A lithium battery failure prediction method, device and system

Abstract

The application provides a lithium battery failure prediction method, device and system, and belongs to the technical field of lithium battery failure. In the lithium battery charging and discharging process, the difference value of the residual charging capacity of two adjacent effective full charges is calculated based on the same battery cell, and the abnormal difference value is determined in the difference value of each battery cell. Since the residual charging capacity difference value of the micro short circuit battery cell is greater than that of the normal battery cell, the micro short circuit failure battery cell can be determined based on the abnormal difference value, the calculation amount is small, and the implementation difficulty is reduced. Before calculating the residual charging capacity each time, it is determined whether it constitutes an effective full charge. Only when the charging voltage curve formed by the full charge battery cell after the completion of the current charging is close to the overall standard charging voltage curve of the battery cell, it is considered to constitute an effective full charge. The residual charging capacity of each non-full charge battery cell can be calculated using the charging voltage curve of the full charge battery cell to obtain a more accurate value, thereby effectively avoiding false prediction results.

Description

Technical Field

[0001] This invention relates to the field of lithium battery failure technology, and in particular to a lithium battery failure prediction method, device and system. Background Technology

[0002] Lithium-ion batteries typically consist of a group of multiple cells. During the manufacturing process, contamination from impurities, or overcharging or over-discharging during use that triggers metal dendrites and causes separator rupture, can easily lead to micro-short circuits. These micro-short circuits often have a long evolutionary cycle. In the mid-to-late stages, the lithium-ion battery will generate a large amount of heat in a short period of time, and there are no effective countermeasures to prevent thermal runaway at this stage. Therefore, early diagnosis of micro-short circuit faults in lithium-ion batteries is crucial to preventing further damage caused by thermal runaway.

[0003] However, in the initial stage of a micro-short circuit, the failure phenomenon is not obvious. At this time, the short-circuit resistance is relatively large, so the short-circuit current is relatively small, and the resulting thermal effect is not significant, making it difficult to determine based on the external temperature of the lithium battery. In addition, the impact on the battery terminal voltage is also small in the initial stage of a micro-short circuit, making it difficult to determine based on the battery terminal voltage. Therefore, existing lithium battery failure prediction methods based on battery temperature or terminal voltage face significant challenges in predicting micro-short circuit failure. Summary of the Invention

[0004] This invention provides a method, apparatus, and system for predicting lithium battery failures, in order to solve the problem of the difficulty in predicting micro-short circuit failures in lithium batteries.

[0005] In a first aspect, the present invention provides a method for predicting lithium battery failure, wherein the lithium battery includes multiple cells connected in series. The method includes: determining whether a full charge constitutes a valid full charge each time the lithium battery is fully charged; wherein, each time the lithium battery is fully charged, the lithium battery includes at least one fully charged cell and multiple partially charged cells; the valid full charge is used to characterize the charging voltage curve of the fully charged cell as being close to the full charge voltage and / or the terminal trend of the standard charging voltage curve of the cell; calculating the remaining charging capacity of each cell during a valid full charge; if the number of valid full charges during the current valid full charge is greater than 1, obtaining the remaining charging capacity of each cell during the previous valid full charge; for each cell, calculating the difference between the remaining charging capacity of the current valid full charge and the previous valid full charge; identifying abnormal differences and corresponding cells among the differences in the remaining charging capacity of each cell; and determining the cells in the lithium battery that have experienced micro-short circuit failure based on the abnormal differences and the corresponding cells.

[0006] In one possible implementation, calculating the remaining charging capacity of each cell during a fully charged lithium battery includes: acquiring the charging voltage curve of each cell during a fully charged lithium battery; for any cell that is not fully charged, using the charging voltage curve of the fully charged cell as a reference curve, transforming the reference curve so that the transformed reference curve overlaps with the charging voltage curve of the not-fully-charged cell to obtain the full-charge curve of the not-fully-charged cell; determining the remaining charging time of the not-fully-charged cell based on its full-charge curve; and obtaining the remaining charging capacity of the cell based on the remaining charging time and charging current.

[0007] In one possible implementation, obtaining the remaining charging capacity of the battery cell based on the remaining charging time and charging current includes: obtaining the remaining charging capacity of the battery cell based on the following formula:

[0008]

[0009] Among them, C i Δt represents the remaining charging capacity of the battery cell, t1 represents the moment when the cell is fully charged, and Δt represents the charging time of the battery cell. i This indicates the remaining charging time of the battery cell, and I represents the charging current.

[0010] In one possible implementation, determining the abnormal difference and the corresponding cell among the differences in the remaining charging capacity of each cell includes: determining whether there is an abnormal difference among the differences in the remaining charging capacity of each cell based on box plot analysis. If an abnormal difference exists, then determining the cell corresponding to the abnormal difference based on the abnormal difference.

[0011] In one possible implementation, after determining the abnormal difference and the corresponding cell, the method further includes: calculating the remaining charging capacity of each cell and the difference during the next effective full charge of the lithium battery, and determining the abnormal difference and the corresponding cell. Multiple full charges are performed to determine the abnormal difference and the corresponding cell for each effective full charge. Accordingly, determining the cell with micro-short circuit failure in the lithium battery based on the abnormal difference and the corresponding cell includes: determining the cell with an abnormal number of times greater than or equal to a preset number of times based on the abnormal difference and the corresponding cell for each effective full charge. The cell with an abnormal number of times greater than or equal to the preset number of times is determined as the cell with micro-short circuit failure in the lithium battery.

[0012] In one possible implementation, the standard charging voltage curve of the battery cell is the charging voltage curve of the battery cell after being fully discharged and then fully charged; the trend of the two charging voltage curves at the end includes: within a preset time before the full charge time, the difference in the overall voltage change rate of the two charging voltage curves is less than a preset change rate difference threshold.

[0013] In one possible implementation, determining whether the charging constitutes a valid full charge includes: determining whether the charging time is greater than a preset time during the charging process; if the charging time is greater than the preset time and the lithium battery voltage reaches the charging cutoff voltage, determining that the lithium battery is validly fully charged; and / or, determining whether the charging rate is less than a preset rate; if the charging rate is less than the preset rate and the lithium battery voltage reaches the charging cutoff voltage, determining that the lithium battery is validly fully charged.

[0014] In one possible implementation, determining whether there are abnormal differences in the remaining charging capacity of each cell using the box plot analysis method includes: determining the upper quartile, median, and lower quartile of each difference based on the remaining charging capacity of each cell; subtracting the lower quartile from the upper quartile to obtain the interquartile range; and obtaining the upper and lower limits based on the upper quartile, lower quartile, and interquartile range. If any difference is greater than the upper limit or less than the lower limit, then that difference is determined to be an abnormal difference.

[0015] Secondly, the present invention provides a lithium battery failure prediction device, wherein the lithium battery includes multiple cells connected in series. The device includes: a judgment module for judging whether the current charging constitutes a valid full charge; wherein, each time the lithium battery is fully charged, the lithium battery includes at least one fully charged cell and multiple partially charged cells; the valid full charge is used to characterize the charging voltage curve of the fully charged cell being close to the full charge voltage and / or the terminal trend of the standard charging voltage curve of the cell; a remaining charging capacity calculation module for calculating the remaining charging capacity of each cell during the current valid full charge; an acquisition module for acquiring the remaining charging capacity of each cell during the previous valid full charge if the number of valid full charges during the current valid full charge is greater than 1; a difference calculation module for calculating, for each cell, the difference between the remaining charging capacity of the current valid full charge and the previous valid full charge; and an abnormal difference determination module for determining abnormal differences and the corresponding cells among the differences in the remaining charging capacity of each cell. The failed cell determination module is used to determine the cells in the lithium battery that have micro-short circuit failure based on the abnormal difference and the corresponding cells.

[0016] Thirdly, the present invention provides a lithium battery system, comprising: a plurality of lithium batteries and a lithium battery monitoring device, wherein the lithium battery monitoring device is connected to each of the lithium batteries and predicts whether there is a micro-short circuit in the cells of each of the lithium batteries by means of the method described in the first aspect or any possible implementation thereof.

[0017] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0018] This invention provides a method, apparatus, and system for predicting lithium battery failure. During the charging and discharging process of a lithium battery, this invention calculates the difference in remaining charging capacity between two consecutive effective full charges for the same cell, and identifies abnormal differences among these differences. Since the difference in remaining charging capacity of a micro-short-circuited cell is greater than that of a normal cell, micro-short-circuited failure cells can be identified based on these abnormal differences. The remaining charging capacity of a micro-short-circuited cell continuously increases during the charging and discharging process. This invention, by identifying micro-short-circuited failure cells based on the difference in remaining charging capacity between two consecutive effective full charges, requires less computation and is easier to implement.

[0019] The method and apparatus provided by this invention not only propose the aforementioned scheme for determining whether a battery cell has experienced a micro-short circuit failure based on the difference in remaining charging capacity after adjacent full charges, but more importantly, it also proposes that when using the above scheme to predict micro-short circuit failures of battery cells, certain restrictions should be placed on the conditions for calculating the remaining charging capacity to avoid erroneous judgments as much as possible. Specifically, before each calculation of the remaining charging capacity, the method and apparatus of this invention first determine whether a valid full charge has been achieved. Only when the charging voltage curve formed by a fully charged cell after this charging is completed is it considered to have achieved a valid full charge, especially when the full charge voltages of the curves are close and / or the trends at the ends of the curves are close. This ensures that the calculation of the remaining charging capacity of each incompletely charged cell using the charging voltage curve of the fully charged cell can yield a more accurate value, thus obtaining a more accurate calculation result and effectively avoiding erroneous prediction results. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is an application scenario diagram of the lithium battery failure prediction method provided in the embodiments of the present invention;

[0022] Figure 2 This is a schematic diagram illustrating the capacity difference of series-connected battery cells provided in an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram illustrating the RCC change of a micro-short-circuit cell during charging and discharging, provided in an embodiment of the present invention.

[0024] Figure 4 This is a flowchart illustrating the implementation of the lithium battery failure prediction method provided in this embodiment of the invention.

[0025] Figure 5 This is a schematic diagram of the cell voltage curve transformation with different parameters provided in the embodiments of the present invention;

[0026] Figure 6 This is a schematic diagram of the voltage curves of each cell in the lithium battery provided in the embodiments of the present invention;

[0027] Figure 7 This is a schematic diagram of the box type provided in an embodiment of the present invention;

[0028] Figure 8 This is a schematic diagram of the structure of the lithium battery failure prediction device provided in an embodiment of the present invention;

[0029] Figure 9 This is a schematic diagram of a lithium battery monitoring device provided in an embodiment of the present invention. Detailed Implementation

[0030] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0031] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0032] Figure 1 This diagram illustrates an application scenario of the lithium battery failure prediction method provided in this invention. This invention can be applied to various application scenarios involving the prediction of lithium battery failure; specific application scenarios are not limited here. For example, in one application scenario, such as… Figure 1 As shown, a lithium battery consists of multiple cells connected in series.

[0033] Lithium-ion battery failures are mainly classified into two categories: performance failures and safety failures. Performance failures refer to the lithium-ion battery's performance failing to meet usage requirements and relevant indicators, primarily including capacity decay, short cycle life, and performance degradation at high and low temperatures. Safety failures refer to lithium-ion batteries exhibiting certain safety risks due to improper use or abuse, primarily including thermal runaway, short circuits, gas expansion, leakage, lithium plating, and expansion deformation.

[0034] Failure analysis of lithium batteries aims to predict and prevent their occurrence, enabling the BMS (Battery Management System) to take effective measures before serious consequences occur, reduce losses, and provide data support for subsequent accident handling. This invention only analyzes the prediction of lithium battery failure; failure prevention is not addressed.

[0035] The occurrence of lithium battery failure is accompanied by failure phenomena, and a particular cause of failure may manifest differently over time. Generally, lithium battery failure phenomena may escalate over time, and failure to address them promptly can lead to incalculable losses. Therefore, predicting lithium battery failure should begin with early failure phenomena, analyzing abnormal battery data, and ultimately aiming to predict failure as early as possible. The following section explains the micro-short-circuit failure phenomenon of lithium batteries and the principles of failure prediction.

[0036] Impurities during lithium battery production or overcharging / over-discharging during use can trigger metal dendrite formation, causing separator rupture and potentially triggering micro-short circuits. These micro-short circuits often have a long evolutionary cycle; in the mid-to-late stages, the lithium battery generates a large amount of heat in a short period, and there are no effective countermeasures to prevent thermal runaway at this stage. Therefore, early diagnosis of lithium battery micro-short circuit faults is crucial to preventing further damage caused by thermal runaway.

[0037] However, in the initial stage of a micro-short circuit, the failure phenomenon is not obvious. At this time, the short-circuit resistance is relatively large, therefore the short-circuit current is relatively small, and the resulting thermal effect is not significant, making it difficult to determine based on the external temperature of the lithium battery. Furthermore, the impact on the battery terminal voltage is also small in the initial stage of a micro-short circuit, making it difficult to determine based on the battery terminal voltage alone. Therefore, existing lithium battery failure prediction methods based on battery temperature or terminal voltage have significant difficulty predicting micro-short circuit failures. Therefore, it is necessary to use models and algorithms to reflect the abnormal electrothermal characteristics of the faulty battery, thereby diagnosing battery micro-short circuit faults. This invention mainly predicts micro-short circuit failures based on the remaining charge capacity of the lithium battery.

[0038] The following section first explains the remaining charging capacity of lithium batteries. Due to inconsistencies in manufacturing processes and uneven operating environments, inconsistencies in the cells within a lithium battery pack are unavoidable.

[0039] Figure 2 This is a schematic diagram illustrating the capacity difference of series-connected battery cells according to an embodiment of the present invention. (Refer to...) Figure 2A lithium battery pack consists of eight cells connected in series. The black squares represent the initial capacity, and the diagonal lines represent the charging capacity. Differences in the production and use of lithium battery packs result in the total capacity of each cell differing from the initial capacity. Because the cells are connected in series, the charge and discharge amounts of each cell are the same during charging and discharging (i.e., the diagonal lines on each cell are equal). When one cell is fully charged, for example... Figure 2 When cell #7 is fully charged, the BMS will stop charging to prevent overcharging. At this time, the difference between the total capacity of the other cells and the current capacity (initial capacity + charging capacity) is the battery's remaining charging capacity (RCC). Figure 2 The blank space between the diagonal line and the dashed line.

[0040] The following further explains the relationship between micro-short circuits and remaining charging capacity in lithium batteries. Ignoring battery aging, if no micro-short circuit occurs, the RCC value of the same cell should be the same each time the battery is fully charged. If a cell experiences a micro-short circuit, it will continuously consume the battery's stored capacity, causing the RCC value of that cell to increase continuously when fully charged. The following uses a small lithium battery pack model to illustrate this situation.

[0041] Figure 3 This is a schematic diagram illustrating the RCC change of a micro-short-circuited cell during charging and discharging, provided as an embodiment of the present invention. (Refer to...) Figure 3 The lithium battery pack consists of two cells, denoted as X# and Y#. X# and Y# have the same total capacity, but cell X# has experienced a micro-short circuit. Based on the charging and discharging principles of lithium battery packs, charging and discharging will stop as soon as one cell reaches its charging or discharging cutoff voltage. The following will use... Figure 3 Explain the various states ① to ⑤ during the charging and discharging process.

[0042] State ①: Initially, both X# and Y# are fully charged, as shown in State ①. At this time, the remaining charging capacity of cell X# is denoted as RCC0, and RCC0 = 0.

[0043] State ②: The lithium battery is being discharged. Due to the energy loss from the micro-short circuit, cell X# will reach full discharge sooner. At the end of the discharge, as in State ②, the remaining discharging capacity (RDC) of cell Y# is equal to the energy loss from the micro-short circuit during the discharge of cell X#. The remaining discharging capacity of cell Y# corresponds to... Figure 3 The black square portion in state ②.

[0044] State ③: Charge the lithium battery pack. The Y# cell will reach the fully charged state earlier. At the end of charging, as in State ③, the remaining charge capacity of the X# cell is counted as RCC1 at this time.

[0045] State ④: Discharge the lithium battery pack. At the end of discharging, as in State ④.

[0046] State ⑤: Charge the lithium battery pack. At the end of charging, as in State ⑤, the remaining charge capacity of the X# cell is counted as RCC2 at this time.

[0047] In the above charge and discharge processes, obviously RCC0 < RCC1 < RCC2. This is because the micro-short circuit will continuously consume the power of the X# cell. Therefore, each time the lithium battery pack is fully charged, the remaining charge capacity of the X# cell continuously increases. At the same time, each time the lithium battery pack is fully discharged, the remaining discharge capacity of the Y# cell continuously increases. This also shows that if the micro-short circuit cell is not processed in time, the capacity difference between the normal cells and the micro-short circuit cells of the lithium battery pack will become larger and larger, and finally it will not be able to charge and discharge normally. If there is no micro-short circuit in X#, the capacity change during the charge and discharge process is exactly the same as that of the Y# cell, and the RCC is always 0. The above process shows that each time the lithium battery pack is fully charged, the RCC value of the normal cell basically does not change, while the RCC value of the micro-short circuit cell will continue to increase. Therefore, it is possible to determine whether a cell has a micro-short circuit by the change of the RCC value of the cell.

[0048] The embodiment of the present invention calculates the difference in the remaining charge capacity between two adjacent full charges based on the same cell, and determines the micro-short circuit failure cell based on the abnormal difference, so as to solve the problem of the relatively large difficulty in predicting the micro-short circuit failure of the lithium battery.

[0049] Figure 4 It is a flowchart of the implementation of the lithium battery failure prediction method provided by the embodiment of the present invention. The lithium battery includes a plurality of serially connected cells. Refer to Figure 4 , the method includes:

[0050] In step 400, when each full charge of the lithium battery is completed, it is judged whether this charge constitutes an effective full charge.

[0051] Generally speaking, during the charging process of the lithium battery, when any cell reaches the charging cut-off voltage, it is determined that the lithium battery is fully charged. Correspondingly, the BMS controls the lithium battery to stop charging. Based on the above description of the remaining charge capacity, after a certain cell in the plurality of cells of the lithium battery is fully charged, the other cells are in an undercharged state. At this time, the lithium battery includes at least one fully charged cell and a plurality of undercharged cells.

[0052] Furthermore, the term "effective full charge" indicates that the charging voltage curve of the fully charged cell and the standard charging voltage curve of the cell are close to each other in terms of full charge voltage and / or terminal trend. The standard charging voltage curve of the cell is the charging voltage curve after the cell has been fully discharged and then fully charged. The closeness of the full charge voltages of the two charging voltage curves means that the difference in voltage values ​​at the moment of full charge is less than a preset voltage difference threshold. Correspondingly, the closeness of the terminal trends of the two charging voltage curves means that within a preset time period before the moment of full charge, the difference in the overall voltage change rate of the two charging voltage curves is less than a preset change rate difference threshold. It can be understood that, among the conditions constituting an effective full charge, either the full charge voltage or the terminal trend can be satisfied, or both must be satisfied simultaneously.

[0053] The reason for proposing the concept of effective full charge and defining it as above in this invention is mainly because the applicant of this invention has discovered that the concept and calculation of remaining charging capacity are related to the charging process of a fully charged cell. As will be described in step 401 and its sub-steps below, the calculation of remaining charging capacity can be based on a fully charged cell. For example, the following embodiments of this invention provide two methods for calculating remaining charging capacity, both of which are based on the high correlation between the charging processes of fully charged and partially charged cells. The remaining charging time of each cell is obtained based on a fully charged cell, and then the remaining charging capacity is obtained.

[0054] During the research and development process, the applicant discovered that the method of determining the remaining charging capacity based on a fully charged cell resulted in significant fluctuations in the calculation results each time. Specifically, the applicant found that the calculated remaining charging capacity was not accurate for every full charge. Furthermore, the applicant discovered that when a lithium battery is effectively fully charged, the charging curve of a fully charged cell differs little from that of a partially charged cell. Therefore, obtaining the remaining charging capacity result based on a fully charged cell is more accurate, leading to more accurate predictions of micro-short circuit failures.

[0055] In one possible implementation, step 400 may include: determining whether the charging time is greater than a preset time after charging is complete. If the charging time is greater than the preset time, the lithium battery is determined to be effectively fully charged.

[0056] It should be noted that, during the research and development process, the applicant of this invention discovered that when the charging time is too short, even if the battery is fully charged, the full-charge voltage at the end of the charging process may differ significantly from the full-charge voltage of the complete discharge-to-full-charge curve (i.e., the aforementioned standard charging voltage curve). This leads to a decrease in the accuracy of the remaining charging capacity and an excessively high prediction error rate. Therefore, the effective full-charge judgment condition of this invention includes a charging time longer than a preset time, in order to improve the accuracy of calculating the remaining charging capacity and to improve the accuracy of predicting micro-short circuit anomalies.

[0057] For example, the preset charging time is 30 minutes. That is, the continuous charging time must not be less than 30 minutes.

[0058] In one possible implementation, step 400 may also include: determining whether the charging rate is less than a preset rate after charging is complete. If the charging rate is less than the preset rate, the lithium battery is determined to be effectively fully charged.

[0059] For example, the charging rate is a measure of how fast a battery charges, referring to the current required to charge the battery to its rated capacity within a specified time.

[0060] It should be further noted that during the research and development process, the applicant of this invention also discovered that when the charging rate is too high, the voltage change rate at the end of the charging of the first fully charged cell is large. When charging stops, the voltage of other cells may still be in the plateau region, resulting in a large error in calculating the remaining charging time of other cells, which in turn leads to a decrease in the accuracy of the remaining charging capacity and a high prediction error rate. Therefore, the effective full-charge judgment condition of this invention includes a charging rate less than a preset rate, in order to improve the accuracy of calculating the remaining charging capacity and to improve the accuracy of micro-short circuit anomaly prediction.

[0061] For example, the preset value of the charging rate can be determined based on experimental data of this type of battery cell. The preset value of the charging rate may vary for different battery cells.

[0062] It is understood that in some other embodiments, both of the above conditions must be met simultaneously to be considered a valid full charge. Furthermore, charging time and charging rate are two charging process parameters that the applicant of this invention has currently discovered that can affect the charging voltage curve of a fully charged cell. This invention does not exclude the possibility that other charging process parameters may also affect the degree to which the charging voltage curve of a fully charged cell approaches the standard charging voltage curve at the end of charging.

[0063] In step 401, when the lithium battery is effectively fully charged, the remaining charging capacity of each cell is calculated.

[0064] Correspondingly, the remaining charging capacity corresponds to the remaining charging capacity of a fully charged cell and a partially charged cell when the lithium battery is fully charged. Therefore, the remaining charging capacity of each cell can only be calculated when the lithium battery is at least fully charged in a conventional manner. Furthermore, when the lithium battery is fully charged, the remaining charging capacity of a fully charged cell is 0, while the remaining charging capacity of a partially charged cell can be calculated based on the charging curve transformation of the fully charged cell. Specifically, as mentioned earlier, based on the high correlation between the charging processes of fully charged and partially charged cells, the remaining charging time of each cell can be obtained using the fully charged cell as a benchmark, and thus the remaining charging capacity can be obtained. This will be discussed in detail below.

[0065] In step 402, if the number of valid full charges in this valid full charge is greater than 1, the remaining charging capacity of each cell at the time of the last valid full charge is obtained.

[0066] In some embodiments, the above-mentioned effective full charge counts refer to the effective full charge counts used to record the remaining charging capacity of each cell.

[0067] It should be noted that the lithium battery can be a battery undergoing its first charge or a battery that has been used for a period of time and has undergone multiple charge-discharge cycles. In this embodiment of the invention, during the charging and discharging process of a lithium battery, if the remaining charging capacity of each cell is obtained for the first effective full charge in step 401 (i.e., the number of effective full charges in step 402 is 1), then step 401 continues to be executed. When the lithium battery is effectively fully charged again, the remaining charging capacity of each cell is calculated for that next effective full charge. Thus, the difference in remaining charging capacity between two adjacent effective full charges can be obtained in subsequent steps.

[0068] For example, the remaining charging capacity of each cell is expressed as C. n,i For example, C n,1 C n,2 C n,M , where n represents the nth charging that meets the conditions, i represents the i-th battery cell, and M is the total number of battery cells.

[0069] In step 403, for each cell, the difference between the remaining charging capacity of the cell during the current effective full charge and the previous effective full charge is calculated.

[0070] For example, the difference in remaining charging capacity can be expressed as ΔC. n,i For example, ΔC n,1 ΔC n,2 , …, ΔC n,M , where n represents the nth charging that meets the conditions, i represents the i-th battery cell, and M is the total number of battery cells.

[0071] It should be noted that in lithium batteries, the RCC value of a micro-short-circuited cell increases continuously during the charging and discharging process. Therefore, the difference ΔC between the RCC values ​​of two consecutive effective full charges of the same cell is calculated. n,i =C n,i -C n-1,i ΔC of micro-short-circuited cells n,I The difference will be greater than that of a normal cell. That is, among the RCC differences between two consecutive effective full charges of each cell, the difference of the micro-short-circuited cell will be greater than that of the normal cell, which can be used as a basis for determining micro-short circuits.

[0072] In step 404, abnormal differences and corresponding cells are identified from the differences in the remaining charging capacity of each cell.

[0073] In some embodiments, the difference in remaining charging capacity of a normal battery cell is smaller than the difference in remaining charging capacity of an abnormal battery cell. For example, the change in remaining charging capacity of a normal battery cell is small, while the abnormal battery cell has a larger difference in remaining charging capacity between two effective full charges due to the consumption of charging capacity by a micro-short circuit.

[0074] In some embodiments, a fixed difference threshold is preset. Among the differences in the remaining charging capacity of each cell, differences exceeding the aforementioned difference threshold are identified as abnormal differences.

[0075] In some embodiments, determining the corresponding cell includes obtaining the cell number corresponding to the abnormal difference.

[0076] In step 405, based on the abnormal difference and the corresponding cell, the cell with micro-short circuit failure in the lithium battery is identified.

[0077] In some embodiments, for a given battery cell, an abnormal difference between the remaining charge capacity after two consecutive full charges indicates that the cell has significant charging losses during charging. Since the difference is greater for a micro-short-circuited cell than for a normal cell, this suggests that the cell is more likely to be micro-short-circuited.

[0078] In some embodiments, if the difference in remaining charging capacity of any cell is determined to be abnormal, then the cell is determined to have a micro-short circuit failure.

[0079] This invention, in its embodiments, calculates the difference in remaining charging capacity between two consecutive effective full charges for the same cell during the charging and discharging process of a lithium battery, and identifies abnormal differences among these differences. Since the difference in remaining charging capacity of a micro-short-circuited cell is greater than that of a normal cell, a micro-short-circuited failure cell can be identified based on this abnormal difference. The remaining charging capacity of a micro-short-circuited cell increases continuously during the charging and discharging process. This invention, by identifying micro-short-circuited failure cells based on the difference in remaining charging capacity between two consecutive effective full charges, requires less computation and is easier to implement.

[0080] Furthermore, the method and apparatus provided by this invention not only propose the aforementioned scheme for determining whether a battery cell has experienced a micro-short circuit failure based on the difference in remaining charging capacity after adjacent full charges, but more importantly, it also proposes that when using the above scheme to predict micro-short circuit failures of battery cells, certain restrictions need to be placed on the conditions for calculating the remaining charging capacity to avoid erroneous judgments as much as possible. Specifically, before each calculation of the remaining charging capacity, the method and apparatus of this invention first determine whether a valid full charge has been achieved. Only when the charging voltage curve formed by a fully charged cell after this charging is completed is it considered to have achieved a valid full charge, especially when the full charge voltages of the curves are close and / or the trends at the ends of the curves are close. This ensures that the calculation of the remaining charging capacity of each incompletely charged cell using the charging voltage curve of the fully charged cell can yield a more accurate value, thus obtaining a more accurate calculation result and effectively avoiding erroneous prediction results.

[0081] This invention is applicable to lithium battery packs with varying health levels. Theoretically, if the health of a single cell in a lithium battery pack remains constant, its remaining charging capacity will always remain unchanged. However, in actual use, the cell's health gradually decreases, potentially affecting its remaining charging capacity. In this invention, the difference in remaining charging capacity between two adjacent charging cycles is used for prediction. Since the cell's health does not change significantly between adjacent charging cycles, the difference is essentially unaffected by the cell's health. Therefore, this solution is applicable to battery packs with varying health levels.

[0082] It should also be noted that the embodiments of the present invention require full charging. If the lithium battery pack is charged at a low frequency, the amount of data will be insufficient, making it impossible to predict micro-short circuits in a timely manner. This issue will not cause false alarms for micro-short circuits, but may only result in missed alarms. Accordingly, during actual battery use, prompts can be made to periodically charge the battery to perform battery maintenance, increasing the data available for micro-short circuit prediction.

[0083] The following explains the calculation of the remaining charging capacity in step 401. The remaining charging capacity is the difference between the total capacity of the cells when the lithium battery pack is fully charged and the current capacity, which is difficult to measure directly. This embodiment of the invention uses the cell charging voltage curve for indirect calculation.

[0084] The main factors affecting the charging voltage curve of a battery cell are total capacity, internal resistance, and initial capacity. When the total capacity, internal resistance, and initial capacity of different cells in a lithium battery pack are the same, their charging voltage curves completely overlap. When the total capacity, internal resistance, and initial capacity are different, their charging voltage curves can overlap after translation and scaling.

[0085] Figure 5 This is a schematic diagram illustrating the transformation of cell voltage curves with different parameters provided in an embodiment of the present invention. (Refer to...) Figure 5The figure shows the charging voltage curves of two battery cells (cell A and cell B) with different total capacity, internal resistance, and initial capacity. From ① to ②, the cell B curve eliminates the effect of inconsistent initial capacity by horizontally shifting ΔAh. From ② to ③, the cell B curve eliminates the effect of inconsistent internal resistance by vertically shifting ΔU. From ③ to ④, the cell B curve eliminates the effect of inconsistent total capacity by scaling. After transformation, the cell B curve overlaps with the cell A curve.

[0086] Therefore, this embodiment of the invention uses the voltage curve of the first fully charged cell during charging as a benchmark to analyze the charging time required for other cells to reach the charging cutoff voltage under conditions where they can continue charging, and calculates the remaining charging capacity. The following describes the method for calculating the remaining charging capacity of cells in the lithium battery pack that are not fully charged at the end of charging.

[0087] Referring to the above, when the total capacity, internal resistance, and initial capacity of the cells in a lithium battery pack are all consistent with the first fully charged cell, the voltage curve of that cell will be completely consistent with the first fully charged cell, i.e., RCC will be 0. Next, we will analyze the situation where the cell parameters are inconsistent with the first fully charged cell.

[0088] The following is a specific method for calculating the remaining charging capacity of each battery cell:

[0089] In one possible implementation, when the lithium battery is effectively fully charged, the remaining charging capacity of each cell at this effective full charge includes:

[0090] In step 4011, when the lithium battery is effectively fully charged, the charging voltage curve of each cell is obtained. The lithium battery when effectively fully charged includes fully charged cells and multiple cells that are not fully charged.

[0091] Figure 6 This is a schematic diagram of the voltage curves of each cell in a lithium battery provided in an embodiment of the present invention. (Refer to...) Figure 6 The lithium battery consists of four cells, Cell1 to Cell4. The solid lines represent the actual voltage curves of each cell. At time t1, cell Cell1 (corresponding to...) Figure 6 The leftmost curve in the diagram reaches the charging cutoff voltage first, at which point the BMS stops charging to prevent overcharging. Cell1 is a fully charged cell, while Cell2, Cell3, and Cell4 are not fully charged cells.

[0092] In step 4012, for any cell that is not fully charged, the charging voltage curve of a fully charged cell is used as a reference curve. The reference curve is transformed so that the transformed reference curve overlaps with the charging voltage curve of the cell that is not fully charged, thus obtaining the full charge curve of the cell that is not fully charged.

[0093] In some embodiments, the initial capacity of a partially charged cell is different from that of a fully charged cell, while the total capacity and internal resistance are the same. For example, the total capacity and internal resistance of cell 3 are the same as those of cell 1, but its initial capacity is lower than that of cell 1. Therefore, when charging ends, cell 3 has not reached the charging cutoff voltage, and the voltage curve corresponds to... Figure 6 The rightmost solid curve. According to the transformation method above, the voltage curve of cell 1 from time t1-Δt3 to time t1 can be shifted to the right to obtain the dashed part of cell 3, and then the complete voltage curve of cell 3 at full charge can be obtained.

[0094] In some embodiments, the total capacity and initial capacity of a partially charged cell are the same as those of a fully charged cell, but their internal resistance is different. For example, the total capacity and initial capacity of cell 2 are the same as those of cell 1, but its internal resistance is lower than that of cell 1. According to the transformation method described above, the cell 1 curve can be transformed downwards to obtain the complete voltage curve of cell 2 when fully charged.

[0095] In some embodiments, the internal resistance and initial capacity of a partially charged cell are the same as those of a fully charged cell, but their total capacity is different. For example, the internal resistance and initial capacity of cell 4 are the same as those of cell 1, but their total capacity is higher than that of cell 1. According to the transformation method described above, the cell 1 curve can be scaled to obtain the complete voltage curve of cell 4 when fully charged.

[0096] In some embodiments, considering that lithium battery packs are screened during practical applications, the difference in cell internal resistance and total capacity within the same battery pack is relatively small. For example, a 10% difference in RCC is within an acceptable range. For simplicity, the impact of the difference in internal resistance and total capacity on the RCC value can be ignored. That is, the total capacity and internal resistance of all cells in the battery pack are considered to be the same, and the voltage curve of the cell that is fully charged first is used as the reference curve to calculate the RCC value of the remaining cells.

[0097] In step 4013, the remaining charging time of the partially charged cell is determined based on the full charge curve of the partially charged cell.

[0098] For example, Δt3 represents the remaining charging time.

[0099] In some embodiments, the first full-charge time of a fully charged cell is obtained based on its full-charge curve. The second full-charge time of a partially charged cell is obtained based on its full-charge curve. The remaining charging time of the partially charged cell is obtained by subtracting the first full-charge time from the second full-charge time.

[0100] In step 4014, the remaining charging capacity of the battery cell is obtained based on the remaining charging time and charging current.

[0101] Considering that the current for each charge may not be the same in actual operation, it is not possible to directly use the change in the remaining charging time as the basis for judging whether a micro short circuit has occurred. Instead, the RCC value is calculated based on this time to make the judgment.

[0102] In one possible implementation, the remaining charging capacity of the battery cell is obtained based on the remaining charging time and charging current, including by using the following formula:

[0103]

[0104] Among them, C i Δt represents the remaining charging capacity of the battery cell, t1 represents the moment when the cell is fully charged, and Δt represents the charging time of the battery cell. i This indicates the remaining charging time of the battery cell, and I represents the charging current.

[0105] The following is another way to calculate the remaining charging capacity of each battery cell:

[0106] In one possible implementation, when the lithium battery is effectively fully charged, calculating the remaining charging capacity of each cell at this effective full charge includes: obtaining the full charge voltage of each cell at the effective full charge moment, wherein the fully charged lithium battery includes fully charged cells and multiple partially charged cells, and for each partially charged cell, the full charge voltage at the full charge moment means its voltage value at the full charge moment. Subsequently, for any partially charged cell, during the charging process of the fully charged cell, determining the charging moment of the fully charged cell when its full charge voltage is the same as that of the partially charged cell; determining the duration between the charging moment and the full charge moment of the lithium battery as the remaining charging time of the partially charged cell; and obtaining the remaining charging capacity of the cell based on the remaining charging time and charging current.

[0107] It should be noted that the embodiments of the present invention have high requirements for the timeliness of cell data. For example, when the full charge voltage of each cell is obtained at the moment the lithium battery is fully charged, the voltage of the cell that is not fully charged drops rapidly at the end of charging. If the voltage of the cell that is not fully charged at that moment cannot be accurately obtained, it will affect the calculation of the remaining charging capacity of the cell, and thus affect the accuracy of the prediction.

[0108] Accordingly, to improve the timeliness of cell data, the voltage at the end of charging can be directly recorded on the secondary BMS (SBMU), and then this data can be reported to the tertiary BMS or the cloud for subsequent calculations.

[0109] Regarding step 404, how to determine abnormal differences, the following is an example based on the box plot method. Figure 7 This is a schematic diagram of a box-type structure provided in an embodiment of the present invention. (Refer to...) Figure 7 :

[0110] In one possible implementation, identifying abnormal differences and corresponding cells among the differences in remaining charging capacity of each cell includes: determining whether abnormal differences exist among the differences in remaining charging capacity of each cell using a box plot analysis method. If abnormal differences exist, the cell corresponding to the abnormal difference is then identified.

[0111] A box plot is a statistical chart used to display the dispersion of a set of data. Its advantage is that it is not affected by outliers and can accurately and stably depict the discrete distribution of data.

[0112] This invention uses a box plot to perform a consistency analysis on the remaining charging capacity difference of all battery cells, determining if there is any abnormal data, and recording the corresponding cell number if so. The analysis only requires calculating the remaining charging capacity difference of the cells and performing box plot analysis after each effective full charge, resulting in minimal computational load and resource consumption.

[0113] It should be noted that during charge-discharge cycles, the remaining charge capacity of each cell continuously changes, and the difference in remaining charge capacity between cells also continuously changes. For lithium batteries of different models and used in different environments, it is difficult to determine a fixed threshold to judge whether the aforementioned differences in remaining charge capacity are abnormal. Therefore, this embodiment of the invention uses a box plot to determine abnormal values ​​based on the comparison of the RCC difference data of each cell. As the remaining charge capacity of each cell continuously changes during charge-discharge cycles, the difference between normal and abnormal cells gradually increases, and the corresponding upper and lower limits for abnormal judgment also continuously change, thus avoiding the need to set a fixed judgment threshold and preventing misjudgments.

[0114] The following examples illustrate the specific process of determining outlier values ​​using box plot analysis.

[0115] In one possible implementation, determining whether there are abnormal differences in the remaining charging capacity of each cell based on box plot analysis includes:

[0116] In step 501, based on the difference in the remaining charging capacity of each cell, the upper quartile, median, and lower quartile of each difference are determined.

[0117] In some embodiments, the difference in remaining charging capacity of each cell, i.e., ΔC of all cells, is used. n,i After arranging all values ​​from smallest to largest, the 75th percentile is the upper quartile Q3, the 50th percentile is the median Q2, and the 25th percentile is the lower quartile Q1.

[0118] In step 502, the lower quartile is subtracted from the upper quartile to obtain the interquartile range.

[0119] In some embodiments, the interquartile range (IQR) is calculated based on the following formula: IQR = Q3 - Q1.

[0120] In step 503, the upper limit and lower limit are obtained based on the upper quartile, lower quartile, and interquartile range.

[0121] In some embodiments, the upper limit is calculated based on the following formula: Q3 + 1.5IQR.

[0122] In some embodiments, the lower limit is calculated based on the following formula: Q1 - 1.5IQR.

[0123] In step 504, if any difference is greater than the upper limit or less than the lower limit, then the difference is determined to be an abnormal difference.

[0124] In some embodiments, the ΔC of all battery cells is... n,i Values ​​exceeding the upper limit or falling below the lower limit are identified as abnormal differences.

[0125] In one possible implementation, after determining the abnormal difference and the corresponding cell, the method further includes: calculating the remaining charging capacity and difference of each cell during the next effective full charge of the lithium battery, and determining the abnormal difference and the corresponding cell. This process is repeated multiple times to determine the abnormal difference and the corresponding cell for each effective full charge.

[0126] Accordingly, based on the abnormal differences and the corresponding cells, the cells in the lithium battery that have micro-short-circuit failures are identified as follows: based on the abnormal differences of each effective full charge and the corresponding cells, cells in the lithium battery with an abnormal number greater than or equal to a preset number are identified. These cells with an abnormal number greater than or equal to the preset number are then identified as cells in the lithium battery that have micro-short-circuit failures.

[0127] For example, the preset number of times is greater than or equal to 2. More exemplarily, the preset number of times is 20. That is, a cell is determined to have a micro-short circuit only when the number of times a certain cell is recorded as having an abnormal difference is greater than or equal to 20.

[0128] It should be noted that during the research and development process, the applicant of this invention discovered that when a lithium battery experiences a micro-short circuit, it typically does not show a significant difference from a normal battery within a single operating cycle. Therefore, in step 405, if a cell is judged as having a micro-short circuit abnormality simply because it experiences only one RCC difference anomaly, there is a possibility of misjudgment.

[0129] Therefore, in this embodiment of the invention, after multiple full-charge cycles, cells with abnormal RCC differences greater than or equal to a preset number of cycles are identified as micro-short-circuit failure cells, reducing the probability of misjudgment and improving the accuracy of micro-short-circuit failure.

[0130] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0131] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0132] Figure 8 This is a schematic diagram of the lithium battery failure prediction device provided in an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0133] Reference Figure 8 A lithium battery comprises multiple cells connected in series. The lithium battery failure prediction device 8 includes:

[0134] The judgment module 80 is used to determine whether the current charging constitutes a valid full charge; wherein, each time the lithium battery is fully charged, the lithium battery includes at least one fully charged cell and multiple partially charged cells; the valid full charge is used to characterize that the charging voltage curve of the fully charged cell is close to the full charge voltage and / or end trend of the standard charging voltage curve of the cell.

[0135] The remaining charging capacity calculation module 81 is used to calculate the remaining charging capacity of each cell when the lithium battery is effectively fully charged.

[0136] The acquisition module 82 is used to acquire the remaining charging capacity of each cell when the last valid full charge occurred, if the number of valid full charges is greater than 1.

[0137] The difference calculation module 83 is used to calculate the difference between the remaining charging capacity of the current effective full charge and the previous effective full charge for each cell.

[0138] The abnormal difference determination module 84 is used to determine the abnormal difference and the corresponding battery cell from the difference in the remaining charging capacity of each battery cell.

[0139] The failed cell identification module 85 is used to identify cells with micro-short circuit failures in lithium batteries based on abnormal differences and the corresponding cells.

[0140] This invention, in its embodiments, calculates the difference in remaining charging capacity between two consecutive full charges of the same cell during the charging and discharging process of a lithium battery, and identifies abnormal differences among these differences. Since the difference in remaining charging capacity of a micro-short-circuited cell is greater than that of a normal cell, a micro-short-circuited failure cell can be identified based on this abnormal difference. The remaining charging capacity of a micro-short-circuited cell increases continuously during the charging and discharging process. This invention, by identifying micro-short-circuited failure cells based on the difference in remaining charging capacity between two consecutive full charges, requires less computation and is easier to implement.

[0141] In one possible implementation, the remaining charging capacity calculation module 81 is specifically used to acquire the charging voltage curve of each cell when the lithium battery is effectively fully charged. The lithium battery at effective full charge includes fully charged cells and multiple partially charged cells. For each partially charged cell, the full charge voltage at the moment of full charge is its voltage value at the moment of full charge. Then, for any partially charged cell, the charging voltage curve of the fully charged cell is used as a reference curve. The reference curve is transformed so that the transformed reference curve overlaps with the charging voltage curve of the partially charged cell, thus obtaining the full charge curve of the partially charged cell. Based on the full charge curve of the partially charged cell, the remaining charging time of the partially charged cell is determined. Based on the remaining charging time and charging current, the remaining charging capacity of the cell is obtained.

[0142] In one possible implementation, the remaining charging capacity of the battery cell is obtained based on the remaining charging time and charging current, including by using the following formula:

[0143]

[0144] Among them, C i Δt represents the remaining charging capacity of the battery cell, t1 represents the moment when the cell is fully charged, and Δt represents the charging time of the battery cell. i This indicates the remaining charging time of the battery cell, and I represents the charging current.

[0145] In one possible implementation, the abnormal difference determination module 84 is specifically used to determine whether there are abnormal differences among the differences in the remaining charging capacity of each cell based on the box plot analysis method. If abnormal differences exist, the cell corresponding to the abnormal difference is determined based on the abnormal difference.

[0146] In one possible implementation, after the abnormal difference determination module 84, the method further includes: calculating the remaining charging capacity and difference of each cell during the next effective full charge of the lithium battery, and determining the abnormal difference and the corresponding cell. This is repeated multiple times to determine the abnormal difference and the corresponding cell for each effective full charge. Correspondingly, the failed cell determination module 85 is specifically used to determine, based on the abnormal difference and the corresponding cell for each effective full charge, cells in the lithium battery with an abnormal number greater than or equal to a preset number. Cells with an abnormal number greater than or equal to the preset number are then identified as cells with micro-short circuit failures in the lithium battery.

[0147] In one possible implementation, the remaining charging capacity calculation module 81 is specifically used to determine whether the charging time is greater than a preset value and whether the charging rate is less than a preset value during the charging process. If the charging time is greater than the preset value and the charging rate is less than the preset value, then when the lithium battery is fully charged, the remaining charging capacity of each cell at the time of full charge is calculated.

[0148] In one possible implementation, determining whether there are abnormal differences in the remaining charging capacity of each cell based on box plot analysis includes: determining the upper quartile, median, and lower quartile of each difference; subtracting the lower quartile from the upper quartile to obtain the interquartile range; and obtaining the upper and lower limits based on the upper quartile, lower quartile, and interquartile range. If any difference is greater than the upper limit or less than the lower limit, that difference is determined to be an abnormal difference.

[0149] Figure 9 This is a schematic diagram of a lithium battery monitoring device provided in an embodiment of the present invention. Figure 9 As shown, the lithium battery monitoring device 9 of this embodiment includes: a processor 90, a memory 91, and a computer program 92 stored in the memory 91 and executable on the processor 90. When the processor 90 executes the computer program 92, it implements the steps in the various lithium battery failure prediction method embodiments described above, for example... Figure 4 Steps 400 to 405 are shown. Alternatively, when the processor 90 executes the computer program 92, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 8 The functions of modules 80 to 85 are shown.

[0150] For example, the computer program 92 can be divided into one or more modules / units, which are stored in the memory 91 and executed by the processor 90 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 92 in the lithium battery monitoring device 9. For example, the computer program 92 can be divided into... Figure 8 Modules 80 to 85 are shown.

[0151] The lithium battery monitoring device 9 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The lithium battery monitoring device 9 may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art will understand that... Figure 9 This is merely an example of the lithium battery monitoring device 9 and does not constitute a limitation on the lithium battery monitoring device 9. It may include more or fewer components than shown, or combine certain components, or different components. For example, the lithium battery monitoring device may also include input / output devices, network access devices, buses, etc.

[0152] The processor 90 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0153] The memory 91 can be an internal storage unit of the lithium battery monitoring device 9, such as a hard disk or memory of the lithium battery monitoring device 9. The memory 91 can also be an external storage device of the lithium battery monitoring device 9, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the lithium battery monitoring device 9. Furthermore, the memory 91 can include both internal storage units and external storage devices of the lithium battery monitoring device 9. The memory 91 is used to store the computer program and other programs and data required by the lithium battery monitoring device. The memory 91 can also be used to temporarily store data that has been output or will be output.

[0154] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0155] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0156] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0157] In the embodiments provided by this invention, it should be understood that the disclosed device / lithium battery monitoring device and method can be implemented in other ways. For example, the device / lithium battery monitoring device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0158] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0159] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0160] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various lithium battery failure prediction method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0161] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A lithium battery failure prediction method, characterized by, The lithium battery includes a plurality of series-connected battery cells; the method includes: calculating the residual charging capacity of each battery cell at the time of full charging of the lithium battery; if the number of full charging at the time of full charging is greater than 1, obtaining the residual charging capacity of each battery cell at the time of last full charging; for each battery cell, calculating the difference between the residual charging capacity of the battery cell at the time of this full charging and the residual charging capacity at the time of last full charging; based on the differences in the residual charging capacity of each battery cell, determining the upper quartile, the median and the lower quartile among the differences; subtracting the lower quartile from the upper quartile to obtain the interquartile range; based on the upper quartile, the lower quartile and the interquartile range, obtaining the upper limit and the lower limit; if any difference is greater than the upper limit or less than the lower limit, determining the difference as an abnormal difference; if there is an abnormal difference, determining the battery cell corresponding to the abnormal difference based on the abnormal difference; based on the abnormal difference and the corresponding battery cell, determining the battery cell with micro-short circuit failure in the lithium battery.

2. The lithium battery failure prediction method of claim 1, wherein The calculation of the residual charging capacity of each battery cell at the time of full charging of the lithium battery includes: obtaining the charging voltage curve of each battery cell at the time of full charging of the lithium battery, wherein the lithium battery at the time of full charging includes a fully charged battery cell and a plurality of uncharged battery cells; for any uncharged battery cell, taking the charging voltage curve of the fully charged battery cell as a reference curve, transforming the reference curve so that the transformed reference curve overlaps with the charging voltage curve of the uncharged battery cell to obtain the full charging curve of the uncharged battery cell; based on the full charging curve of the uncharged battery cell, determining the residual charging time of the uncharged battery cell; based on the residual charging time and the charging current, obtaining the residual charging capacity of the battery cell.

3. The lithium battery failure prediction method of claim 2, wherein, The calculation of the residual charging capacity of each battery cell at the time of full charging of the lithium battery includes: based on the following formula, obtaining the residual charging capacity of the battery cell: wherein, represents the remaining charge capacity of the battery cell, represents the full charge time of the fully charged battery cell, represents the remaining charge time of the battery cell, represents the charging current.

4. The lithium battery failure prediction method of claim 1, wherein, After determining the abnormal difference and the corresponding battery cell, the method further includes: calculating the residual charging capacity of each battery cell and the difference at the time of full charging of the lithium battery in the next time, determining the abnormal difference and the corresponding battery cell; repeating the full charging for multiple times to determine the abnormal difference and the corresponding battery cell in each full charging; correspondingly, based on the abnormal difference and the corresponding battery cell, determining the battery cell with micro-short circuit failure in the lithium battery includes: based on the abnormal difference and the corresponding battery cell in each full charging, determining the battery cell with an abnormal number of times greater than or equal to a preset number of times in the lithium battery; determining the battery cell with an abnormal number of times greater than or equal to a preset number of times as the battery cell with micro-short circuit failure in the lithium battery.

5. The lithium battery failure prediction method of claim 1, wherein, The calculation of the residual charging capacity of each battery cell at the time of full charging of the lithium battery includes: determining whether the charging time is greater than a preset value and whether the charging rate is less than a preset value during the charging process; if the charging time is greater than the preset value and the charging rate is less than the preset value, calculating the residual charging capacity of each battery cell at the time of full charging of the lithium battery.

6. A lithium battery failure prediction apparatus characterized by comprising: The lithium battery includes a plurality of series-connected battery cells; the device includes: a residual charging capacity calculation module, configured to calculate the residual charging capacity of each battery cell at the time of full charging of the lithium battery; an obtaining module, configured to, if the number of full charging at the time of full charging is greater than 1, obtain the residual charging capacity of each battery cell at the time of last full charging; a difference calculation module, configured to calculate, for each battery cell, a difference between a current full-charge remaining charge capacity and a last full-charge remaining charge capacity of the battery cell; an abnormal difference determination module, configured to determine, based on the differences between the remaining charge capacities of the battery cells, upper quartiles, median values and lower quartiles of the differences; obtain an interquartile range by subtracting the lower quartiles from the upper quartiles; obtain upper and lower limits based on the upper quartiles, the lower quartiles and the interquartile range; determine any difference as an abnormal difference if the difference is greater than the upper limit or less than the lower limit; and determine, based on the abnormal difference, a battery cell corresponding to the abnormal difference if the abnormal difference exists; a failed battery cell determination module, configured to determine, based on the abnormal difference and the corresponding battery cell, a micro-short-circuit failed battery cell in the lithium battery.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the lithium battery failure prediction method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7. The computer program is executed by the processor to implement the steps of the lithium battery failure prediction method according to any one of claims 1 to 5.

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