Monitoring Method, Device, Computer Equipment and Storage Medium for Self-Discharge of Battery

Abstract

The present disclosure provides a method, device, computer device, and storage medium for monitoring self-discharge of a battery, relating to the technical field of battery management. The method includes: obtaining voltage monitoring data and current monitoring data of each battery cell; obtaining a first state of charge and a second state of charge of each battery cell according to the voltage monitoring data and the current monitoring data; determining an outlier trend of the first state of charge of each battery cell according to each first state of charge; determining an outlier trend of the second state of charge of each battery cell according to each second state of charge; and determining whether the self-discharge of each battery cell is abnormal according to the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell. Thus, monitoring of self-discharge is achieved based on the outlier trend of the state of charge of the battery, improving the accuracy and reliability of battery self-discharge monitoring.

Description

Technical Field

[0001] The present disclosure relates to the technical field of battery management, and particularly to a method, a device, a computer device, and a storage medium for monitoring self-discharge of a battery. Background Art

[0002] Electric vehicles use batteries as power sources and have relatively less impact on the environment compared to traditional vehicles. Therefore, more and more people choose electric vehicles as travel tools. The battery of an electric vehicle is composed of multiple battery cells connected in series and stacked. When there is a short circuit point inside the battery, the positive and negative electrodes are connected to generate a short circuit current, which will cause the self-discharge problem of the battery. When the self-discharge of the battery is abnormal, it will affect the normal use of the electric vehicle. Therefore, it is of great significance to study how to accurately and reliably monitor the self-discharge situation of the battery. Summary of the Invention

[0003] The present disclosure aims to at least solve one of the technical problems in the related art to some extent.

[0004] A first aspect embodiment of the present disclosure provides a method for monitoring self-discharge of a battery, including:

[0005] Obtaining voltage monitoring data and current monitoring data of each battery cell;

[0006] According to the voltage monitoring data and the current monitoring data, obtaining a first state of charge and a second state of charge of each battery cell, where the first state of charge is the state of charge of each battery cell at the initial moment of a set period, and the second state of charge is the state of charge of each battery cell at the end moment of the set period;

[0007] Determining an outlier trend of the first state of charge of each battery cell according to each first state of charge;

[0008] Determining an outlier trend of the second state of charge of each battery cell according to each second state of charge;

[0009] Determining whether the self-discharge of each battery cell is abnormal according to the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell.

[0010] A second aspect embodiment of the present disclosure provides a device for monitoring self-discharge of a battery, including:

[0011] A first obtaining module, configured to obtain voltage monitoring data and current monitoring data of each battery cell;

[0012] A second acquisition module, configured to obtain a first state of charge and a second state of charge of each battery cell according to the voltage monitoring data and the current monitoring data, where the first state of charge is the state of charge of each battery cell at the initial moment of a set period, and the second state of charge is the state of charge of each battery cell at the end moment of the set period;

[0013] A first determination module, configured to determine the outlier trend of the first state of charge of each battery cell according to each first state of charge;

[0014] A second determination module, configured to determine the outlier trend of the second state of charge of each battery cell according to each second state of charge;

[0015] A third determination module, configured to determine whether the self-discharge of each battery cell is abnormal according to the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell.

[0016] An embodiment of the third aspect of the present disclosure provides a computer device, including: a memory, a processor, and computer instructions stored on the memory and executable on the processor. When the processor executes the instructions, the monitoring method for battery self-discharge proposed in the embodiment of the first aspect of the present disclosure is implemented.

[0017] An embodiment of the fourth aspect of the present disclosure provides a vehicle, including the computer device proposed in the embodiment of the third aspect of the present disclosure.

[0018] An embodiment of the fifth aspect of the present disclosure provides a non-transitory computer-readable storage medium, storing computer instructions, and when the computer instructions are executed by a processor, the monitoring method for battery self-discharge proposed in the embodiment of the first aspect of the present disclosure is implemented.

[0019] An embodiment of the sixth aspect of the present disclosure provides a computer program product, and when the instruction processor in the computer program product executes, the monitoring method for battery self-discharge proposed in the embodiment of the first aspect of the present disclosure is executed.

[0020] The monitoring method, device, computer device, and storage medium for battery self-discharge provided by the present disclosure have the following beneficial effects:

[0021] First, obtain the voltage monitoring data and current monitoring data of each battery cell; then, based on the voltage monitoring data and current monitoring data, obtain the first state of charge and the second state of charge of each battery cell, where the first state of charge is the state of charge of each battery cell at the initial moment of the set period, and the second state of charge is the state of charge of each battery cell at the end moment of the set period; then, based on each first state of charge, determine the outlier trend of the first state of charge of each battery cell, and based on each second state of charge, determine the outlier trend of the second state of charge of each battery cell; finally, based on the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell, determine whether the self-discharge of each battery cell is abnormal. Thus, it realizes the monitoring of the self-discharge of the battery based on the outlier trend of the state of charge of the battery within a certain period of time, reduces the false alarm rate caused by data fluctuations, and improves the accuracy and reliability of the battery self-discharge monitoring.

[0022] Additional aspects and advantages of the present disclosure will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present disclosure will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, where:

[0024] Figure 1 is a schematic flowchart of a method for monitoring the self-discharge of a battery provided by an embodiment of the present disclosure;

[0025] Figure 2 is a schematic flowchart of a method for monitoring the self-discharge of a battery provided by another embodiment of the present disclosure;

[0026] Figure 3 is a schematic diagram of the battery SOC_OCV empirical curve provided by an embodiment of the present disclosure;

[0027] Figure 4 is a schematic structural diagram of a device for monitoring the self-discharge of a battery provided by an embodiment of the present disclosure;

[0028] Figure 5 shows a block diagram of an exemplary computer device suitable for implementing the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The embodiments of the present disclosure will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as a limitation of the present disclosure.

[0030] The following describes a method, device, computer device, and storage medium for monitoring the self-discharge of a battery according to an embodiment of the present disclosure with reference to the accompanying drawings.

[0031] Figure 1 It is a schematic flowchart of the method for monitoring the self-discharge of a battery provided by an embodiment of the present disclosure.

[0032] In an embodiment of the present disclosure, the method for monitoring the self-discharge of a battery is configured in a device for monitoring the self-discharge of a battery as an example. The device for monitoring the self-discharge of a battery can be applied to any on-vehicle device, cloud device, or other hardware devices with various operating systems, touchscreens, and / or displays, so that the device can perform the function of monitoring the self-discharge of a battery.

[0033] As Figure 1 shown, the method for monitoring the self-discharge of a battery may include the following steps:

[0034] Step 101: Obtain voltage monitoring data and current monitoring data of each battery cell.

[0035] It should be noted that the power battery of an electric vehicle is composed of multiple battery cells electrically connected. For example, a power battery may include 96 battery cells.

[0036] It can be understood that in order to ensure the reliable operation of the battery and extend the service life of the battery, each battery cell in the battery pack should reach a balanced and consistent state. Therefore, the voltage and current of each battery cell can be monitored to maintain and manage the battery.

[0037] Among them, the current monitoring data of each series-connected battery cell is the same, and the voltage monitoring data of the battery cells may be different due to individual differences. The voltage monitoring data is the voltage value of each battery cell at any moment, and the current monitoring data is the current value of each battery cell at any moment.

[0038] Step 102: Obtain the first state of charge and the second state of charge of each battery cell according to the voltage monitoring data and the current monitoring data, where the first state of charge is the state of charge of each battery cell at the initial moment of a set period, and the second state of charge is the state of charge of each battery cell at the end moment of the set period.

[0039] It should be noted that the state of charge (SOC) of the battery is the remaining battery power. When there is a short circuit point inside the battery, the positive and negative electrodes are connected to generate a short circuit current, and the available power gradually decreases over time, that is, the SOC decreases, and at the same time, the battery voltage decreases.

[0040] It can be understood that both the state of charge (SOC) of the battery and the battery voltage can be used as indicators to characterize the self-discharge of the battery. Among them, the decrease in the state of charge (SOC), that is, the integral of the short-circuit current over time, is currently the most accurate indicator for measuring the self-discharge of the battery.

[0041] Therefore, in the embodiments of the present disclosure, the state of charge (SOC) of each battery cell at the initial moment and the end moment of the set period can be obtained, so as to evaluate the self-discharge situation based on the state of charge (SOC) of each battery cell at different moments.

[0042] It should be noted that the evaluation of the self-discharge situation of the battery can be repeated according to the set time to achieve continuous monitoring of the battery.

[0043] For example, the self-discharge situation of the battery can be evaluated once a day. Or, the self-discharge situation of the battery can be evaluated once every two days. The present disclosure does not make any limitations in this regard.

[0044] It can be understood that the self-discharge situation of the battery will gradually change with the accumulation of time, but the self-discharge situation of the battery may not change much in a short period of time.

[0045] Therefore, in the embodiments of the present disclosure, a set period can be set, and the self-discharge situation of the battery can be evaluated based on the state of charge of the battery at the initial moment and the end moment of the set period.

[0046] Among them, the duration of the set period can be set according to actual needs. For example, it can be 5 days, 7 days, 10 days, etc. The present disclosure does not make any limitations in this regard.

[0047] Step 103: Determine the outlier trend of the first state of charge of each battery cell according to each first state of charge.

[0048] It should be noted that the power battery of an electric vehicle may include multiple battery cells, and each battery cell corresponds to a state of charge respectively. The outlier trend of the state of charge can be any type of value that can characterize the difference in the state of charge of each battery cell.

[0049] For example, the first states of charge of each battery cell are SOC 11 , SOC 12 , ……, SOC 1n . Among them, n is the number of battery cells. The average value of the first states of charge of each battery cell is: (SOC 11 + SOC 12 + …… + SOC 1n ) / n, and the outlier trend of the first state of charge of each battery cell can be:

[0050] It should be noted that the above examples are only for illustration and cannot be used as a limitation to the first state of charge outlier trend in the embodiments of the present disclosure.

[0051] Step 104: Determine the second state of charge outlier trend of each battery cell according to each second state of charge.

[0052] Among them, the second state of charge outlier trend and the first state of charge outlier trend characterize the state of charge characteristics of the battery cell at different times. Therefore, for the specific implementation of determining the second state of charge outlier trend of each battery cell according to each second state of charge, reference can be made to the implementation of determining the first state of charge outlier trend of each battery cell according to each first state of charge, which will not be elaborated here.

[0053] Step 105: Determine whether the self-discharge of each battery cell is abnormal according to the first state of charge outlier trend and the second state of charge outlier trend corresponding to each battery cell.

[0054] Among them, there are various judgment methods when determining whether the self-discharge of each battery cell is abnormal.

[0055] For example, a certain numerical range can be set in advance. When the difference between the first state of charge outlier trend and the second state of charge outlier trend exceeds this numerical range, it can be determined that the self-discharge of this battery cell is abnormal. Otherwise, it can be determined that the self-discharge of this battery cell is normal.

[0056] Alternatively, corresponding weights can be assigned to the first state of charge outlier trend and the second state of charge outlier trend respectively. Then, multiply the first state of charge outlier trend and the second state of charge outlier trend by their corresponding weights respectively, and then subtract. If the difference is greater than a preset threshold, it can be determined that the self-discharge of this battery cell is abnormal. Otherwise, it can be determined that the self-discharge of this battery cell is normal.

[0057] It should be noted that the above examples are only for illustration and cannot be used as a limitation to determining whether the self-discharge of the battery cell is abnormal according to the first state of charge outlier trend and the second state of charge outlier trend in the embodiments of the present disclosure.

[0058] In an embodiment of the present disclosure, first, voltage monitoring data and current monitoring data of each battery cell are obtained; then, according to the voltage monitoring data and the current monitoring data, a first state of charge and a second state of charge of each battery cell are obtained, where the first state of charge is the state of charge of each battery cell at the initial moment of a set period, and the second state of charge is the state of charge of each battery cell at the end moment of the set period; then, according to each first state of charge, a first state of charge outlier trend of each battery cell is determined, and according to each second state of charge, a second state of charge outlier trend of each battery cell is determined; finally, according to the first state of charge outlier trend and the second state of charge outlier trend corresponding to each battery cell, whether the self-discharge of each battery cell is abnormal is determined. Thus, monitoring of the self-discharge of the battery is realized based on the outlier trend of the state of charge of the battery within a certain period of time, the false alarm rate caused by data fluctuations is reduced, and the accuracy and reliability of the self-discharge monitoring of the battery are improved.

[0059] Figure 2 It is a schematic flow chart of a method for monitoring the self-discharge of a battery provided by another embodiment of the present disclosure. As Figure 2 shown, the method for monitoring the self-discharge of the battery may include the following steps:

[0060] Step 201, obtain voltage monitoring data and current monitoring data of each battery cell.

[0061] Among them, for the specific implementation manner of step 201, reference may be made to the detailed description of other embodiments of the present disclosure, which will not be elaborated here.

[0062] Step 202, determine a target time period during which the current monitoring data is within a set range within a set period.

[0063] It can be understood that when an electric vehicle is in a driving state, the battery current is significantly greater than the current when the electric vehicle is in a stationary state. Based on the premise that the change trends of batteries with the same design are similar after being left for a certain period of time after a large current, the time period when the electric vehicle is in a stationary state can be determined according to the battery current monitoring data, and the corresponding state of charge can be determined according to the battery voltage in the stationary state.

[0064] In an embodiment of the present disclosure, the set range can be set according to the current value, and the time period during which the current monitoring data is within the set range is screened as the target time period.

[0065] For example, the set range can be from 0 to 10 A, then the time period during which the current magnitude is between 0 and 10 A within the set period is the target time period. Or, the set range can be from 0 to 5 A, then the time period during which the current magnitude is between 0 and 5 A within the set period is the target time period.

[0066] It should be noted that the above examples are only for illustration and cannot be used to limit the set range, target time period, etc. in the embodiments of the present disclosure.

[0067] Step 203: Determine the first state of charge of each battery cell according to the voltage monitoring data of each battery cell at the initial moment of the target time period and the mapping relationship between the state of charge of the battery and the open-circuit voltage.

[0068] It can be understood that the current data and voltage data of the battery cell at each moment are in one-to-one correspondence. That is to say, when the target time period is determined according to the current monitoring data, the voltage monitoring data of the target time period can be obtained.

[0069] It should be noted that the terminal voltage of the battery in the open-circuit state is called the open-circuit voltage (abbreviated as OCV). There is a certain mapping relationship between the state of charge of the battery and the open-circuit voltage, which can be characterized by the SOC_OCV empirical curve, as Figure 3 shown.

[0070] Therefore, according to the voltage monitoring data of the battery cell at the initial moment of the target time period and based on the SOC_OCV empirical curve characterizing the mapping relationship between the state of charge of the battery and the open-circuit voltage, the state of charge at the initial moment of the target time period can be obtained.

[0071] Among them, based on the SOC_OCV empirical curve of the battery, it can be known that the SOC and OCV are approximately linearly related in the interval, and the state of charge differences corresponding to the same voltage difference in different voltage intervals are different. Therefore, the linear interpolation method can be used to obtain the state of charge of the battery at all moments within the first target time period, and the sliding average method is used for downsampling and data denoising.

[0072] It should be noted that since the target time period is determined according to the current magnitude of the battery. Within a set period, the target time period can be composed of multiple non-contiguous sub-time periods.

[0073] For example, when the set period is 7 days, the target time period may include 0:00 to 8:00 on the first day, 12:00 to 14:00, 6:00 to 22:00 on the second day, 0:00 to 12:00 on the seventh day, etc. Correspondingly, the initial moment of the target time period is 0:00 on the first day, and the end moment of the target time period is 12:00 on the seventh day.

[0074] In addition, in some embodiments, in addition to the current of the battery, the duration can also be combined to determine the target time period. For example, the time period during which the current of the battery lasts for more than 1 hour within the set range is the target time period.

[0075] It should be noted that the above examples are only for illustration and cannot be used to limit the target time period in the embodiments of the present disclosure.

[0076] Step 204: Determine the first state of charge (SOC) discreteness rate of all battery cells based on each first SOC.

[0077] It should be noted that, in theory, the charging and discharging among battery cells should be consistent, that is, the difference in the SOC among battery cells should be as small as possible. When the difference in the SOC of each battery cell is large, it can be determined that the consistency of each battery cell is poor. At this time, the result of judging the self-discharge situation based on the SOC of the battery cell may not be accurate.

[0078] In the embodiments of the present disclosure, to improve the accuracy of the judgment result, the first SOC discreteness rate of all battery cells can be determined based on the first SOC corresponding to each battery cell, and then the consistency of all battery cells can be evaluated according to the SOC discreteness rate.

[0079] In a possible implementation manner, the first SOC discreteness rate of all battery cells can be determined according to the standard deviation and / or variance of the first SOC corresponding to each battery cell.

[0080] Step 205: In response to the first SOC discreteness rate being less than or equal to the second set threshold, determine the first reference SOC based on the median of the first SOC corresponding to all battery cells.

[0081] Among them, the second set threshold can be any value set in advance, and the present disclosure does not limit this. The first reference SOC can be used as a comparison benchmark for the first SOC corresponding to each battery cell. By comparing the first SOC corresponding to each battery cell with the first reference SOC, the difference between the two can be determined.

[0082] In the embodiments of the present disclosure, the median of the first SOC corresponding to all battery cells can be used as the first reference SOC.

[0083] For example, the first SOC of each battery cell is SOC 11 , SOC 12 , ……, SOC 1n . Arrange SOC 11 , SOC 12 , ……, SOC 1n in ascending order of numerical value, and the number in the middle position is the first reference SOC. If there are an even number of first SOCs, the average of the two middlemost values can be taken as the first reference SOC.

[0084] Step 206: Determine the first SOC outlier trend of each battery cell according to the difference between the first SOC corresponding to each battery cell and the first reference SOC.

[0085] Among them, the outlier trend of the state of charge can characterize the differences in the first state of charge corresponding to each battery cell. In the embodiments of the present disclosure, the difference between the first state of charge corresponding to each battery cell and the first reference state of charge can be used as the outlier trend of the first state of charge of each battery cell.

[0086] For example, the first states of charge of each battery cell are SOC 11 , SOC 12 , ……, SOC 1n . The outlier trend of the first state of charge of each battery cell can be: SOC 11 - SOC 1i , SOC 12 - SOC 1i , ……, SOC 1n - SOC 1i . Among them, SOC 1i is the first reference state of charge.

[0087] Step 207: Determine the second state of charge of each battery cell according to the voltage monitoring data at the end moment of the target period of each battery cell and the mapping relationship between the state of charge of the battery and the open-circuit voltage.

[0088] Among them, for the specific implementation manner of determining the second state of charge of each battery cell, reference may be made to the specific implementation manner of determining the first state of charge of each battery cell in the embodiments of the present disclosure, which will not be elaborated herein.

[0089] Step 208: Determine the second state of charge dispersion rate of all battery cells according to each second state of charge.

[0090] Among them, for the specific implementation manner of determining the second state of charge dispersion rate, reference may be made to the detailed description of determining the first state of charge dispersion rate in the embodiments of the present disclosure, which will not be elaborated herein.

[0091] Step 209: In response to the second state of charge dispersion rate being less than or equal to the second set threshold, determine the second reference state of charge according to the median of the second state of charge corresponding to each battery cell.

[0092] Among them, for the specific implementation manner of determining the second reference state of charge, reference may be made to the detailed description of determining the first reference state of charge in the embodiments of the present disclosure, which will not be elaborated herein.

[0093] Step 210: Determine the outlier trend of the second state of charge of each battery cell according to the difference between the second state of charge corresponding to each battery cell and the second reference state of charge.

[0094] Among them, for the specific implementation of determining the outlier trend of the second state of charge of each battery cell, reference may be made to the detailed description of determining the outlier trend of the first state of charge of each battery cell in the embodiments of the present disclosure, which will not be elaborated here.

[0095] Step 211: Determine the change amount of the outlier trend of each battery cell according to the difference between the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell.

[0096] Step 212: Determine the change rate of the outlier trend of each battery cell according to the ratio of the change amount of the outlier trend of each battery cell to the set period.

[0097] Among them, the outlier trend of the state of charge corresponding to each battery cell characterizes the difference in the state of charge among the battery cells. In theory, the charging and discharging among the battery cells should be consistent, that is, the difference in the state of charge among the battery cells should be as small as possible.

[0098] It can be understood that as time accumulates, when the difference between a certain battery cell and other battery cells gradually becomes larger, it indicates that the self-discharge of this battery cell is abnormal.

[0099] Therefore, in the embodiments of the present disclosure, by comparing the difference between the outlier trend of the first state of charge and the outlier trend of the second state of charge of the battery cell, the change amount of the outlier trend of the battery cell can be obtained.

[0100] Furthermore, according to the ratio of the change amount of the outlier trend of the battery cell to the set period, the change rate of the outlier trend of the battery cell can be obtained.

[0101] For example, if the outlier trend of the first state of charge of the battery cell is ΔSOC1, the outlier trend of the second state of charge is ΔSOC2, the set period is T, and the change rate of the outlier trend of the battery cell is: (ΔSOC1 - ΔSOC2) / T.

[0102] Step 213: In response to the change rate of the outlier trend of any battery cell being greater than the first set threshold, determine that the self-discharge of the battery cell is abnormal.

[0103] Step 214: In response to the change rate of the outlier trend of each battery cell being less than or equal to the first set threshold, determine that the self-discharge of each battery cell is normal.

[0104] It can be understood that when the change rate of the outlier trend of a certain battery cell gradually becomes larger, it indicates that the difference between this battery cell and other battery cells is getting larger and larger.

[0105] Therefore, a threshold for the change rate of the outlier trend of the battery cell can be preset, and by comparing the change rate of the outlier trend with the set threshold, it can be determined whether the self-discharge of the battery cell is abnormal.

[0106] Specifically, when the outlier trend change rate of any battery cell is greater than the first set threshold, it can be determined that the self-discharge of the battery cell is abnormal. Otherwise, the self-discharge of the battery cell is normal.

[0107] Among them, the first set threshold can be any value set in advance, and the present disclosure does not limit this.

[0108] For example, when the first set threshold is 0.1 and the outlier trend change rate of a certain battery cell is 0.16, which is greater than the first set threshold, it can be determined that the self-discharge of the battery cell is abnormal. Or, when the outlier trend change rate of a certain battery cell is 0.07, which is less than the first set threshold, it can be determined that the self-discharge of the battery cell is normal.

[0109] In the embodiment of the present disclosure, first, the voltage monitoring data and current monitoring data of each battery cell are obtained, then the target time period within the set period is selected according to the current monitoring data of the battery cell, and the corresponding state of charge is obtained based on the voltage monitoring data of the target time period; then the first state-of-charge outlier trend at the initial moment of the target time period and the second state-of-charge outlier trend at the end moment of the target time period of the battery cell are determined according to the state of charge; finally, whether the self-discharge of the battery cell is abnormal is determined according to the first state-of-charge outlier trend, the second state-of-charge outlier trend, and the duration of the set period. Thus, by using the long-term state-of-charge outlier trend of the battery cell for determination, the accuracy of the battery self-discharge determination is greatly improved, and the false alarm rate caused by data fluctuations is reduced.

[0110] To implement the above embodiment, the present disclosure also proposes a monitoring device for battery self-discharge.

[0111] Figure 4 It is a schematic structural diagram of the monitoring device for battery self-discharge provided by the embodiment of the present disclosure.

[0112] As Figure 4 shown, the monitoring device 100 for battery self-discharge may include: a first acquisition module 110, a second acquisition module 120, a first determination module 130, a second determination module 140, and a third determination module 150.

[0113] Among them, the first acquisition module 110 is used to acquire the voltage monitoring data and current monitoring data of each battery cell;

[0114] The second acquisition module 120 is used to acquire the first state of charge and the second state of charge of each battery cell according to the voltage monitoring data and current monitoring data, where the first state of charge is the state of charge of each battery cell at the initial moment of the set period, and the second state of charge is the state of charge of each battery cell at the end moment of the set period;

[0115] The first determination module 130 is configured to determine the outlier trend of the state of charge of each battery cell according to each first state of charge.

[0116] The second determination module 140 is configured to determine the outlier trend of the second state of charge of each battery cell according to each second state of charge.

[0117] The third determination module 150 is configured to determine whether the self-discharge of each battery cell is abnormal according to the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell.

[0118] For the functions and specific implementation principles of the above-mentioned modules in the embodiments of the present disclosure, reference may be made to the above-mentioned method embodiments, and details are not described herein again.

[0119] The monitoring device for the self-discharge of the battery in the embodiment of the present disclosure first obtains the voltage monitoring data and current monitoring data of each battery cell; then, according to the voltage monitoring data and current monitoring data, obtains the first state of charge and the second state of charge of each battery cell, where the first state of charge is the state of charge of each battery cell at the initial moment of the set period, and the second state of charge is the state of charge of each battery cell at the end moment of the set period; then, according to each first state of charge, determines the outlier trend of the first state of charge of each battery cell, and according to each second state of charge, determines the outlier trend of the second state of charge of each battery cell; finally, according to the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell, determines whether the self-discharge of each battery cell is abnormal. Thus, the monitoring of the self-discharge of the battery is realized based on the outlier trend of the state of charge of the battery within a certain period of time, the false alarm rate caused by data fluctuations is reduced, and the accuracy and reliability of the self-discharge monitoring of the battery are improved.

[0120] In a possible implementation manner of the embodiment of the present disclosure, the third determination module 150 includes:

[0121] The first determination unit is configured to determine the change amount of the outlier trend of each battery cell according to the difference between the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell.

[0122] The second determination unit is configured to determine the change rate of the outlier trend of each battery cell according to the ratio of the change amount of the outlier trend of each battery cell to the set period.

[0123] The third determination unit is configured to determine that the self-discharge of the battery cell is abnormal in response to the change rate of the outlier trend of any battery cell being greater than the first set threshold.

[0124] The fourth determination unit is configured to determine that the self-discharge of each battery cell is normal in response to the change rate of the outlier trend of each battery cell being less than or equal to the first set threshold.

[0125] In a possible implementation manner of the embodiment of the present disclosure, the second acquisition module 120 includes:

[0126] A fifth determination unit, configured to determine a target time period in which the current monitoring data is within a set range within a set period;

[0127] A sixth determination unit, configured to determine a first state of charge of each battery cell according to the voltage monitoring data at the initial moment of each battery cell in the target time period and the mapping relationship between the state of charge of the battery and the open-circuit voltage.

[0128] A seventh determination unit, configured to determine a second state of charge of each battery cell according to the voltage monitoring data at the end moment of each battery cell in the target time period and the mapping relationship between the state of charge of the battery and the open-circuit voltage.

[0129] In a possible implementation manner of the embodiment of the present disclosure, the first determination module 130 includes:

[0130] An eighth determination unit, configured to determine a reference state of charge according to the median of the first states of charge corresponding to all battery cells respectively;

[0131] A ninth determination unit, configured to determine the outlier trend of the first state of charge of each battery cell according to the difference between the first state of charge corresponding to each battery cell and the reference state of charge.

[0132] In a possible implementation manner of the embodiment of the present disclosure, the second determination module 140 includes:

[0133] A tenth determination unit, configured to determine a second reference state of charge according to the median of the second states of charge corresponding to all the battery cells respectively;

[0134] An eleventh determination unit, configured to determine the outlier trend of the second state of charge of each battery cell according to the difference between the second state of charge corresponding to each battery cell and the second reference state of charge.

[0135] In a possible implementation manner of the embodiment of the present disclosure, the device further includes:

[0136] A fourth determination module, configured to determine the first state of charge dispersion rate of all battery cells according to each first state of charge;

[0137] The first determination module is configured to, in response to the first state of charge dispersion rate being less than or equal to a second set threshold, determine the outlier trend of the first state of charge of each battery cell according to each first state of charge.

[0138] In a possible implementation manner of the embodiment of the present disclosure, the device further includes:

[0139] A fifth determination module, configured to determine the second state of charge discretization rate of all battery cells according to each second state of charge.

[0140] The second determination module is configured to, in response to the second state of charge discretization rate being less than or equal to a second set threshold, determine the second state of charge outlier trend of each battery cell according to each second state of charge.

[0141] In a possible implementation manner of the embodiments of the present disclosure, the fourth determination module is configured to:

[0142] Determine the first state of charge discretization rate according to the standard deviation and / or variance of the first state of charge corresponding to each battery cell.

[0143] In a possible implementation manner of the embodiments of the present disclosure, the fifth determination module is configured to:

[0144] Determine the second state of charge discretization rate according to the standard deviation and / or variance of the second state of charge corresponding to each battery cell.

[0145] For the functions and specific implementation principles of the above-mentioned modules in the embodiments of the present disclosure, reference may be made to the above-mentioned method embodiments, and details are not described herein again.

[0146] The battery self-discharge monitoring device in the embodiments of the present disclosure first obtains the voltage monitoring data and current monitoring data of each battery cell, then screens the target time period within the set period according to the current monitoring data of the battery cell, and obtains the corresponding state of charge based on the voltage monitoring data of the target time period; then determines the first state of charge outlier trend of the battery cell at the initial moment of the target time period and the second state of charge outlier trend of the battery cell at the initial moment of the target time period according to the state of charge; finally, determines whether the self-discharge of the battery cell is abnormal according to the first state of charge outlier trend, the second state of charge outlier trend, and the duration of the set period. Thus, by using the long-term state of charge outlier trend of the battery cell for determination, the accuracy of the battery self-discharge determination is greatly improved, and the false alarm rate caused by data fluctuations is reduced.

[0147] To implement the above embodiments, the present disclosure also proposes a computer device, including: a memory, a processor, and computer instructions stored on the memory and executable on the processor. When the processor executes the instructions, the battery self-discharge monitoring method proposed in the foregoing embodiments of the present disclosure is implemented.

[0148] To implement the above embodiments, the present disclosure also proposes a vehicle, including the computer device proposed in the foregoing embodiments of the present disclosure.

[0149] To implement the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the method for monitoring battery self-discharge as proposed in the foregoing embodiments of the present disclosure.

[0150] To implement the above embodiments, the present disclosure also provides a computer program product, which, when the instructions in the computer program product are executed by a processor, executes the method for monitoring battery self-discharge as proposed in the foregoing embodiments of the present disclosure.

[0151] Figure 5 The block diagram of an exemplary computer device suitable for implementing the embodiments of the present disclosure is shown. Figure 5 The displayed computer device 12 is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0152] As Figure 5 shown, the computer device 12 is presented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0153] The bus 18 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the multiple bus architectures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnection (PCI) bus.

[0154] The computer device 12 typically includes a variety of computer system-readable media. These media can be any available media accessible by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0155] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used for reading and writing on non-removable, non-volatile magnetic media ( Figure 5 not shown, commonly referred to as a "hard disk drive"). Although Figure 5 not shown in the figure, a disk drive for reading and writing on removable non-volatile disks (such as "floppy disks") and an optical disk drive for reading and writing on removable non-volatile optical disks (such as compact disc read only memory (CD-ROM), digital video disc read only memory (DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 through one or more data media interfaces. The memory 28 may include at least one program product having a set (such as at least one) of program modules configured to perform the functions of the embodiments of the present disclosure.

[0156] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 42 generally perform the functions and / or methods in the embodiments described in the present disclosure.

[0157] The computer device 12 can also communicate with one or more external devices 14 (such as keyboards, pointing devices, monitors 24, etc.), and can also communicate with one or more devices that enable users to interact with the computer device 12, and / or communicate with any device that enables the computer device 12 to communicate with one or more other computing devices (such as network cards, modems, etc.). Such communication can be carried out through the input / output (I / O) interface 22. Moreover, the computer device 12 can also communicate with one or more networks (such as a Local Area Network (LAN), a Wide Area Network (WAN), and / or a public network, such as the Internet) through the network adapter 20. As shown in the figure, the network adapter 20 communicates with other modules of the computer device 12 through the bus 18. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0158] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the methods mentioned in the foregoing embodiments.

[0159] The technical solution of the present disclosure first obtains the voltage monitoring data and current monitoring data of each battery cell; then, according to the voltage monitoring data and current monitoring data, obtains the first state of charge and the second state of charge of each battery cell, where the first state of charge is the state of charge of each battery cell at the initial moment of the set period, and the second state of charge is the state of charge of each battery cell at the end moment of the set period; then, according to each first state of charge, determines the outlier trend of the first state of charge of each battery cell, and according to each second state of charge, determines the outlier trend of the second state of charge of each battery cell; finally, according to the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell, determines whether the self-discharge of each battery cell is abnormal. Thus, it realizes the monitoring of the self-discharge of the battery based on the outlier trend of the state of charge of the battery within a certain period of time, reduces the false alarm rate caused by data fluctuations, and improves the accuracy and reliability of the battery self-discharge monitoring.

[0160] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0161] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0162] Any process or method description, whether in a flowchart or otherwise described herein, can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present disclosure pertain.

[0163] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0164] It should be understood that various parts of the present disclosure can be implemented using hardware, software, firmware, or combinations thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0165] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above-described embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0166] In addition, each functional unit in various embodiments of the present disclosure may be integrated into one processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0167] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for monitoring self-discharge of a battery, characterized in that, Including: Obtaining voltage monitoring data and current monitoring data of each battery cell; Obtaining a first state of charge and a second state of charge of each battery cell according to the voltage monitoring data and the current monitoring data, wherein the first state of charge is the state of charge of each battery cell at the initial moment of a set period, and the second state of charge is the state of charge of each battery cell at the end moment of the set period; Determining an outlier trend of the first state of charge of each battery cell according to each first state of charge; Determining an outlier trend of the second state of charge of each battery cell according to each second state of charge; Determining whether the self-discharge of each battery cell is abnormal according to the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell, including: Determining a change amount of the outlier trend of each battery cell according to a difference between the outlier trend of the first state of charge and the outlier trend of the second state of charge corresponding to each battery cell, wherein the outlier trend of the state of charge corresponding to each battery cell represents the difference in the state of charge among the battery cells; Determining a change rate of the outlier trend of each battery cell according to a ratio of the change amount of the outlier trend of each battery cell to the set period, wherein when the change rate of the outlier trend of a certain battery cell gradually increases, it indicates that the difference between the battery cell and other battery cells is getting larger; Determining that the self-discharge of the battery cell is abnormal in response to the change rate of the outlier trend of any battery cell being greater than a first set threshold; Determining that the self-discharge of each battery cell is normal in response to the change rate of the outlier trend of each battery cell being less than or equal to the first set threshold.

2. The method according to claim 1, characterized in that, The obtaining the first state of charge and the second state of charge of each battery cell according to the voltage monitoring data and the current monitoring data includes: Determining a target time period in which the current monitoring data is within a set range during the set period; Determining the first state of charge of each battery cell according to the voltage monitoring data at the initial moment of each battery cell in the target time period and the mapping relationship between the state of charge of the battery and the open-circuit voltage; Determining the second state of charge of each battery cell according to the voltage monitoring data at the end moment of each battery cell in the target time period and the mapping relationship between the state of charge of the battery and the open-circuit voltage.

3. The method according to claim 1, wherein The determining the outlier trend of the first state of charge of each battery cell according to each first state of charge includes: Determining a first reference state of charge according to the median of the first states of charge corresponding to all the battery cells; Determining the outlier trend of the first state of charge of each battery cell according to the difference between the first state of charge corresponding to each battery cell and the first reference state of charge; The determining the outlier trend of the second state of charge of each battery cell according to each second state of charge includes: Determining a second reference state of charge according to the median of the second states of charge corresponding to all the battery cells; Determine the second state of charge outlier trend of each battery cell according to the difference between the second state of charge corresponding to each battery cell and the second reference state of charge.

4. The method according to any one of claims 1-3, characterized in that Further included: Determine the first state of charge dispersion rate of all battery cells according to each of the first states of charge; Determine the second state of charge dispersion rate of all battery cells according to each of the second states of charge; The step of determining the first state of charge outlier trend of each battery cell according to each of the first states of charge includes: In response to the first state of charge dispersion rate being less than or equal to a second set threshold, determine the first state of charge outlier trend of each battery cell according to each of the first states of charge; The step of determining the second state of charge outlier trend of each battery cell according to each of the second states of charge includes: In response to the second state of charge dispersion rate being less than or equal to a second set threshold, determine the second state of charge outlier trend of each battery cell according to each of the second states of charge.

5. The method according to claim 4, characterized in that, The step of determining the first state of charge dispersion rate of all battery cells according to each of the first states of charge includes: Determine the first state of charge dispersion rate according to the standard deviation and / or variance of the first state of charge corresponding to each battery cell; The step of determining the second state of charge dispersion rate of all battery cells according to each of the second states of charge includes: Determine the second state of charge dispersion rate according to the standard deviation and / or variance of the second state of charge corresponding to each battery cell.

6. A monitoring device for self-discharge of a battery, characterized in that, Included: A first acquisition module for acquiring voltage monitoring data and current monitoring data of each battery cell; A second acquisition module for acquiring the first state of charge and the second state of charge of each battery cell according to the voltage monitoring data and the current monitoring data, where the first state of charge is the state of charge of each battery cell at the initial moment of a set period, and the second state of charge is the state of charge of each battery cell at the end moment of the set period; A first determination module for determining the first state of charge outlier trend of each battery cell according to each of the first states of charge; A second determination module for determining the second state of charge outlier trend of each battery cell according to each of the second states of charge; A third determination module for determining whether the self-discharge of each battery cell is abnormal according to the first state of charge outlier trend and the second state of charge outlier trend corresponding to each battery cell; The third determination module includes: A first determination unit for determining the outlier trend change amount of each battery cell according to the difference between the first state of charge outlier trend and the second state of charge outlier trend corresponding to each battery cell, where the state of charge outlier trend corresponding to each battery cell characterizes the difference in the state of charge among the battery cells; A second determination unit, configured to determine the outlier trend change rate of each battery cell according to the ratio of the outlier trend change amount of each battery cell to the set period, wherein when the outlier trend change rate of a certain battery cell gradually increases, it indicates that the difference between the battery cell and other battery cells is becoming larger and larger; A third determination unit, configured to determine that the self-discharge of the battery cell is abnormal in response to the outlier trend change rate of any one of the battery cells being greater than a first set threshold; A fourth determination unit, configured to determine that the self-discharge of each battery cell is normal in response to the outlier trend change rate of each battery cell being less than or equal to the first set threshold.

7. A computer device, characterized in that, Comprising a memory, a processor, and computer instructions stored on the memory and executable on the processor, when the processor executes the instructions, implementing the battery self-discharge monitoring method according to any one of claims 1-5.

8. A vehicle, characterized in that, Comprising the computer device according to claim 7.

9. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are executed by the processor, implementing the battery self-discharge monitoring method according to any one of claims 1-5.

10. A computer program product, characterized in that, Comprising computer instructions, which implement the battery self-discharge monitoring method according to any one of claims 1-5 when executed by the processor.

Images