A battery cluster management method, a warning method, a battery cluster and a storage medium

Abstract

This application proposes a battery cluster management method, a battery cluster warning method, a battery cluster, and a storage medium. The battery cluster management method proposed in this application collects multi-frame characteristic parameter data of all battery cells within a window time, calculates the fluctuation parameters corresponding to each battery cell, then performs normal distribution standardization processing on the fluctuation parameters corresponding to each battery cell to obtain the standard fluctuation parameters corresponding to each battery cell, and finally detects the standard fluctuation parameters corresponding to all battery cells to obtain unqualified battery cells. On the basis of the battery cluster management method, this application also proposes a battery cluster warning method. The warning method is to obtain the standard fluctuation parameters corresponding to each battery cell within multiple consecutive window times, analyze the change trend of the standard fluctuation parameters of each battery cell, and judge the safety of each battery cell according to the change trend, and generate corresponding warning information. The warning information includes: warning type and warning level.

Description

Technical Field

[0001] The present application relates to the technical field of battery clusters, and specifically relates to a battery cluster management method, an early warning method, a battery cluster and a storage medium. Background Art

[0002] With the development of technology and the improvement of people's living standards, energy storage electronic products are becoming more and more popular among consumers. Among energy storage electronic products, there are many high-power-consuming electronic products, such as new energy vehicles. Due to their high power consumption and the need for high charging efficiency, the number of batteries in energy storage power stations is increasing, and the scale is also becoming larger and larger.

[0003] As the scale of energy storage power stations becomes larger and larger, the management difficulty of the battery cells included in the energy storage power stations is also increasing. When there are relatively inconsistent battery cells in the energy storage power station, it will seriously affect the charge and discharge efficiency of the energy storage power station. Therefore, finding relatively inconsistent battery cells in the energy storage power station is the key to the management of the energy storage power station.

[0004] In the prior art, there are various methods for detecting the battery cells of energy storage power stations, such as: voltage / internal resistance / capacity consistency screening, electrochemical impedance spectroscopy (EIS) screening, machine learning and data-driven methods, local outlier factor (LOF) and improved Shannon entropy algorithm (ImEn).

[0005] Among the above-mentioned existing methods, simple measurement methods are convenient and fast, but have low accuracy. Complex discrimination methods are based on a large amount of historical data, or the detection process is complex, time-consuming, and has strict environmental requirements. Summary of the Invention

[0006] To solve the above technical problems, the present application provides a battery cluster management method, an early warning method, a battery cluster and a storage medium. Through the method, the result of detecting inconsistent battery cells in the battery cluster can be made more accurate, and the method does not require a large amount of historical data, and the detection process is also very simple.

[0007] In a first aspect, a battery cluster management method, the management method specifically includes:

[0008] Step 1: Select a window time, and collect N-frame characteristic parameter data of M battery cells within the window time, where M and N are natural numbers.

[0009] Step 2: Based on the N-frame characteristic parameter data of M battery cells within the window time, calculate M fluctuation parameters, where N is a natural number large enough.

[0010] Step 3: Perform normal distribution standardization processing on the M fluctuation parameters to obtain M standard fluctuation parameters.

[0011] Step 4: Perform eligibility detection on the M standard fluctuation parameters, obtain the unqualified standard fluctuation parameters within the window time, mark the battery cells corresponding to the unqualified standard fluctuation parameters as unqualified battery cells, and send the information of the unqualified battery cells to the management platform; and

[0012] Step 5: Select subsequent window times and repeat Steps 1 - 4.

[0013] The method proposed in this application can make the management result of the battery cluster more accurate and time - sequenced by collecting the characteristic parameter data of each battery cell within multiple window times and finding unqualified battery cells based on the differences in the characteristic parameter data, and the implementation process of the method is very simple.

[0014] Further, Step 2 specifically includes:

[0015] Calculating the range of the characteristic parameter data of each battery cell within the window time as the fluctuation parameter, or

[0016] Calculating the standard deviation of the characteristic parameter data of each battery cell within the window time as the fluctuation parameter.

[0017] Through this invention design, the fluctuation range of the characteristic parameter data of the battery cell can be known. When using the range as the fluctuation parameter, it can reflect the inconsistency of internal resistance and capacity. When using the standard deviation as the fluctuation parameter, it can not only reflect the inconsistency of internal resistance and capacity, but also reflect the problems of internal short - circuit, self - discharge rate, and relaxation voltage, etc., but the requirement for computing power also increases accordingly.

[0018] Further, when M is greater than a preset threshold, the M fluctuation parameters follow a normal distribution.

[0019] When the total number of battery cells M is large, the fluctuation parameters of the characteristic parameter data of each battery cell in the sample follow a normal distribution. Combining this with the processing of the characteristic parameter data in Step 3 can better reflect the relative inconsistency of individual battery cells in the battery cluster.

[0020] Further, the eligibility detection includes:

[0021] Drawing a scatter plot of the M standard fluctuation parameters, and regarding the standard fluctuation parameters not within the confidence interval as unqualified standard fluctuation parameters, or

[0022] Setting a standard fluctuation parameter threshold, comparing the absolute values of the M standard fluctuation parameters with the standard fluctuation parameter threshold, and regarding the standard fluctuation parameters with absolute values greater than the standard fluctuation parameter threshold as unqualified standard fluctuation parameters.

[0023] Regarding the qualification test scheme, this application proposes two schemes, but no matter which scheme is adopted, the test standard for the standard fluctuation parameter is dynamic, and changes with the change of the characteristic parameter data of all battery cells within the window time. Through this method design, the test results can better reflect the relative inconsistency within the battery cluster and be more time-series.

[0024] In the second aspect, based on the above battery cluster management method, the present application also proposes a battery cluster early warning method, which specifically includes:

[0025] When the operating conditions of all battery cells in the battery cluster are the same within the window time, the standard fluctuation parameters corresponding to each battery cell within multiple consecutive window times are obtained, the change trend of the standard fluctuation parameters of each battery cell is analyzed, and the safety of each battery cell is judged based on the change trend, and the judgment result is output.

[0026] This application determines the safety of each battery cell by the changing trend of the standard fluctuation parameters within the macro window time, and can promptly inform relevant practitioners of the health status of the battery cluster, greatly improving the health of the battery cluster and reducing labor costs.

[0027] Further, judging the safety of each battery cell according to the change trend is specifically as follows:

[0028] Step 1: Obtain the number of standard fluctuation parameters of each battery cell that fall within each value interval.

[0029] Step 2: Compare the ratio of the number to the total number of standard fluctuation parameters of each battery cell with the ratio threshold, and generate normal state information or warning information as the judgment result according to the comparison result.

[0030] The changing trend of the standard fluctuation parameters of each battery cell over time can be judged by the number of standard fluctuation parameters of each battery cell falling in each numerical interval, and then compared with the proportion threshold, and normal status information or warning information is generated according to the comparison result. Through this method design, relevant practitioners can know in advance which battery cell in the energy storage battery station has an inconsistent trend, and can deal with it in advance to facilitate large-scale operation and maintenance, reduce costs and avoid unnecessary losses.

[0031] Furthermore, the warning information includes: warning type and warning level.

[0032] Through early warning information, relevant practitioners can take targeted measures to deal with inconsistent cells within the battery cluster.

[0033] Furthermore, generating normal status information or warning information according to the comparison result specifically includes:

[0034] If the ratio of the number of standard fluctuation parameters of the battery cell falling within the first numerical range to the total number of standard fluctuation parameters exceeds the ratio threshold, an over-large first warning message is generated.

[0035] If the ratio of the number of standard fluctuation parameters of the battery cell falling within the second numerical range to the total number of standard fluctuation parameters exceeds the ratio threshold, an over-large second warning message is generated.

[0036] If the ratio of the number of standard fluctuation parameters of the battery cell falling within the third numerical range to the total number of standard fluctuation parameters exceeds the ratio threshold, an over-large third warning message is generated.

[0037] If the ratio of the number of standard fluctuation parameters of the battery cell falling within the fourth numerical range to the total number of standard fluctuation parameters exceeds the ratio threshold, an under-small third warning message is generated.

[0038] If the ratio of the number of standard fluctuation parameters of the battery cell falling within the fifth numerical range to the total number of standard fluctuation parameters exceeds the ratio threshold, an under-small second warning message is generated.

[0039] If the ratio of the number of standard fluctuation parameters of the battery cell falling within the sixth numerical range to the total number of standard fluctuation parameters exceeds the ratio threshold, an under-small first warning message is generated.

[0040] In other cases, a normal state message is generated.

[0041] Wherein, the first numerical range > the second numerical range > the third numerical range > the fourth numerical range > the fifth numerical range > the sixth numerical range.

[0042] The present invention designs a variety of warning messages. Different warning messages correspond to different battery cell states and different risk levels. Through this design, relevant practitioners can be reminded to take different actions according to different warning messages.

[0043] In a third aspect, the present application proposes a battery cluster, specifically including:

[0044] Multiple battery cells.

[0045] An acquisition module, configured to acquire the characteristic parameter data of each battery cell within a window time.

[0046] A first data processing module, configured to calculate the fluctuation parameters of each battery cell within the window time.

[0047] A second data processing module, configured to calculate the standard fluctuation parameters of each battery cell within the window time.

[0048] A detection module is used to perform qualification detection on the M standard fluctuation parameters, obtain the unqualified standard fluctuation parameters within the window time, and mark the battery cells corresponding to the standard fluctuation parameters as unqualified battery cells.

[0049] A third data processing module is used to calculate the number of standard fluctuation parameters of each battery cell falling within each numerical interval.

[0050] An early warning generation module is used to compare the ratio of the number of standard fluctuation parameters of each battery cell falling within each numerical interval to the total number of standard fluctuation parameters of each battery cell with the ratio threshold, and generate normal status information or early warning information according to the comparison result.

[0051] Based on the collaborative cooperation among the various modules, the battery cluster proposed in this application can effectively implement the battery cluster management method mentioned in the first aspect.

[0052] In a fourth aspect, based on the same inventive concept, this application also proposes a computer-readable storage medium storing computer-executable instructions, and when the computer-executable instructions are executed by a control processor, the battery cluster management method and the battery cluster early warning method as described are implemented.

[0053] It should be noted that since the computer-readable storage medium in this embodiment and the battery cluster management method and the battery cluster early warning method in the above embodiment are based on the same inventive concept, the corresponding content in the method embodiment also applies to this system embodiment and will not be elaborated here.

[0054] In summary, this application collects the characteristic parameter data of all battery cells within a window time, obtains multiple fluctuation parameters that satisfy the normal distribution, then performs standardization processing on the fluctuation parameters to obtain multiple standard fluctuation parameters, and then detects the multiple standard fluctuation parameters to obtain the unqualified standard fluctuation parameters within the window time, and marks the battery cells corresponding to the standard fluctuation parameters as unqualified battery cells.

[0055] Compared with the prior art, this application has at least the following beneficial effects:

[0056] The method proposed in this application detects all battery cells within a window time, making the detection result have timeliness. The process of this method does not use historical data, only uses the data of different battery cells within the same window time, and the process of this method performs precise processing on the collected data multiple times, such as calculating fluctuation parameters, normal distribution standardizing fluctuation parameters, and the detection process, making the final detection result more accurate, and the simple mathematical principles are used in the process of data processing, so the process of this method is also very simple. This application is applicable to energy storage power stations, especially large-scale energy storage power stations. Description of the Drawings

[0057] Figure 1 It is a flowchart of the battery cluster management method shown in the embodiments of the present application.

[0058] Figure 2 It is a standard fluctuation parameter distribution diagram of a single selected characteristic parameter of a certain cluster of batteries within a certain window time shown in the embodiments of the present application.

[0059] Figure 3 It is a timing diagram of the standard fluctuation parameter distribution of a single selected characteristic parameter of a certain cluster of batteries shown in the embodiments of the present application.

[0060] Figure 4 It is a battery cluster shown in the embodiments of the present application. Detailed Embodiments

[0061] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0062] Embodiment 1:

[0063] As shown in the attached Figure 1 figure, the present application proposes a battery cluster management method, and the management method specifically includes:

[0064] Step 1: Select a window time, and collect N-frame characteristic parameter data of M battery cells within the window time, where M and N are natural numbers.

[0065] Step 2: Based on the N-frame characteristic parameter data of M battery cells within the window time, calculate M fluctuation parameters.

[0066] Step 3: Perform normal distribution standardization processing on the M fluctuation parameters to obtain M standard fluctuation parameters.

[0067] Step 4: Perform a qualification test on the M standard fluctuation parameters, obtain the unqualified standard fluctuation parameters within the window time, mark the battery cells corresponding to the unqualified standard fluctuation parameters as unqualified battery cells, and send the information of the unqualified battery cells to the management platform; and

[0068] Step 5: Select subsequent window times, and repeat Steps 1 - 4.

[0069] The window time is a period of time intercepted from the total time length, and the lengths of each window time are equal. In an embodiment of the present invention, optionally, the window time length is 10S, but is not limited thereto.

[0070] In an embodiment of the present invention, optionally, let the total number of battery cell samples of the battery cluster be M, and there are N frames of data of a selected characteristic parameter A collected by each battery cell within the same window time. The selected characteristic parameter A of the i-th battery monomer at the j-th moment is denoted as: Aij, i ∈ [1, M], j ∈ [1, N].

[0071] In an embodiment of the present invention, optionally, the selected characteristic parameter A may be: one of the measured voltage, measured temperature, or measured current, but is not limited thereto.

[0072] After marking the unqualified battery cells, it is also necessary to send the information of the unqualified battery cells to the management platform. In an embodiment of the present invention, optionally, the information of the unqualified battery cells is sent to the management platform through the cloud. After receiving the information, relevant practitioners repair or replace the unqualified battery cells.

[0073] In an embodiment of the present invention, optionally, step 2 specifically includes:

[0074] Calculating the range of the data of the characteristic parameter A of each battery cell within the window time as the fluctuation parameter, or

[0075] Calculating the standard deviation of the data of the characteristic parameter A of each battery cell within the window time as the fluctuation parameter.

[0076] When using the range as the fluctuation parameter, it can reflect the inconsistency of internal resistance and capacity. When using the standard deviation as the fluctuation parameter, it can not only reflect the inconsistency of internal resistance and capacity, but also reflect the inconsistency problems such as internal short circuit, self-discharge rate, and relaxation voltage.

[0077] When using the range as the fluctuation parameter, the specific steps are as follows:

[0078] Calculating the range of the data of the selected characteristic parameter A of each battery cell within this window time, and denoting it as the fluctuation parameter of the data of the selected characteristic parameter A of battery i within this window time, as shown in the following formula:

[0079] σ Ai =Aij max -Aij min 。

[0080] When using the standard deviation as the fluctuation parameter, the specific steps are as follows:

[0081] Calculate the overall standard deviation of the selected characteristic parameter A data for each battery cell within the window time, denoted as the fluctuation parameter of the selected characteristic parameter A data of battery i within the window time, as shown in the following formula:

[0082]

[0083] Among them, in the above formula, μ Ai is the mean value of the selected characteristic parameter A data within the window time.

[0084] In an embodiment of the present invention, optionally, when M is greater than a preset threshold, the M fluctuation parameters follow a normal distribution.

[0085] In statistics, when the preset threshold is 30, that is, when there are more than 30 fluctuation parameter data, the normal distribution can be satisfied. In an embodiment of the present invention, optionally, the preset threshold is taken as 30 and M is 48, but it is not limited thereto. In the actual application process, generally 240 battery cells are taken as a battery cluster.

[0086] When the total number of battery cells M is large, the fluctuation parameters of the selected characteristic parameter data of each battery cell follow a normal distribution. Standardize it, that is, it satisfies:

[0087]

[0088] Among them, in the above formula, is the variance of the fluctuation parameter, is the mean value of the fluctuation parameter.

[0089] The standardized σ Ai ' obtained through the above calculation, that is: the standard fluctuation parameter of the selected characteristic parameter A data of each battery cell within the window time after standardization within the battery cluster.

[0090] In an embodiment of the present invention, optionally, the qualification detection includes:

[0091] Draw a scatter plot of the M standard fluctuation parameters, and regard the standard fluctuation parameters outside the confidence interval as unqualified standard fluctuation parameters, or

[0092] Set a standard fluctuation parameter threshold, compare the absolute values of the M standard fluctuation parameters with the standard fluctuation parameter threshold, and regard the standard fluctuation parameters with absolute values greater than the standard fluctuation parameter threshold as unqualified standard fluctuation parameters.

[0093] By drawing a simple scatter plot, or presetting the σ Ai ' threshold for judgment, find the relatively inconsistent battery cells within the battery cluster, and the specific problems can be determined by the deviation size. The probability that the common σ Ai ' ∈ (-1, 1) is about 68.268949%, σAi The probability that '∈ (-2, 2) is approximately 95.449974%, σ Ai The probability that '∈ (-3, 3) is approximately 99.730020%,, σ Ai The probability that '∈ (-4, 4) is approximately 99.9936%. The judgment method of the embodiment of the present invention no longer depends on the common selection threshold, but is the result obtained by the relative level of the selected cluster.

[0094] In the embodiment of the present invention, optionally, the standard fluctuation parameter threshold is 2. Taking the voltage parameter as an example, if σ Vi ' < -2, that is, its absolute value is greater than 2 and the value is negative, then it can be preliminarily determined that within the selected window time, the internal resistance of the battery cell is relatively small within the same cluster or the capacity is relatively high within the same cluster; if σ Vi ' > 2, that is, its absolute value is greater than 2 and the value is positive, then it can be preliminarily determined that within the selected window time, the internal resistance of the battery cell is relatively large within the same cluster or the capacity is relatively low within the same cluster.

[0095] In the embodiment of the present invention, optionally, the standard fluctuation parameter threshold is 2. Taking the temperature parameter as an example, if σ Ti ' < -2, that is, its absolute value is greater than 2 and the value is negative, then it can be preliminarily determined that the temperature fluctuation of the battery cell is extremely small within the same cluster and is at a relatively low level within the window time, and the temperature control of this temperature measurement point is very good; if σ Ti ' > 2, that is, its absolute value is greater than 2 and the value is positive, then it can be preliminarily determined that the temperature fluctuation of the battery cell is extremely large within the same cluster and is at a relatively low level within the window time, and the temperature control of this temperature measurement point is not good and is greatly affected by the working conditions.

[0096] In the embodiment of the present invention, optionally, the confidence interval is the normal fluctuation interval of the standard fluctuation parameter, and the confidence interval changes dynamically with the change of all the standard fluctuation parameters within the window time. As shown in the appendix Figure 2 shown, within this window time, the confidence interval is (-2, +2).

[0097] In the embodiment of the present invention, optionally, as shown in the appendix Figure 2 shown, for the standard fluctuation parameter of the characteristic parameter A data of the battery cell cluster (48 single cells) within a certain window time, it can be seen that the standard fluctuation parameters of the characteristic parameter A data of some battery cells exceed ±2 (confidence interval), and even the standard fluctuation parameters of the characteristic parameter A data of individual battery cells exceed ±3 (the battery cell number 28 in the appendix Figure 2 ), so that the battery cells with obvious relative inconsistency of the standard fluctuation parameters of the characteristic parameter A data within the battery cell cluster within this window time can be quickly locked.

[0098] Embodiment 2:

[0099] In a second aspect, based on the above battery cluster management method, the present application also proposes a battery cluster warning method, specifically including:

[0100] When the operating conditions of all the battery cells in the battery cluster are the same within the window time, obtain the standard fluctuation parameters corresponding to each battery cell within multiple consecutive window times, analyze the change trend of the standard fluctuation parameters of each battery cell, and based on the change trend, judge the safety of each battery cell and output the judgment result.

[0101] In an embodiment of the present invention, optionally, a determination threshold TV is set for subsequent warning judgment. Perform numerical interval partitioning statistics on the standard fluctuation parameters of the selected characteristic parameter A of each battery cell within different window times, and calculate the number of standard fluctuation parameters in each numerical interval.

[0102] In an embodiment of the present invention, optionally, the judging the safety of each battery cell according to the change trend is specifically:

[0103] Step 1: Obtain the number of standard fluctuation parameters of each battery cell falling within each numerical interval.

[0104] Step 2: Compare the ratio of the number to the total number of standard fluctuation parameters of each battery cell with the ratio threshold, and generate normal state information or warning information as the judgment result according to the comparison result.

[0105] The numerical partitioning should include: ≥3, ≥2 to <3, ≥1.9 to <2, ≥1.8 to <1.9, ≥ -1.8 to <1.8, > -1.9 to < -1.8, > -2 to ≤ -1.9, > -3 to ≤ -2, ≤ -3, covering all possible ranges. Compare the ratio of the statistical quantity of each numerical interval to the total number with the threshold VT to generate warning information of different levels and different types.

[0106] The change trend of the standard fluctuation parameters of each battery cell over time can be judged by the number of standard fluctuation parameters of each battery cell falling within each numerical interval, and then compared with the ratio threshold, and normal state information or warning information is generated according to the comparison result. Through this method design, relevant practitioners can know in advance which battery cell in the energy storage battery station has an inconsistent trend, and can perform early processing to facilitate large-scale operation and maintenance, reduce costs and avoid unnecessary losses.

[0107] In an embodiment of the present invention, optionally, the warning information includes: warning type and warning level. The warning level can successively include, from high to low in terms of urgency: red, orange, and yellow warnings, etc.; the warning type can include over-large warning and over-small warning.

[0108] Through the warning information, relevant practitioners can conduct targeted processing on the inconsistent battery cells within the battery cluster.

[0109] In an embodiment of the present invention, optionally, generating normal state information or warning information according to the comparison result specifically includes:

[0110] If the proportion of the number of standard fluctuation parameters of the battery cell falling within the first numerical range to the total number of standard fluctuation parameters exceeds the proportion threshold, then generate an over-large first warning information.

[0111] If the proportion of the number of standard fluctuation parameters of the battery cell falling within the second numerical range to the total number of standard fluctuation parameters exceeds the proportion threshold, then generate an over-large second warning information.

[0112] If the proportion of the number of standard fluctuation parameters of the battery cell falling within the third numerical range to the total number of standard fluctuation parameters exceeds the proportion threshold, then generate an over-large third warning information.

[0113] If the proportion of the number of standard fluctuation parameters of the battery cell falling within the fourth numerical range to the total number of standard fluctuation parameters exceeds the proportion threshold, then generate an undersize third warning information.

[0114] If the proportion of the number of standard fluctuation parameters of the battery cell falling within the fifth numerical range to the total number of standard fluctuation parameters exceeds the proportion threshold, then generate an undersize second warning information.

[0115] If the proportion of the number of standard fluctuation parameters of the battery cell falling within the sixth numerical range to the total number of standard fluctuation parameters exceeds the proportion threshold, then generate an undersize first warning information.

[0116] In other cases, generate normal state information.

[0117] Wherein, the first numerical range > the second numerical range > the third numerical range > the fourth numerical range > the fifth numerical range > the sixth numerical range.

[0118] In an embodiment of the present invention, optionally, the first numerical range is [3, +∞), the second numerical range is [2, 3), the third numerical range is [1.9, 2), the fourth numerical range is (-2, -1.9], the fifth numerical range is (-3, -2], and the sixth numerical range is (-∞, -3].

[0119] In an embodiment of the present invention, optionally, the over-large first warning information is over-large red information, the over-large second warning information is over-large orange information, the over-large third warning information is over-large yellow information, the undersize first warning information is undersize red information, the undersize second warning information is undersize orange information, and the undersize third warning information is undersize yellow information.

[0120] For example, if the proportion of the standard fluctuation parameters of the selected characteristic parameters of a certain battery cell in the ≥3 numerical range exceeds VT, an excessive red information is generated and sent to the management platform, so that relevant practitioners can perform early processing, which is beneficial to large-scale operation and maintenance, cost reduction, and avoidance of unnecessary losses.

[0121] In an embodiment of the present invention, optionally, as shown in the appendix Figure 3 As shown, it can be clearly seen that the fluctuation of the standard fluctuation parameter of the characteristic parameter A data of the battery cell numbered 28 in the battery cluster is significantly the largest in the later window time among the same cluster and has exceeded 3. There is also a tendency for the standard fluctuation parameter of the characteristic parameter A data of the battery cell numbered 3 to exceed 2, and the standard fluctuation parameter of the characteristic parameter A data of the battery cell numbered 21 is relatively small. The data in the appendix Figure 3 provides early warning information. After receiving the early warning information, relevant practitioners need to perform quality inspection on the battery cells corresponding to the early warning information and determine whether the battery cells are faulty according to the actual situation.

[0122] Embodiment 3:

[0123] As shown in the appendix Figure 4 As shown, the present application proposes a battery cluster, specifically including:

[0124] Multiple battery cells.

[0125] An acquisition module for acquiring the characteristic parameter data of each battery cell within the window time.

[0126] A first data processing module for calculating the fluctuation parameters of each battery cell within the window time.

[0127] A second data processing module for calculating the standard fluctuation parameters of each battery cell within the window time.

[0128] A detection module for performing a pass / fail detection on the M standard fluctuation parameters, obtaining the unqualified standard fluctuation parameters within the window time, and marking the battery cells corresponding to the standard fluctuation parameters as unqualified battery cells.

[0129] A third data processing module for calculating the number of standard fluctuation parameters of each battery cell falling within each numerical range.

[0130] An early warning generation module for comparing the ratio of the number of standard fluctuation parameters of each battery cell falling within each numerical range to the total number of standard fluctuation parameters of each battery cell with the ratio threshold, and generating normal state information or early warning information according to the comparison result.

[0131] In an embodiment of the present invention, optionally, the acquisition module may be at least one of a current sensor, a voltage sensor, or a temperature sensor, but is not limited thereto.

[0132] In an embodiment of the present invention, optionally, the first data processing module, the second data processing module, and the third data processing module may be a single-chip microcomputer, a computer, or an FPGA storing a mathematical principle algorithm or a statistical algorithm, but not limited thereto.

[0133] In an embodiment of the present invention, optionally, the detection module and the warning generation module may be a single-chip microcomputer, a computer, or an FPGA storing a logic algorithm, but not limited thereto.

[0134] Based on the collaborative cooperation among the various modules, the battery cluster proposed in the present application can effectively implement the battery cluster management method mentioned in Embodiment 1 and the warning method mentioned in Embodiment 2.

[0135] Embodiment 4:

[0136] The present application also proposes a computer-readable storage medium storing computer-executable instructions, characterized in that when the computer-executable instructions are executed by a control processor, the battery cluster management method mentioned in Embodiment 1 and the warning method mentioned in Embodiment 2 are implemented.

[0137] It should be noted that since the computer-readable storage medium in this embodiment and the battery cluster management method in the above embodiment are based on the same inventive concept, the corresponding content in the method embodiment also applies to this system embodiment and will not be elaborated here.

[0138] In summary, the present application collects the characteristic parameter data of all battery cells within a window time, obtains multiple fluctuation parameters that satisfy a normal distribution, then performs normalization processing on the fluctuation parameters to obtain multiple standard fluctuation parameters, and then detects the multiple standard fluctuation parameters to obtain the unqualified standard fluctuation parameters within the window time, and marks the battery cells corresponding to the standard fluctuation parameters as unqualified battery cells.

[0139] The method proposed in the present application detects all battery cells within a window time, making the detection result have timeliness. The process of the method does not use historical data, only uses the data of different battery cells within the same window time, and the process of the method performs precise processing on the collected data multiple times, such as calculating fluctuation parameters, normal distribution standardizing fluctuation parameters, and the detection process, making the final obtained detection result more accurate, and the simple mathematical principles are used in the process of data processing, so the process of the method is also very simple.

[0140] In several embodiments provided by the present application, it can be understood that each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, the program segment, or the part of code includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in an order different from that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved.

[0141] If the described functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing an electronic device to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs and other various media that can store program codes.

[0142] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present application. It should be understood that the above description is only specific embodiments of the present application and is not used to limit the protection scope of the present application. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An early warning method based on a battery cluster management method, characterized in that, The battery cluster includes M battery cells, and the battery cluster management method specifically includes: S1: Select a window time, and collect N frames of characteristic parameter data of the M battery cells within the window time, where M and N are natural numbers; S2: Based on the N frames of characteristic parameter data of the M battery cells within the window time, calculate M fluctuation parameters; S3: Perform normal distribution standardization processing on the M fluctuation parameters to obtain M standard fluctuation parameters; S4: Perform a qualification test on the M standard fluctuation parameters, obtain the unqualified standard fluctuation parameters within the window time, mark the battery cells corresponding to the unqualified standard fluctuation parameters as unqualified battery cells, and send the information of the unqualified battery cells to the management platform; and S5: Select subsequent window times, and repeat steps S1 - S4; Among them, the qualification test includes: Draw a scatter image of the M standard fluctuation parameters, regard the standard fluctuation parameters outside the confidence interval as unqualified standard fluctuation parameters, or set a standard fluctuation parameter threshold, compare the absolute values of the M standard fluctuation parameters with the standard fluctuation parameter threshold, and regard the standard fluctuation parameters with absolute values greater than the standard fluctuation parameter threshold as unqualified standard fluctuation parameters; The step S2 specifically includes: Calculate the range of the characteristic parameter data of each battery cell within the window time as the fluctuation parameter, or calculate the standard deviation of the characteristic parameter data of each battery cell within the window time as the fluctuation parameter; The early warning method specifically includes: When the working conditions of all battery cells in the battery cluster are the same within each window time, obtain the standard fluctuation parameters corresponding to each battery cell within multiple consecutive window times, analyze the change trend of the standard fluctuation parameters of each battery cell, and judge the safety of each battery cell according to the change trend, and output a judgment result; Judging the safety of each battery cell according to the change trend is specifically: D1: Obtain the number of standard fluctuation parameters of each battery cell falling within each numerical interval; D2: Compare the ratio of the number to the total number of standard fluctuation parameters of each battery cell with a ratio threshold, and generate normal state information or early warning information as the judgment result according to the comparison result.

2. The warning method according to claim 1, wherein When M is greater than a preset threshold, the M fluctuation parameters follow a normal distribution.

3. The early warning method according to claim 1, wherein The early warning information includes: early warning type and early warning level.

4. The warning method according to claim 3, wherein Generating normal state information or early warning information according to the comparison result specifically includes: If the ratio of the number of standard fluctuation parameters of a battery cell falling within the first numerical interval to the total number of standard fluctuation parameters exceeds the ratio threshold, generate an excessive first early warning information; If the ratio of the number of standard fluctuation parameters of a battery cell falling within the second numerical interval to the total number of standard fluctuation parameters exceeds the ratio threshold, generate an excessive second early warning information; If the ratio of the number of standard fluctuation parameters of a battery cell falling within the third numerical interval to the total number of standard fluctuation parameters exceeds the ratio threshold, generate an excessive third early warning information; If the ratio of the number of standard fluctuation parameters of a battery cell falling within the fourth numerical interval to the total number of standard fluctuation parameters exceeds the ratio threshold, generate a too small third early warning information; If the ratio of the number of standard fluctuation parameters of the battery cell falling within the fifth numerical range to the total number of standard fluctuation parameters exceeds the ratio threshold, a second warning message for being too small is generated; If the ratio of the number of standard fluctuation parameters of the battery cell falling within the sixth numerical range to the total number of standard fluctuation parameters exceeds the ratio threshold, a first warning message for being too small is generated; In other cases, a normal status message is generated; Among them, the first numerical range > the second numerical range > the third numerical range > the fourth numerical range > the fifth numerical range > the sixth numerical range.

5. A battery cluster, characterized in that, The battery cluster adopts the warning method as described in any one of claims 1-4, and the battery cluster includes: Multiple battery cells; An acquisition module for acquiring the characteristic parameter data of each battery cell within the window time; A first data processing module for calculating the fluctuation parameters of each battery cell within the window time; A second data processing module for calculating the standard fluctuation parameters of each battery cell within the window time; A detection module for performing a qualification detection on the standard fluctuation parameters, obtaining the unqualified standard fluctuation parameters within the window time, and marking the battery cells corresponding to the standard fluctuation parameters as unqualified battery cells; A third data processing module for calculating the number of standard fluctuation parameters of each battery cell falling within each numerical range; A warning generation module for comparing the ratio of the number of standard fluctuation parameters of each battery cell falling within each numerical range to the total number of standard fluctuation parameters of each battery cell with the ratio threshold, and generating a normal status message or a warning message according to the comparison result.

6. A computer-readable storage medium storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed by the control processor, the warning method based on the battery cluster management method as described in any one of claims 1-4 is implemented.

Images