing end voltage of each battery cell corresponding to the target event; and according to each charging and discharging ending voltage and each standing ending voltage, respectively calculating relaxation voltage rebound amount of all the battery cells. Therefore, the identification of the local serious lithium precipitation battery core is carried out by acquiring the charging and discharging ending voltage and the standing ending voltage which meet the preset charging and discharging 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 the operation of electric vehicles, the voltage and temperature data of the battery cells in the battery pack are detected to determine whether there are abnormalities, so as to identify whether there are batteries with abnormal risks inside the battery pack. In real operating scenarios, the voltage and temperature of some potential thermal runaway cells during the charging and discharging process are not significantly different from those of normal cells, but there may be serious lithium deposition on the local interface inside. During continuous use, the thermal safety risk of the battery pack continues to increase, thus causing 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 lithium-deposited 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 local severe lithium deposition, comprising:

[0006] Performing charge and discharge event judgment on the battery pack, and obtaining a plurality of target events satisfying preset charge and discharge characteristics from the charge and discharge events; the target events include charge events and / or discharge events;

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

[0008] Determining a battery cell with severe partial 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 static 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 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.

[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 static duration 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 executes a round of discharge events according to the discharge cut-off feature and the second rest time feature at multiple second event times;

[0023] Among them, 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 the 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 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 of the preset static time periods at the nth second event time;

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

[0027] According to each of the second event times, the rebound amounts to be analyzed are sorted from large to small to obtain a second sorting result of each of the rebound amounts to be analyzed at each of the second event times;

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

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

[0030] Determine a 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;

[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, configured to perform charge and discharge event judgment on the battery pack, and acquire multiple target events satisfying preset charge and discharge characteristics from the charge and discharge events;

[0035] A second acquisition module is used to acquire an event result of each of the target events, wherein the event result of each of the target events evenly includes a relaxation voltage rebound amount corresponding to all the cells in the battery pack;

[0036] An identification module, used for determining a battery cell with severe local lithium deposition from all the battery cells according to the 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 proposes 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 local severe 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 local severe lithium deposition as described in the first aspect.

[0040] The method, device, equipment and storage medium for identifying a battery cell with local severe lithium deposition disclosed in the present invention perform charge and discharge event judgment on a battery pack, obtain multiple target events that meet preset charge and discharge characteristics from the charge and discharge events; obtain event results of each target event, and the event results of each target event evenly include relaxation voltage rebound amounts corresponding to all batteries in the battery pack; determine the battery cell with local severe lithium deposition from all the batteries according to the relaxation voltage rebound amounts of each battery cell; wherein obtaining the event result of each target event includes: obtaining the charge and discharge end voltage and the static end voltage of each battery cell corresponding to the target event; and respectively calculating the relaxation voltage rebound amounts of all the batteries according to each charge and discharge end voltage and each static 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 a method for 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 the method for identifying a battery cell with severe local lithium deposition proposed in this embodiment is shown;

[0044] Figure 3 A schematic diagram of an interface of a battery cell with local abnormal lithium deposition proposed in this embodiment is shown;

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

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

[0047] Figure 6 A curve 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 the method for identifying a battery cell with severe local lithium deposition proposed in this embodiment is shown;

[0050] Fig. 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 in the embodiments of the present invention. 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 shown in the drawings 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 drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are 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 adding the possibility of 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 (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present invention belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present invention.

[0058] Example 1

[0059] The disclosed embodiment provides a method for identifying a battery cell with severe local lithium deposition, which is used to determine whether there is a lithium deposition battery cell with abnormal lithium deposition by analyzing the relaxation voltage change during the static process of charging or discharging a battery pack.

[0060] See also 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 satisfying 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, "charging-resting" and "discharging-resting" events, and the corresponding charge and discharge end voltage and resting 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 charging end voltage and / or the discharging end voltage.

[0063] In this embodiment, the charge and discharge events are judged according to the preset charge and discharge characteristics, so as to obtain multiple target events satisfying the preset charge and discharge characteristics from the charge and discharge events. The static data of the battery pack after charge / discharge is easy to obtain, and the method for processing the data is relatively simple, without the need for complex algorithm investment, and has feasibility, applicability and economy.

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

[0065] In this embodiment, the event results of each target event are obtained, and each event result includes the relaxation voltage rebound amount of all cells in the battery pack. The relaxation voltage rebound amount refers to the amplitude of the voltage rise after a period of time during the charging or discharging process of the battery. This parameter is of great significance for evaluating the performance, health status and whether there are potential problems of the battery.

[0066] See also 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 of the battery cells 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 end of charging, i.e., the charging end voltage, will be recorded. When the battery pack is left to rest after charging, the voltage at the end of rest, i.e., the rest end voltage, will be recorded. Correspondingly, when the discharge process in a discharge event is completed, the voltage at the end of discharge, i.e., the discharge end voltage, will be recorded. When the battery pack is left to rest after discharge, the voltage at the end of rest, i.e., the rest end voltage, will be recorded.

[0069] In this embodiment, the charge and 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 and discharge characteristics, the charge and discharge end voltage and the static end voltage corresponding to each target event are directly called.

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

[0071] In this embodiment, for the charge and discharge end voltage and the rest end voltage of all cells under each target event, the corresponding relaxation voltage rebound amount is calculated. The relaxation voltage rebound amount calculation formula is: ΔV(i) = |V(i, t) - V(i)|, where ΔV(i) represents the absolute value of the difference between the rest end voltage V(i, t) and the charge and discharge end 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 , step S103, determining the lithium deposition cell from each of the cell according to the relaxation voltage rebound amount of each of the cell.

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

[0074] The impact of local abnormal lithium deposition on 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 charging and discharging characteristics include a first event time characteristic, a charging cutoff characteristic and a first standing time characteristic, and 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 at multiple first event times according to the charging cutoff characteristic and the first standing time characteristic.

[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 at multiple first event times according to the charging cutoff characteristics and the first static time characteristics 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 generally two first event times are preset, and each first event time has a set of charging end voltages and static end voltages corresponding to all cells, and a relaxation voltage rebound amount obtained according to the relaxation voltage rebound amount calculation formula. The corresponding relationship between the charging end voltage, the static end voltage and the relaxation voltage rebound amount at each first event time is shown in Table 1 below:

[0078]

[0079] It should be noted that the charge cut-off characteristic requires a SOC of 90% or more, and the first static characteristic requires that the static time used to observe the relaxation voltage change of each battery cell during the static process is not less than 30 minutes. Among them, if the charge cut-off SOC of the battery pack is greater than or equal to 95% SOC, the data obtained will be more accurate.

[0080] See also 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 amount at each first event time is sorted from large to small according to the data. For example, when m=5, the first sorting results of the relaxation voltage rebound amount 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 sorting result of the relaxation voltage rebound amount corresponding to a certain battery cell meets the preset sorting condition, the battery cell is determined as a battery cell with local severe lithium deposition.

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

[0086] See also 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 of the battery cells according to a relaxation voltage rebound amount at each of the first event times.

[0088] In this embodiment, for the charging event, in another case, taking two first event times as an example, the rebound increment of each battery cell is determined according to 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 amount increment from each of the rebound amount increments, and determining the battery cell corresponding to the maximum rebound amount increment as the battery cell with local severe 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 battery cell means that in the battery pack, as the usage time goes by, after several charge and discharge cycles, the increase in ΔV(i) at the same standing time is greater than that of other battery cells.

[0091] See also 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 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 local lithium deposition deterioration of 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, and 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 respectively; 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 static 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 static end voltage.

[0095] It should be noted that generally two second event times are preset, and each second event time has a set of discharge end voltage and static end voltage corresponding to each battery cell, and a relaxation voltage rebound amount obtained according to the relaxation voltage rebound amount calculation formula. The corresponding relationship between the discharge end voltage, the static end voltage and the 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 in which the second static time characteristic includes multiple static time periods, that is, in the target event under the second event time, there must be multiple static end voltages corresponding to the static time periods, and thus it is also necessary to calculate the relaxation voltage rebound amount corresponding to each static time period according to the static end voltage corresponding to each static time period.

[0098] Exemplarily, for the target event corresponding to the nth second event time, it includes: after executing the discharge event on the battery pack, the battery is left at rest, and the current rest end voltage is recorded at rest time t1, and then the rest is continued, 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 cut-off 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 not be less than 30 minutes.

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

[0101] See also 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 of the preset static time periods at the nth second event time.

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

[0104] Step S802: taking all relaxation voltage rebound amounts corresponding to the same resting time in the relaxation voltage rebound amounts of all the battery cells as rebound amounts to be analyzed.

[0105] In this embodiment, multiple relaxation rebound amounts corresponding to the same resting time in the relaxation voltage rebound amounts of all cells in the battery pack are used as rebound amounts to be analyzed. For example, if the battery pack is left at rest for resting time t1 at the remaining second event times, each relaxation voltage rebound amount corresponding to resting time t1 at the nth second event time and each relaxation voltage rebound amount at the remaining second event times are used as rebound amounts 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 the 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 severe lithium deposition 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 of the discharge event are the same as the preset sorting conditions of the charging event. If, as time goes by, the battery pack undergoes several charge and discharge cycles, and after being left standing 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 jth battery cell is a battery cell with local severe lithium deposition.

[0113] Please see again Figure 7 In this figure, the first histogram LI represents the voltage rebound after 30 minutes of static discharge, and the second histogram L2 represents the voltage rebound after >9 hours of static discharge. It is known that cell 7# is a cell with severe lithium deposition in some parts, and the rebound difference of this cell, that is, the rebound amount of relaxation voltage rebound, has the largest change.

[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 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 a battery cell with local severe 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 the event results of each target event, and the event results of each target event evenly include the relaxation voltage rebound amount corresponding to all the batteries in the battery pack; determines the battery cell with local severe lithium deposition from all the batteries according to the relaxation voltage rebound amount of each battery cell; wherein, obtaining the event result of each target event includes: obtaining the charge and discharge end voltage and the static end voltage of each battery cell corresponding to the target event; and calculating the relaxation voltage rebound amount of all the batteries according to each charge and discharge end voltage and each static end voltage. In this way, after obtaining the charge and discharge events and static events that meet the preset charge and discharge characteristics of the battery pack, the charge and discharge end voltage and the static end voltage of all batteries are obtained, and 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 deposition battery cell with local abnormal lithium deposition is analyzed according to the relaxation voltage rebound amount, thereby improving the efficiency of lithium deposition battery cell identification.

[0116] Example 2

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

[0118] A first acquisition module 901 is used 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;

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

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

[0121] The second acquisition module 902 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.

[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 event results after the battery pack executes 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 of the first event times, to obtain a first sorting result of each of the relaxation voltage rebound amounts at each of the first event times; 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 as the locally 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.

[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 severe lithium deposition battery cell.

[0125] Optionally, the preset charge and discharge characteristics include a second event time characteristic, a discharge cut-off characteristic and a second standstill time characteristic. The second acquisition module 902 is used to obtain 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 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 according to the relaxation voltage rebound amount corresponding to each of the preset standing time at the nth second event time; take all the relaxation voltage rebound amounts corresponding to the same standing time in the relaxation voltage rebound amounts of all the battery cells as the rebound amounts to be analyzed; sort the rebound amounts to be analyzed from large to small according to each of the second event times, and obtain a second sorting result of each of the rebound amounts to be analyzed at each of 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.

[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, 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, the first candidate battery cell is used as the local severe lithium deposition 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 local severe lithium deposition cell identification device proposed in this embodiment performs charge and discharge event judgment on the battery pack, obtains multiple target events that meet the preset charge and discharge characteristics from the charge and discharge events; obtains the event results of each target event, and the event results of each target event evenly include the relaxation voltage rebound amount corresponding to all the cells in the battery pack; determines the local severe lithium deposition cells from all the cells according to the relaxation voltage rebound amount of each cell; wherein, obtaining the event result of 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 the cells according to each charge and discharge end voltage and each static end voltage. In this way, after obtaining the charge and discharge events and static events that meet the preset charge and discharge characteristics of the battery pack, the charge and discharge end voltage and the static end voltage of all cells are obtained, and the relaxation voltage rebound amount of each cell is obtained according to the charge and discharge end voltage and the static end voltage, and then the lithium deposition cells with local abnormal lithium deposition are analyzed according to the relaxation voltage rebound amount, thereby improving the efficiency of lithium deposition cell identification.

[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 local severe lithium deposition 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 disclosed embodiment 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 described in Embodiment 1 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, and will not be described again here to avoid repetition.

[0137] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as 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, further definition and explanation thereof is not required in subsequent drawings.

[0139] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for identifying a battery cell with severe local lithium deposition, characterized in that: include: Performing charge and discharge event judgment on the battery pack, and obtaining a plurality of target events satisfying preset charge and discharge characteristics from the charge and discharge events; The target event includes a charging event and / or a discharging event; Acquire an event result of each of the target events, wherein the event result of each of the target events evenly includes relaxation voltage rebound amounts corresponding to all cells in the battery pack; Determining a battery cell with severe partial 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 charge and discharge end voltage and the static end voltage of each of the battery cells corresponding to the target event; The relaxation voltage rebound amounts of all the battery cells are calculated respectively according to the charge and discharge end voltages and the static end voltages.

2. The method for identifying a battery cell with severe local lithium deposition according to claim 1, characterized in that: The preset charge and discharge characteristics include a first event time characteristic, a charge cut-off characteristic, and a first rest time characteristic. The event results of each target event are obtained, including 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.

3. The method for identifying a battery cell with severe local lithium deposition according to claim 2, characterized in that: 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 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.

4. The method for identifying a battery cell with severe local lithium deposition according to claim 2, characterized in that: 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, 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.

5. The method for identifying a battery cell with severe local lithium deposition according to claim 1, characterized in that: The preset charge and discharge characteristics include a second event time characteristic, a discharge cut-off characteristic, and a second static duration characteristic. The event results of each target event are obtained, including 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; Among them, 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 the static end voltage corresponding to each of the preset static time lengths.

6. The method for identifying a battery cell with severe local lithium deposition according to claim 5, characterized in that: 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 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 of the preset static time periods at the nth second event time; All relaxation voltage rebound amounts corresponding to the same resting time in the relaxation voltage rebound amounts of all the battery cells are used as rebound amounts to be analyzed; According to each of the second event times, the rebound amounts to be analyzed are sorted from large to small to obtain a second sorting result of each of the rebound amounts to be analyzed at each of the second event times; The locally severe lithium deposition battery cell is determined according to each of the rebound amount changes and each of the second sorting results.

7. The method for identifying a battery cell with severe local lithium deposition according to claim 6, characterized in that: The determining the local serious lithium deposition battery cell 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 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, 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.

8. A device for identifying batteries with severe local lithium deposition, characterized in that: include: A first acquisition module, configured to perform charge and discharge event judgment on the battery pack, and acquire multiple target events satisfying preset charge and discharge characteristics from the charge and discharge events; The target event includes a charging event and / or a discharging event; A second acquisition module is used to acquire an event result of each of the target events, wherein the event result of each of the target events evenly includes a relaxation voltage rebound amount corresponding to all the cells in the battery pack; An identification module, used for determining a battery cell with severe local lithium deposition from all the battery cells according to the relaxation voltage rebound amount of each of the battery cells; 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.

9. A computer device, characterized in that: It comprises a memory and a processor, 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 7 is implemented.

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

Images