Power battery monitoring method and equipment, electric vehicle and medium

Abstract

The invention provides a power battery monitoring method and device, an electric vehicle and a medium, and the method comprises the steps: if a power battery is charged to the tail end and the SOC value of the power battery is greater than a preset SOC value, obtaining the change rate of the voltage of each cell along with the capacity in the charging process; determining the standard deviation of each battery module according to the change rate of the voltage of each battery cell in each battery module along with the capacity; and based on the standard deviation of each battery module, if the existence of the discrete battery module is detected, in a preset SOC range, according to the obtained standard deviation of the discrete battery module at different time points in the full life cycle, determining the anomaly type of the discrete battery module. According to the invention, the abnormity of the power battery is detected by using the standard deviation of the module at the SOC high-order end, so that the consistency of the power battery can be detected no matter the power battery is charged in a fast charging mode or a slow charging mode, and the use scene is richer.

Description

Technical Field

[0001] The present invention relates to the technical field of power batteries, and specifically provides a monitoring method, equipment, electric vehicle and medium for a power battery. Background Art

[0002] When a cell in a power battery fails, it will be significantly different from other cells in the power battery. Therefore, based on the difference in consistency of cells in the power battery, abnormal diagnosis and prediction can be performed. For example, when a cell in a power battery has a gradual internal short circuit, due to the existence of self-discharge current, the voltage of the cell will be lower than the voltage of other cells in the power battery, and the voltage difference will gradually increase with the degree of internal short circuit. When the cell has poor welding and other conditions, its internal resistance may increase abnormally and be outliers compared to the rest of the cells. Further development may cause thermal runaway risk due to abnormal heat generation.

[0003] Normally, the rate of change of the cell's voltage with capacity (i.e., the voltage-capacity differential dV / dQ) is a commonly used analysis method for the battery's charge and discharge process, but it is usually analyzed for changes in the peak position and peak area of ​​the dV / dQ curve over time, which corresponds to the voltage platform, i.e., the change in the phase change point. For power batteries, since the discharge current is unstable, dV / dQ can only be calculated during the charging process. To accurately locate the dV / dQ curve peak, the charging current must be small, otherwise the feature is difficult to extract. Therefore, there are limitations to the use of abnormal diagnosis of power batteries based on the peak position and peak area of ​​the dV / dQ curve. Summary of the invention

[0004] In order to overcome the above-mentioned defects, the present invention is proposed to provide a power battery monitoring method, equipment, electric vehicle and medium that solves or at least partially solves the technical problem that there are limitations in the use scenarios of abnormal diagnosis of power batteries based on peak position, peak area, etc. of dV / dQ curve.

[0005] In a first aspect, the present invention provides a monitoring method for a power battery, wherein the power battery comprises a plurality of battery modules, wherein the battery modules comprise a plurality of battery cells; the method comprises:

[0006] If the power battery is charged to the end and the SOC value of the power battery is greater than the preset SOC value, the voltage change rate of each battery cell with the capacity during the charging process is obtained;

[0007] Determining the standard deviation of each battery module according to the rate of change of the voltage of each battery cell in each battery module with the capacity;

[0008] Within a preset SOC range, based on the standard deviation of each of the battery modules, detecting whether there is a discrete battery module;

[0009] If the existence of the discrete battery module is detected, within the preset SOC range, obtaining the standard deviation of the discrete battery module at different time points in the entire life cycle;

[0010] The abnormal type of the discrete battery module is determined according to the standard deviation of the discrete battery module at different time points in the entire life cycle.

[0011] Furthermore, in the above-mentioned power battery monitoring method, determining the abnormal type of the discrete battery module according to the standard deviation of the discrete battery module at different time points in the whole life cycle includes:

[0012] If the standard deviation of the discrete battery module at different time points in the entire life cycle increases with time, it is determined that the abnormality type of the discrete battery module is a single-cell abnormality;

[0013] If the standard deviation of the discrete battery module at different time points in the entire life cycle does not increase with time, it is determined that the abnormal type of the discrete battery module is poor initial consistency of the battery cells.

[0014] Furthermore, the above-mentioned power battery monitoring method further includes:

[0015] Sequentially obtaining a first difference between standard deviations of the discrete battery module at two adjacent time points in the entire life cycle;

[0016] In the preset time proportion of the whole life cycle, if the first difference is greater than a first threshold value and the change value of the first difference is outside a preset range, it is determined that the standard deviation of the discrete battery module in the whole life cycle increases with time;

[0017] If the first difference is greater than a first threshold value, and a change in the first difference is within a preset range, it is determined that the standard deviation of the discrete battery module in the entire life cycle does not increase with time.

[0018] Furthermore, in the above-mentioned power battery monitoring method, obtaining the rate of change of the voltage of each battery cell with the capacity during the charging process includes:

[0019] Obtain the voltage, current and charging time of each battery cell during the charging process;

[0020] The voltage-to-capacity change rate is determined according to the voltage, the current, and the charging time.

[0021] Furthermore, in the above-mentioned power battery monitoring method, the standard deviation of each battery module is determined according to the rate of change of the voltage of each battery cell in each battery module with the capacity, including:

[0022] The voltage change rate with capacity of each battery cell is averaged according to SOC to obtain the average voltage change rate with capacity of each battery cell;

[0023] The standard deviation of each battery module is determined based on the rate of change of the average voltage of each cell in each battery module with capacity.

[0024] Furthermore, in the above-mentioned power battery monitoring method, within a preset SOC range, based on the standard deviation of each of the battery modules, detecting whether there is a discrete battery module includes:

[0025] According to the standard deviation of each battery module, the mean standard deviation of all battery modules is obtained;

[0026] Determining a second difference between a standard deviation of each of the battery modules and a mean standard deviation of all the battery modules;

[0027] If the second difference is greater than a second preset threshold, it is determined that the battery module is a discrete battery module.

[0028] Furthermore, the above-mentioned power battery monitoring method further includes:

[0029] If it is detected that the discrete battery module does not exist, it is determined that the power battery has no abnormality.

[0030] In a second aspect, the present invention provides a monitoring device for a power battery, the monitoring device for the power battery comprising a processor and a storage device, the storage device being suitable for storing a plurality of program codes, the program codes being suitable for being loaded and run by the processor to execute any one of the power battery monitoring methods described above.

[0031] In a third aspect, an electric vehicle is provided, the electric vehicle comprising the monitoring device for the power battery as described above.

[0032] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute any one of the above-mentioned methods for monitoring a power battery.

[0033] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0034] In the technical solution of the present invention, if the power battery is charged to the end, and the SOC value of the power battery is greater than the preset SOC value, the voltage change rate of each battery cell with capacity during the charging process is obtained; the standard deviation of each battery module is determined according to the voltage change rate of each battery cell in each battery module; within the preset SOC range, based on the standard deviation of each battery module, if a discrete battery module is detected, the standard deviation of the discrete battery module at different time points in the whole life cycle is obtained within the preset SOC range; the abnormal type of the discrete battery module is determined according to the standard deviation of the discrete battery module at different time points in the whole life cycle. It is realized that at the high end of the SOC, the standard deviation of the module is used to detect the abnormality of the power battery. In this way, whether the power battery is charged by fast charging or slow charging, the consistency of the power battery can be detected, thereby making the use scenarios more abundant. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In addition, similar numbers in the figures are used to represent similar components, among which:

[0036] Figure 1 is a schematic flow chart of main steps of a method for monitoring a power battery according to an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the aggregation results of the standard deviation-SOC curves of multiple battery modules;

[0038] Figure 3 is a schematic flow chart of main steps of a method for monitoring a power battery according to another embodiment of the present invention;

[0039] Figure 4 It is the module_std_5-SOC curve at different time points in the whole life cycle of battery module module_5;

[0040] Figure 5 It is a main structural block diagram of a power battery monitoring device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0042] In the description of the present invention, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as program codes, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program codes, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and the like. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B", and may include only A, only B, or A and B. The singular terms "one" and "the" may also include plural forms.

[0043] For power batteries, most fault diagnoses based on cell inconsistency are simple calculations of voltage, current, and temperature, such as calculating the voltage difference between the cell voltage and the average voltage, and calculating the internal resistance using the pulse point voltage and current. This judgment method is only applicable to situations where the degree of cell failure is already serious. In the early stages of a failure, the absolute values ​​of the voltage and current are not significantly outliers, and these features may not be able to effectively identify them. At this time, it is necessary to use higher-order calculations of external characteristic parameters and trend analysis over a longer time dimension to make early judgments on cell inconsistency anomalies.

[0044] In the related technology, the battery charging and discharging process can be analyzed by the rate of change of the cell's voltage with capacity (i.e., the voltage-capacity differential value dV / dQ) to obtain an early judgment of abnormal cell inconsistency. Specifically, the changes in the peak position and peak area of ​​the dV / dQ curve over time can be analyzed, which corresponds to the voltage platform, that is, the change in the phase change point. For power batteries, since the discharge current is unstable, dV / dQ can only be calculated during the charging process. To accurately locate the peak of the dV / dQ curve, the charging current must be small (i.e., under slow charging conditions), otherwise the feature is difficult to extract.

[0045] However, through the user portrait of power batteries, it can be found that in real life, there are few scenarios for slow charging of power batteries, which is mainly affected by the following factors: first, fast charging is very common and convenient now; second, many slow charging is carried out in underground garages, where the signal is relatively poor and the return signal is affected. In other words, there are limitations in the use of abnormal diagnosis of power batteries based on the peak position and peak area of ​​the dV / dQ curve.

[0046] Therefore, in order to solve the above technical problems, the present invention provides the following technical solutions:

[0047] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a method for monitoring a power battery according to an embodiment of the present invention. The power battery may include a plurality of battery modules, and the battery module includes a plurality of battery cells. Figure 1 As shown, the method for monitoring a power battery in the embodiment of the present invention mainly includes the following steps 101 to 104.

[0048] Step 101: If the power battery is charged to the end and the SOC value of the power battery is greater than a preset SOC value, the voltage change rate of each battery cell with the capacity during the charging process is obtained;

[0049] In a specific implementation process, during the charging process of the power battery, in the later stage of charging, when the SOC value reaches a certain threshold, it indicates that the power battery has entered the end of charging. The threshold of the end of charging in this embodiment can be 80%. When the power battery is charged to the end, if the SOC value of the power battery is greater than the preset SOC value, such as the preset SOC value can be 90%, the voltage change rate of each battery cell with capacity during the charging process can be obtained. Among them, the voltage change rate of the battery cell with capacity is the ratio between the voltage change of the battery cell and the corresponding capacity change under specific charging or discharging conditions. Usually, dV / dQ can be calculated by measuring the change of the voltage and current of the battery or battery cell over time. Specifically, the voltage change rate of the battery cell with capacity can be calculated as follows:

[0050] (1) Obtain the voltage, current and charging time of each battery cell during the charging process;

[0051] (2) Determine the rate of change of the voltage with respect to the capacity based on the voltage, the current and the charging time.

[0052] Specifically, the ratio of voltage to current can be calculated at each time point to obtain dV / dI. dV / dI is filtered to eliminate noise and fluctuations. A time point is selected as a reference point, and the dV / dI values ​​at all time points are compared with the values ​​at the reference point to calculate the relative change, i.e. d(ΔV / ΔI). Integrate d(ΔV / ΔI) over the charging time to obtain the value of dV / dQ.

[0053] Step 102, determining the standard deviation of each battery module according to the rate of change of the voltage of each battery cell in each battery module with the capacity;

[0054] In a specific implementation process, step 102 may be implemented as follows:

[0055] (11) The voltage change rate with capacity of each battery cell is averaged according to SOC to obtain the average voltage change rate with capacity of each battery cell;

[0056] In a specific implementation process, multiple voltage-to-capacity change rates of each battery cell at the same SOC value can be averaged to obtain the average voltage-to-capacity change rate of each battery cell. For example, if the SOC value is 89%, which corresponds to 5 frames of voltage-to-capacity change rates, the average value of these 5 frames of voltage-to-capacity change rates can be taken.

[0057] (12) Determine the standard deviation of each battery module based on the rate of change of the average voltage of each battery cell in each battery module with respect to capacity.

[0058] In a specific implementation process, for the cells in each series-connected battery module of a single power battery, due to their close positions and the same current changes, their consistency is generally high. If the dV / dQ curve of one of the cells is significantly outliers compared with other cells in the battery module, it can be considered that there is a risk of failure. In order to quantify the increase in the dispersion of the dV / dQ curve in the battery module and formulate an abnormal diagnosis strategy, the standard deviation of the battery module can be used as a characteristic value for judgment. In this way, whether the power battery is fast charged (high current charging) or slow charged (low current charging), the required characteristic values ​​can be obtained to facilitate subsequent detection of power battery abnormalities.

[0059] Specifically, after obtaining the average voltage change rate of each battery cell in each battery module with capacity, the standard deviation of each battery cell can be calculated according to the average voltage change rate of each battery cell in each battery module with capacity, so as to determine the standard deviation of each battery module. The process of calculating the standard deviation of the battery module can refer to the existing related technology and will not be repeated here.

[0060] Step 103: Detect whether there is a discrete battery module within a preset SOC range based on the standard deviation of each of the battery modules;

[0061] In a specific implementation process, the threshold of the charging end can be used as the starting value, and the preset SOC value can be used as the ending value to obtain the preset SOC range. For example, the threshold of the charging end is 80%, the preset SOC value can be 90%, and the preset SOC range can be 80%-90%. It should be noted that when setting the SOC range, the threshold of the charging end is the maximum value of the starting value, and the preset SOC value is the minimum value of the ending value, that is, the minimum range of the preset SOC range can be 80%-90%, or a larger SOC range can be set according to actual needs, such as 75%-95%.

[0062] In a specific implementation process, whether there is a discrete battery module can be detected within the preset SOC range based on the standard deviation of each of the battery modules.

[0063] Specifically, based on the standard deviation of each battery module and the preset SOC range, a standard deviation-SOC relationship of a battery module, such as a two-dimensional table, can be obtained, and discrete battery modules can be detected based on the two-dimensional table. Specifically, the mean standard deviation of all battery modules can be obtained according to the standard deviation of each battery module in the two-dimensional table; the second difference between the standard deviation of each battery module and the mean standard deviation of all battery modules is determined; if the second difference is greater than a second preset threshold, the battery module is determined to be a discrete battery module.

[0064] See also Figure 2 , Figure 2 It is a schematic diagram of the standard deviation-SOC curve of multiple battery modules. Figure 2 Take six battery modules as an example, namely battery module 1 to battery module 6. Figure 2 As shown, for the fifth battery module 5, it obviously deviates from other battery modules. The final analysis result shows that the second difference between the standard deviation of the fifth battery module 5 and the mean standard deviation of all battery modules is greater than the second threshold value. Therefore, Figure 2 The fifth battery module 5 is an outlier battery module.

[0065] Step 104: If the existence of the discrete battery module is detected, the standard deviation of the discrete battery module at different time points in the entire life cycle is obtained within the preset SOC range.

[0066] In a specific implementation process, if the existence of the discrete battery module is detected, it can be determined that the discrete battery module is abnormal. In order to further determine the abnormality type of the discrete battery module, the standard deviation of the discrete battery module at different time points in the entire life cycle can be obtained.

[0067] In a specific implementation process, if it is detected that the discrete battery module does not exist, it can be determined that there is no abnormality in the power battery.

[0068] Step 105 : Determine the abnormal type of the discrete battery module according to the standard deviation of the discrete battery module at different time points in the entire life cycle.

[0069] In a specific implementation process, the first difference between the standard deviations of the discrete battery module at two adjacent time points in the full life cycle can be obtained in sequence. In the preset time proportion of the full life cycle, if the first difference is greater than the first threshold, and the first difference gradually increases, it is determined that the standard deviation of the discrete battery module in the full life cycle increases with time; if the first difference is greater than the first threshold, and the change value of the first difference is within the preset range, it is determined that the standard deviation of the discrete battery module in the full life cycle does not increase with time. Among them, the starting time point of the preset time proportion can be the time point when the first difference is greater than the first threshold for the first time, and the ending time point of the preset time proportion can be the current time point.

[0070] In a specific implementation process, if the standard deviation of the discrete battery module at different time points in the entire life cycle shows that it increases with time, the abnormality type of the discrete battery module is determined to be a single-cell abnormality; if the standard deviation of the discrete battery module at different time points in the entire life cycle shows that it does not increase with time, the abnormality type of the discrete battery module is determined to be poor initial consistency of the battery cells.

[0071] The monitoring method of the power battery of this embodiment, if the power battery is charged to the end, and the SOC value of the power battery is greater than the preset SOC value, obtain the rate of change of the voltage of each battery cell with the capacity during the charging process; determine the standard deviation of each battery module according to the rate of change of the voltage of each battery cell with the capacity in each battery module; within the preset SOC range, based on the standard deviation of each battery module, if a discrete battery module is detected, obtain the standard deviation of the discrete battery module at different time points in the whole life cycle within the preset SOC range; determine the abnormal type of the discrete battery module according to the standard deviation of the discrete battery module at different time points in the whole life cycle. It is realized that at the high end of the SOC, the standard deviation of the module is used to detect the abnormality of the power battery, so that no matter whether the power battery is charged by fast charging or slow charging, the consistency of the power battery can be detected, thereby making the use scenarios more abundant.

[0072] See attached Figure 3 , Figure 3 FIG. 1 is a flow chart showing the main steps of a method for monitoring a power battery according to another embodiment of the present invention. Figure 3 As shown, the method for monitoring a power battery in the embodiment of the present invention mainly includes the following steps 301 to 313.

[0073] Step 301: After charging starts, monitor the change of SOC during the charging process;

[0074] Step 302, detect whether the SOC is greater than a preset SOC value, if so, execute step 302, if not, return to step 301;

[0075] Step 303: extracting the voltage, current and SOC of all single cells during the entire charging process;

[0076] Step 304, performing equal SOC filtering on the voltage of the single cell, and calculating the dV / dQ curve of the single cell by ampere-hour integration;

[0077] Step 305, averaging the dV / dQ curves of the single cells according to the SOC to obtain a dV / dQ mean value curve of the single cells;

[0078] Step 306, calculating the standard deviation between each battery cell in each battery module at each SOC point;

[0079] Step 307, drawing a module standard deviation-SOC curve of 80%-end-SOC of different battery modules during the last charge in the power battery;

[0080] Step 308: Is there any battery module with a high module standard deviation-SOC curve? If yes, go to step 309; if no, go to step 313;

[0081] Step 309, drawing a curve of 80%-end-SOC of the battery module over the entire life cycle;

[0082] Step 310: Whether the 80%-end-SOC curve of the battery module has an increasing trend over time during the entire life cycle; if so, execute step 311; if not, execute step 312;

[0083] Step 311, determining that the abnormality type of the battery module is a single-cell abnormality;

[0084] Step 312: Determine that the abnormality type of the battery module is poor initial consistency of the battery cells;

[0085] Step 313: There is no abnormality in the power battery.

[0086] The technical solution of the present invention is described below with specific examples:

[0087] a. Assume that there are 192 cells in power battery A, 16 modules connected in series, and 12 cells in each module;

[0088] b. The last charge of power battery A ended when it reached 93% SOC. The voltage, current, and charging time of 192 cells were extracted and filtered to obtain dV / dQ_1 to dV / dQ_192;

[0089] c. Aggregate and average dV / dQ_1 to dV / dQ_192 according to SOC to obtain dV / dQ_avg_1 to dV / dQ_avg_192;

[0090] d. Take the dV / dQ_avg of each SOC point of the 12 cells in the same series-connected battery module and calculate the standard deviation module_std of the battery module;

[0091] e. In the 80%-93% SOC range, the module_std-SOC curve of the 16 battery modules connected in series of power battery A was drawn, and it was found that the curve of battery module_5 was abnormally high;

[0092] f. Extract the charging process that meets the conditions in the full life cycle charging SOC interval of the battery module module_5 of the power battery A, and draw the module_std_5-SOC curve at different time points in the full life cycle of the battery module module_5;

[0093] g. It is found that the module_std_5-SOC curve of the battery module module_5 has a gradual growth trend in the whole life cycle, that is, the dispersion between the cells in the battery module module_5 gradually increases and the consistency becomes worse;

[0094] Specifically, Figure 4 It is the module_std_5-SOC curve at different time points in the whole life cycle of battery module module_5. Figure 4 As shown, Figure 4 The module_std_5-SOC curve at different time points (20221125, 20201206, 20210705, 20210917, 20220221, 20220223) in the entire life cycle of battery module module_5 increases as time goes by, indicating that the dispersion between battery cells in battery module module_5 gradually increases, the consistency deteriorates, and there is a risk of abnormal single cell in battery module module_5.

[0095] h. Determine that battery module_5 of power battery A has a single-cell abnormality risk.

[0096] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0097] It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0098] Furthermore, the present invention also provides a monitoring device for a power battery.

[0099] See attached Figure 5 , Figure 5 FIG. 1 is a main structural block diagram of a power battery monitoring device according to an embodiment of the present invention. Figure 5 As shown, the monitoring device for the power battery in the embodiment of the present invention may include a processor 51 and a storage device 52 .

[0100] The storage device 52 may be configured to store a program for executing the method for monitoring a power battery of the above method embodiment, and the processor 51 may be configured to execute the program in the storage device 52, which includes but is not limited to executing the program for monitoring a power battery of the above method embodiment. For ease of explanation, only the part related to the embodiment of the present invention is shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The monitoring device of the power battery may be a control device formed by various electronic devices.

[0101] In a specific implementation process, the number of the storage device 52 and the processor 51 can be multiple. The program for executing the power battery monitoring method of the above method embodiment can be divided into multiple subprograms, and each subprogram can be loaded and run by the processor 51 to execute different steps of the power battery monitoring method of the above method embodiment. Specifically, each subprogram can be stored in different storage devices 52, and each processor 51 can be configured to execute the program in one or more storage devices 52 to jointly implement the power battery monitoring method of the above method embodiment, that is, each processor 51 executes different steps of the power battery monitoring method of the above method embodiment to jointly implement the power battery monitoring method of the above method embodiment.

[0102] The above-mentioned multiple processors 51 may be processors deployed on the same device. For example, the above-mentioned device may be a high-performance device composed of multiple processors, and the above-mentioned multiple processors 51 may be processors configured on the high-performance device. In addition, the above-mentioned multiple processors 51 may also be processors deployed on different devices. For example, the above-mentioned device may be a server cluster, and the above-mentioned multiple processors 51 may be processors on different servers in the server cluster.

[0103] Furthermore, the present invention also provides an electric vehicle, which includes the monitoring device for the power battery.

[0104] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the power battery monitoring method of the above method embodiment, and the program can be loaded and run by the processor to implement the above power battery monitoring method. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0105] Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic.

[0106] Those skilled in the art will appreciate that the modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0107] It should be noted that the relevant user personal information that may be involved in the various embodiments of the present application is strictly in accordance with the requirements of laws and regulations, following the principles of legality, legitimacy and necessity, based on the reasonable purposes of business scenarios, to process the personal information that users actively provide during the use of products / services or generated due to the use of products / services, as well as the personal information obtained with the user's authorization.

[0108] The user personal information processed by the applicant will vary depending on the specific product / service scenario, and shall be based on the specific scenario in which the user uses the product / service, which may involve the user's account information, device information, driving information, vehicle information or other related information. The applicant will treat the user's personal information and its processing with a high degree of diligence.

[0109] The Applicant attaches great importance to the security of user personal information and has adopted reasonable and feasible security protection measures that comply with industry standards to protect user information and prevent personal information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0110] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for monitoring a power battery, characterized in that: The power battery includes a plurality of battery modules, and the battery module includes a plurality of battery cells; the method includes: If the power battery is charged to the end and the SOC value of the power battery is greater than the preset SOC value, the voltage change rate of each battery cell with the capacity during the charging process is obtained; Determining the standard deviation of each battery module according to the rate of change of the voltage of each battery cell in each battery module with the capacity; Within a preset SOC range, based on the standard deviation of each of the battery modules, detecting whether there is a discrete battery module; If the existence of the discrete battery module is detected, within the preset SOC range, obtaining the standard deviation of the discrete battery module at different time points in the entire life cycle; The abnormal type of the discrete battery module is determined according to the standard deviation of the discrete battery module at different time points in the entire life cycle.

2. The method for monitoring a power battery according to claim 1, characterized in that: Determining the abnormal type of the discrete battery module according to the standard deviation of the discrete battery module at different time points in the entire life cycle includes: If the standard deviation of the discrete battery module at different time points in the entire life cycle increases with time, it is determined that the abnormality type of the discrete battery module is a single-cell abnormality; If the standard deviation of the discrete battery module at different time points in the entire life cycle does not increase with time, it is determined that the abnormal type of the discrete battery module is poor initial consistency of the battery cells.

3. The method for monitoring a power battery according to claim 2, characterized in that: Also includes: Sequentially obtaining a first difference between standard deviations of the discrete battery module at two adjacent time points in the entire life cycle; In the preset time proportion of the whole life cycle, if the first difference is greater than a first threshold value and the change value of the first difference is outside a preset range, it is determined that the standard deviation of the discrete battery module in the whole life cycle increases with time; If the first difference is greater than a first threshold value, and a change in the first difference is within a preset range, it is determined that the standard deviation of the discrete battery module in the entire life cycle does not increase with time.

4. The method for monitoring a power battery according to claim 1, characterized in that: Obtain the rate of change of voltage and capacity of each battery cell during charging, including: Obtain the voltage, current and charging time of each battery cell during the charging process; The voltage-to-capacity change rate is determined according to the voltage, the current, and the charging time.

5. The method for monitoring a power battery according to claim 1, characterized in that: According to the rate of change of voltage of each battery cell in each battery module with capacity, the standard deviation of each battery module is determined, including: The voltage change rate with capacity of each battery cell is averaged according to SOC to obtain the average voltage change rate with capacity of each battery cell; The standard deviation of each battery module is determined based on the rate of change of the average voltage of each cell in each battery module with capacity.

6. The method for monitoring a power battery according to claim 1, characterized in that: Within a preset SOC range, based on the standard deviation of each of the battery modules, detecting whether there is a discrete battery module includes: According to the standard deviation of each battery module, the mean standard deviation of all battery modules is obtained; Determining a second difference between a standard deviation of each of the battery modules and a mean standard deviation of all the battery modules; If the second difference is greater than a second preset threshold, it is determined that the battery module is a discrete battery module.

7. The method for monitoring a power battery according to any one of claims 1 to 6, characterized in that: Also includes: If it is detected that the discrete battery module does not exist, it is determined that the power battery has no abnormality.

8. A power battery monitoring device, characterized in that: The method comprises a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, and the program codes are suitable for being loaded and run by the processor to execute the power battery monitoring method according to any one of claims 1 to 7.

9. An electric vehicle, characterized in that: A monitoring device for a power battery comprising the device as claimed in claim 8.

10. A computer-readable storage medium, characterized in that: A plurality of program codes are stored, and the program codes are suitable for being loaded and run by a processor to execute the power battery monitoring method according to any one of claims 1 to 7.

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