Battery fault detection method, device, equipment and storage medium

Abstract

The present application discloses a battery fault detection method, apparatus, device, and storage medium, relating to the field of battery technology. The method comprises the following steps: collecting historical cell temperature data, historical ambient temperature data, and the average current ambient temperature of a battery to be detected; determining a cell temperature difference sequence based on the maximum cell temperature and the minimum cell temperature at each time point in the historical cell temperature data; determining an ambient temperature change based on the historical ambient temperature average determined from the historical ambient temperature data and the average current ambient temperature; dividing the cell temperature difference sequence into cell temperature difference groups; determining the average within each cell temperature difference group to obtain a group average temperature sequence; performing linear fitting on the group average temperature sequence to obtain a temperature trend slope; determining a target fault determination threshold based on the ambient temperature change and a correction coefficient; and determining whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold. This method can improve the accuracy of battery detection.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a battery fault detection method, device, equipment and storage medium. Background Art

[0002] With the rapid development of energy storage technology, energy storage batteries have been widely used in fields such as renewable energy, transportation, and power dispatch. The health of energy storage batteries has a crucial impact on their performance and lifespan. Therefore, how to detect battery failures in real time and implement timely repair or replacement measures has become a key issue in ensuring the stable operation of battery systems.

[0003] Currently, common battery fault detection methods primarily rely on monitoring parameters such as temperature and charge / discharge current. However, these methods often have limitations, particularly in complex operating environments or when the battery itself is relatively minorly damaged, which can lead to inaccurate or delayed fault detection. Furthermore, existing technologies generally overlook the direct impact of ambient temperature on the trend of battery temperature differences: as ambient temperature rises, the heat dissipation efficiency of the energy storage cabinet decreases, causing the temperature difference between the battery cells to naturally increase, which can lead to misjudgment.

[0004] As can be seen from the above, how to improve the accuracy of battery fault detection during the battery fault detection process is a problem that needs to be solved urgently. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a battery fault detection method, device, equipment and storage medium, which can improve the accuracy of battery fault detection during the battery fault detection process, thereby improving the safety of the production process. The specific scheme is as follows:

[0006] In a first aspect, the present application provides a battery fault detection method, comprising:

[0007] Collect historical cell temperature data, historical ambient temperature data, and an average value of the current ambient temperature of the battery to be tested, and determine a cell temperature difference value sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data;

[0008] Determining a historical ambient temperature average based on the historical ambient temperature data, determining an ambient temperature change based on the historical ambient temperature average and the current ambient temperature average, then dividing the battery cell temperature difference value sequence according to a preset group length to obtain battery cell temperature difference value groups, and determining an intra-group average value corresponding to each battery cell temperature difference value group to obtain a group average temperature sequence;

[0009] A linear fit is performed on the group average temperature series to obtain a temperature trend slope corresponding to the battery cell temperature difference, and the original fault judgment threshold is adjusted using the ambient temperature change and the correction coefficient to obtain a target fault judgment threshold, so as to determine whether the battery to be tested has a fault based on the temperature trend slope and the target fault judgment threshold.

[0010] Optionally, collecting historical cell temperature data, historical ambient temperature data, and an average value of the current ambient temperature of the battery to be tested, and determining a cell temperature difference value sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data, includes:

[0011] Determine a battery to be tested based on preset requirements, obtain historical cell temperature data, historical ambient temperature data, and an average value of the current ambient temperature corresponding to the battery to be tested, and determine a target time point based on the preset requirements;

[0012] Determining, based on the target time point, battery cell temperature data at each time point corresponding to the target time point in the historical battery cell temperature data, and setting the battery cell temperature data with the largest temperature value among the battery cell temperature data at each time point as the maximum battery cell temperature, and then setting the battery cell temperature data with the smallest temperature value among the battery cell temperature data at each time point as the minimum battery cell temperature;

[0013] A cell temperature difference value relative to the target time point is determined based on the difference between the maximum cell temperature and the minimum cell temperature, and a cell temperature difference value sequence is determined based on each of the cell temperature difference values.

[0014] Optionally, determining a historical ambient temperature average based on the historical ambient temperature data, determining an ambient temperature change based on the historical ambient temperature average and the current ambient temperature average, and then dividing the battery cell temperature difference value sequence according to a preset group length to obtain battery cell temperature difference value groups, and determining an intra-group average value corresponding to each battery cell temperature difference value group to obtain a group average temperature sequence, includes:

[0015] Counting the number of data of each of the historical ambient temperature data to determine a historical ambient temperature average based on each of the historical ambient temperature data and the number of data, and determining an ambient temperature change based on a difference between the historical ambient temperature average and the current ambient temperature average;

[0016] The battery cell temperature difference value sequence is divided according to the chronological order and the preset group length to obtain each battery cell temperature difference value group, and the group average value corresponding to each battery cell temperature difference value group is determined, and each group average value is set as a new historical ambient temperature average value, so as to construct a group average temperature sequence based on the chronological order and each historical ambient temperature average value.

[0017] Optionally, after determining the intra-group average values ​​corresponding to the battery cell temperature difference groups and obtaining the group average temperature sequence, the method further includes:

[0018] Determine the average value to be calculated and the standard deviation determination result corresponding to each of the group average values, and determine the result to be compared based on each of the group average values ​​and the corresponding average value to be calculated;

[0019] It is determined in turn whether each of the results to be compared is greater than the corresponding standard deviation determination result. If each of the results to be compared is greater than the corresponding standard deviation determination result, the cell temperature difference value group corresponding to the result to be compared is deleted from the group average temperature sequence to obtain a new group average temperature sequence.

[0020] Optionally, performing linear fitting on the group average temperature series to obtain a temperature trend slope corresponding to the cell temperature difference, and adjusting an original fault determination threshold using the ambient temperature change and a correction coefficient to obtain a target fault determination threshold, so as to determine whether the battery to be tested has a fault based on the temperature trend slope and the target fault determination threshold, includes:

[0021] Performing a linear fit on the group average temperature series based on a time index using a preset fitting formula to obtain a temperature trend slope corresponding to the battery cell temperature difference, and determining a current heat dissipation efficiency, thereby determining a correction coefficient using the current heat dissipation efficiency; wherein a value range corresponding to the correction coefficient satisfies a preset value range condition;

[0022] Using a preset function and based on the correction coefficient, the original fault determination threshold is adjusted to obtain a target fault determination threshold, and then determining whether the temperature trend slope is greater than the target fault determination threshold;

[0023] If the temperature trend slope is greater than the target fault judgment threshold, the operating state corresponding to the battery to be detected is determined to be a fault state; if the temperature trend slope is not greater than the target fault judgment threshold, the operating state corresponding to the battery to be detected is determined to be a normal operating state.

[0024] In a second aspect, the present application provides a battery fault detection device, comprising:

[0025] a temperature difference value sequence determination module, configured to collect historical cell temperature data, historical ambient temperature data, and an average value of the current ambient temperature of the battery to be tested, and determine a cell temperature difference value sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data;

[0026] an average temperature sequence determination module, configured to determine a historical ambient temperature average based on the historical ambient temperature data, determine an ambient temperature change based on the historical ambient temperature average and the current ambient temperature average, then divide the battery cell temperature difference sequence according to a preset group length to obtain battery cell temperature difference groups, and determine an intra-group average corresponding to each battery cell temperature difference group to obtain a group average temperature sequence;

[0027] A fault judgment threshold determination module is used to perform linear fitting on the group average temperature series to obtain a temperature trend slope corresponding to the battery cell temperature difference, and use the ambient temperature change and the correction coefficient to adjust the original fault judgment threshold to obtain a target fault judgment threshold, so as to determine whether the battery to be tested has a fault based on the temperature trend slope and the target fault judgment threshold.

[0028] Optionally, the average temperature sequence determination module includes:

[0029] an ambient temperature change determination unit, configured to count the number of data of each of the historical ambient temperature data, determine a historical ambient temperature average based on each of the historical ambient temperature data and the number of data, and determine an ambient temperature change based on a difference between the historical ambient temperature average and the current ambient temperature average;

[0030] The average temperature sequence determination subunit is used to divide the battery cell temperature difference value sequence according to the chronological order and the preset grouping length to obtain each battery cell temperature difference value group, and determine the group average value corresponding to each battery cell temperature difference value group, and set each group average value as the new historical ambient temperature average, so as to construct a group average temperature sequence based on the chronological order and each historical ambient temperature average.

[0031] Optionally, the fault judgment threshold determination module includes:

[0032] a correction coefficient determination unit, configured to perform a linear fit on the group average temperature series using a preset fitting formula and based on a time index to obtain a temperature trend slope corresponding to the cell temperature difference, and determine a current heat dissipation efficiency, thereby determining a correction coefficient using the current heat dissipation efficiency; wherein a value range corresponding to the correction coefficient satisfies a preset value range condition;

[0033] a temperature trend slope judgment unit, configured to adjust an original fault judgment threshold value using a preset function and based on the correction coefficient to obtain a target fault judgment threshold value, and then judge whether the temperature trend slope is greater than the target fault judgment threshold value;

[0034] An operating status determination unit is configured to determine that the operating status corresponding to the battery to be detected is a fault status if the temperature trend slope is greater than the target fault judgment threshold, and to determine that the operating status corresponding to the battery to be detected is a normal operating status if the temperature trend slope is not greater than the target fault judgment threshold.

[0035] In a third aspect, the present application provides an electronic device, comprising:

[0036] Memory, used to store computer programs;

[0037] The processor is configured to execute the computer program to implement the aforementioned battery failure detection method.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program implements the aforementioned battery fault detection method when executed by a processor.

[0039] As can be seen from the above, before performing battery fault detection, the present application needs to collect historical cell temperature data, historical ambient temperature data and the current ambient temperature average of the battery to be detected, and determine the cell temperature difference value sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data; determine the historical ambient temperature average based on the historical ambient temperature data, and determine the ambient temperature change based on the historical ambient temperature average and the current ambient temperature average, and then divide the cell temperature difference value sequence according to the preset group length to obtain each cell temperature difference value group, and determine the group average value corresponding to each cell temperature difference value group to obtain the group average temperature sequence; perform linear fitting on the group average temperature sequence to obtain the temperature trend slope corresponding to the cell temperature difference, and use the ambient temperature change and the correction coefficient to adjust the original fault judgment threshold to obtain the target fault judgment threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault judgment threshold.

[0040] It can be seen that the present application first needs to collect the historical cell temperature data, historical ambient temperature data and the current ambient temperature average of the battery to be tested, and determine the cell temperature difference sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data; then, determine the historical ambient temperature average based on the historical ambient temperature data, and determine the ambient temperature change based on the historical ambient temperature average and the current ambient temperature average, and then divide the cell temperature difference sequence according to the preset group length to obtain each cell temperature difference group, and determine the group average value corresponding to each cell temperature difference group to obtain the group average temperature sequence; finally, perform linear fitting on the group average temperature sequence to obtain the temperature trend slope corresponding to the cell temperature difference, and use the ambient temperature change and the correction coefficient to adjust the original fault judgment threshold to obtain the target fault judgment threshold, so as to determine whether the battery to be tested has a fault based on the temperature trend slope and the target fault judgment threshold. In this way, the accuracy of battery fault detection is improved during battery fault detection, thereby improving the safety of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0042] Figure 1 This is a flow chart of a battery fault detection method disclosed in this application;

[0043] Figure 2 This is a structural schematic diagram of a battery fault detection device disclosed in this application;

[0044] Figure 3 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] With the rapid development of energy storage technology, energy storage batteries have been widely used in the fields of renewable energy, transportation, and power dispatching. At present, common battery fault detection methods mainly rely on the monitoring of parameters such as temperature and charge and discharge current, but these methods often have certain limitations, especially when the battery working environment is complex or the battery itself is slightly damaged, which may lead to inaccurate or delayed fault detection. In addition, the existing technology generally ignores the direct impact of ambient temperature on the trend of battery temperature difference changes: when the ambient temperature rises, the heat dissipation efficiency of the energy storage cabinet decreases, causing the temperature difference of the battery cell to naturally increase, which may cause misjudgment. To this end, the present application provides a battery fault detection method that can improve the accuracy of battery fault detection, thereby improving the safety of the production process.

[0047] See also Figure 1 As shown, an embodiment of the present invention discloses a battery fault detection method, including:

[0048] Step S11 , collecting historical cell temperature data, historical ambient temperature data and the average value of the current ambient temperature of the battery to be tested, and determining a cell temperature difference sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data.

[0049] In this embodiment, during the process of testing the battery, the application needs to collect and pre-process the battery data. In the process of collecting data, the embodiment of the application needs to collect the historical battery cell temperature data corresponding to the battery to be tested. and ambient temperature data The average ambient temperature in recent time and the maximum cell temperature at each time point and minimum cell temperature The difference is defined as the cell temperature difference , and the definition of the cell temperature difference is as follows:

[0050] .

[0051] Specifically, historical cell temperature data, historical ambient temperature data and the average current ambient temperature of the battery to be tested are collected, and a cell temperature difference sequence is determined based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data. This may include: determining the battery to be tested based on preset requirements to obtain the historical cell temperature data, historical ambient temperature data and the average current ambient temperature corresponding to the battery to be tested, and determining the target time point based on the preset requirements; determining the cell temperature data of each time point corresponding to the target time point in the historical cell temperature data based on the target time point, and setting the cell temperature data with the largest temperature value in the cell temperature data at each time point as the maximum cell temperature, and then setting the cell temperature data with the smallest temperature value in the cell temperature data at each time point as the minimum cell temperature; determining the cell temperature difference from the target time point based on the difference between the maximum cell temperature and the minimum cell temperature, and determining a cell temperature difference sequence based on each cell temperature difference value.

[0052] Step S12: determining the historical ambient temperature mean based on the historical ambient temperature data, determining the ambient temperature change based on the historical ambient temperature mean and the current ambient temperature mean, and then dividing the battery cell temperature difference sequence according to the preset grouping length to obtain each battery cell temperature difference group, and determining the group average value corresponding to each battery cell temperature difference group to obtain a group average temperature sequence.

[0053] In this embodiment, it is necessary to determine the ambient temperature change , that is, based on the historical ambient temperature average and the current ambient temperature average Determine the ambient temperature change ΔTenv, and the expression is as follows:

[0054] ;

[0055] Subsequently, the embodiment of the present application needs to follow the set group size, such as , the temperature difference of the battery cell Group and determine the average temperature for each group , to use the average temperature instead of the group average temperature to reduce the temperature fluctuation problem.

[0056] Specifically, the historical ambient temperature mean is determined based on the historical ambient temperature data, and the ambient temperature change is determined based on the historical ambient temperature mean and the current ambient temperature mean, and then the battery cell temperature difference value sequence is divided according to the preset grouping length to obtain each battery cell temperature difference value group, and the group average value corresponding to each battery cell temperature difference value group is determined to obtain the group average temperature sequence, which may include: counting the number of data of each historical ambient temperature data to determine the historical ambient temperature mean based on each historical ambient temperature data and the number of data, and determining the ambient temperature change based on the difference between the historical ambient temperature mean and the current ambient temperature mean; dividing the battery cell temperature difference value sequence according to the chronological order and the preset grouping length to obtain each battery cell temperature difference value group, and determining the group average value corresponding to each battery cell temperature difference value group, and setting the group average value as the new historical ambient temperature mean, so as to construct a group average temperature sequence based on the chronological order and each historical ambient temperature mean.

[0057] Then, in a specific embodiment, after obtaining the group average temperature sequence including the average values ​​within the group corresponding to each battery cell temperature difference value group, the embodiment of the present application needs to determine the average temperature of each group. The corresponding average and standard deviation , and in Specifically, after determining the intra-group average value corresponding to each battery cell temperature difference value group and obtaining the group average temperature sequence, the method may further include: determining the to-be-calculated average value and the standard deviation determination result corresponding to each intra-group average value, and determining the to-be-calculated result based on each intra-group average value and the corresponding to-be-calculated average value; sequentially judging whether each to-be-compared result is greater than the corresponding standard deviation determination result; if each to-be-compared result is greater than the corresponding standard deviation determination result, deleting the battery cell temperature difference value group corresponding to the to-be-compared result from the group average temperature sequence to obtain a new group average temperature sequence.

[0058] Step S13: Perform linear fitting on the group average temperature sequence to obtain the temperature trend slope corresponding to the battery cell temperature difference, and use the ambient temperature change and the correction coefficient to adjust the original fault judgment threshold to obtain the target fault judgment threshold, so as to determine whether the battery to be tested has a fault based on the temperature trend slope and the target fault judgment threshold.

[0059] In this embodiment, after obtaining the group average temperature sequence, a linear fitting operation needs to be performed on the group average temperature sequence. Perform linear fitting and obtain the fitting formula: .in, is the time index, is the slope, and the unit is ).

[0060] Then, in a specific embodiment, the embodiment of the present application needs to be based on the amount of change in ambient temperature. and correction factor (obtained by experimental calibration), the original threshold is adjusted using a preset formula Perform dynamic adjustment operations, and the expression of the preset formula is as follows:

[0061] ;

[0062] in, The value range is (calibrated based on experimental data on heat dissipation efficiency reduction), and The state of the battery to be detected is determined to be a fault state.

[0063] Specifically, linear fitting is performed on the group average temperature sequence to obtain the temperature trend slope corresponding to the battery cell temperature difference, and the ambient temperature change and the correction coefficient are used to adjust the original fault judgment threshold to obtain the target fault judgment threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault judgment threshold. It can include: using a preset fitting formula and based on the time index to linearly fit the group average temperature sequence to obtain the temperature trend slope corresponding to the battery cell temperature difference, and determining the current heat dissipation efficiency, so as to determine the correction coefficient using the current heat dissipation efficiency; the value range corresponding to the correction coefficient meets the preset value range condition; using a preset function and based on the correction coefficient to adjust the original fault judgment threshold to obtain the target fault judgment threshold, and then judging whether the temperature trend slope is greater than the target fault judgment threshold; the preset function is a nonlinear correction function; if the temperature trend slope is greater than the target fault judgment threshold, then the operating state corresponding to the battery to be detected is judged to be a fault state, and if the temperature trend slope is not greater than the target fault judgment threshold, then the operating state corresponding to the battery to be detected is judged to be a normal operating state.

[0064] As can be seen from the above, the embodiment of the present application first needs to collect the historical cell temperature data, historical ambient temperature data and the current ambient temperature average of the battery to be tested, and determine the cell temperature difference value sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data; then, determine the historical ambient temperature average based on the historical ambient temperature data, and determine the ambient temperature change based on the historical ambient temperature average and the current ambient temperature average. Then, divide the cell temperature difference value sequence according to the preset group length to obtain each cell temperature difference value group, and determine the group average value corresponding to each cell temperature difference value group to obtain the group average temperature sequence; finally, perform linear fitting on the group average temperature sequence to obtain the temperature trend slope corresponding to the cell temperature difference, and use the ambient temperature change and the correction coefficient to adjust the original fault judgment threshold to obtain the target fault judgment threshold, so as to determine whether the battery to be tested has a fault based on the temperature trend slope and the target fault judgment threshold. In this way, the accuracy of battery fault detection is improved during battery fault detection, thereby improving the safety of the production process.

[0065] Accordingly, see Figure 2 As shown, the present application also provides a battery fault detection device, comprising:

[0066] A temperature difference value sequence determination module 11 is configured to collect historical cell temperature data, historical ambient temperature data, and an average value of the current ambient temperature of the battery to be tested, and determine a cell temperature difference value sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data;

[0067] an average temperature sequence determining module 12, configured to determine a historical ambient temperature average based on the historical ambient temperature data, determine an ambient temperature change based on the historical ambient temperature average and the current ambient temperature average, divide the battery cell temperature difference sequence according to a preset group length to obtain battery cell temperature difference groups, and determine an intra-group average corresponding to each battery cell temperature difference group to obtain a group average temperature sequence;

[0068] The fault judgment threshold determination module 13 is used to perform linear fitting on the group average temperature sequence to obtain the temperature trend slope corresponding to the battery cell temperature difference, and use the ambient temperature change and the correction coefficient to adjust the original fault judgment threshold to obtain the target fault judgment threshold, so as to determine whether the battery to be tested has a fault based on the temperature trend slope and the target fault judgment threshold.

[0069] As can be seen from the above, before performing battery fault detection, the embodiment of the present application first needs to collect the historical cell temperature data, historical ambient temperature data and the current ambient temperature average of the battery to be detected, and determine the cell temperature difference value sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data; then, determine the historical ambient temperature average based on the historical ambient temperature data, and determine the ambient temperature change based on the historical ambient temperature average and the current ambient temperature average. Then, divide the cell temperature difference value sequence according to the preset group length to obtain each cell temperature difference value group, and determine the group average value corresponding to each cell temperature difference value group to obtain the group average temperature sequence; finally, perform linear fitting on the group average temperature sequence to obtain the temperature trend slope corresponding to the cell temperature difference, and use the ambient temperature change and the correction coefficient to adjust the original fault judgment threshold to obtain the target fault judgment threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault judgment threshold. In this way, the accuracy of battery fault detection is improved during the battery fault detection process, thereby improving the safety of the production process.

[0070] In some specific implementations, the temperature difference value sequence determination module 11 may specifically include:

[0071] a time point determination unit, configured to determine a battery to be detected based on preset requirements, obtain historical cell temperature data, historical ambient temperature data, and an average value of the current ambient temperature corresponding to the battery to be detected, and determine a target time point based on the preset requirements;

[0072] a cell temperature data determining unit, configured to determine, based on the target time point, cell temperature data at each time point corresponding to the target time point in the historical cell temperature data, and set the cell temperature data having the largest temperature value among the cell temperature data at each time point as the maximum cell temperature, and then set the cell temperature data having the smallest temperature value among the cell temperature data at each time point as the minimum cell temperature;

[0073] The temperature difference sequence determining subunit is used to determine the cell temperature difference from the target time point based on the difference between the maximum cell temperature and the minimum cell temperature, and to determine a cell temperature difference sequence based on each of the cell temperature differences.

[0074] In some specific implementations, the average temperature sequence determination module 12 may specifically include:

[0075] an ambient temperature change determination unit, configured to count the number of data of each of the historical ambient temperature data, determine a historical ambient temperature average based on each of the historical ambient temperature data and the number of data, and determine an ambient temperature change based on a difference between the historical ambient temperature average and the current ambient temperature average;

[0076] The average temperature sequence determination subunit is used to divide the battery cell temperature difference value sequence according to the chronological order and the preset grouping length to obtain each battery cell temperature difference value group, and determine the group average value corresponding to each battery cell temperature difference value group, and set each group average value as the new historical ambient temperature average, so as to construct a group average temperature sequence based on the chronological order and each historical ambient temperature average.

[0077] In some specific embodiments, the battery fault detection device may further include:

[0078] a unit for determining a result to be compared, configured to determine the average value to be calculated and the standard deviation determination result corresponding to each of the intra-group average values, and determine the result to be compared based on each of the intra-group average values ​​and the corresponding average value to be calculated;

[0079] The comparison result judgment unit is used to judge in turn whether each of the comparison results is greater than the corresponding standard deviation determination result. If each of the comparison results is greater than the corresponding standard deviation determination result, the battery cell temperature difference value group corresponding to the comparison result is deleted from the group average temperature sequence to obtain a new group average temperature sequence.

[0080] In some specific implementations, the fault judgment threshold determination module 13 may specifically include:

[0081] a correction coefficient determination unit, configured to perform a linear fit on the group average temperature series using a preset fitting formula and based on a time index to obtain a temperature trend slope corresponding to the cell temperature difference, and determine a current heat dissipation efficiency, thereby determining a correction coefficient using the current heat dissipation efficiency; wherein a value range corresponding to the correction coefficient satisfies a preset value range condition;

[0082] a temperature trend slope judgment unit, configured to adjust an original fault judgment threshold value using a preset function and based on the correction coefficient to obtain a target fault judgment threshold value, and then judge whether the temperature trend slope is greater than the target fault judgment threshold value;

[0083] An operating status determination unit is configured to determine that the operating status corresponding to the battery to be detected is a fault status if the temperature trend slope is greater than the target fault judgment threshold, and to determine that the operating status corresponding to the battery to be detected is a normal operating status if the temperature trend slope is not greater than the target fault judgment threshold.

[0084] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 3This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the battery fault detection method disclosed in any of the aforementioned embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0085] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0086] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0087] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, and can be Windows Server, NetWare, Unix, Linux, etc. In addition to including a computer program capable of implementing the battery fault detection method disclosed in any of the aforementioned embodiments and executed by the electronic device 20, the computer program 222 can further include a computer program capable of implementing other specific tasks.

[0088] Furthermore, this application discloses a computer-readable storage medium for storing a computer program. When executed by a processor, the computer program implements the aforementioned battery fault detection method. The specific steps of this method can be found in the corresponding contents disclosed in the aforementioned embodiments and will not be further described here.

[0089] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0090] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0091] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0092] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0093] The above is a detailed introduction to the technical solution provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A battery fault detection method, characterized in that: include: Collect historical cell temperature data, historical ambient temperature data, and an average value of the current ambient temperature of the battery to be tested, and determine a cell temperature difference value sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data; Determining a historical ambient temperature average based on the historical ambient temperature data, determining an ambient temperature change based on the historical ambient temperature average and the current ambient temperature average, then dividing the battery cell temperature difference value sequence according to a preset group length to obtain battery cell temperature difference value groups, and determining an intra-group average value corresponding to each battery cell temperature difference value group to obtain a group average temperature sequence; Performing a linear fit on the group average temperature series to obtain a temperature trend slope corresponding to the battery cell temperature difference, and adjusting an original fault determination threshold using the ambient temperature change and a correction coefficient to obtain a target fault determination threshold, so as to determine whether the battery to be tested has a fault based on the temperature trend slope and the target fault determination threshold; Specifically, a preset fitting formula is used to perform a linear fit on the group average temperature series based on a time index to obtain a temperature trend slope corresponding to the battery cell temperature difference, and a current heat dissipation efficiency is determined to determine a correction coefficient using the current heat dissipation efficiency; The value range corresponding to the correction coefficient meets the preset value range condition; the original fault judgment threshold is adjusted using a preset function and based on the correction coefficient to obtain a target fault judgment threshold; The expression of the preset function is as follows: ； in, is the correction coefficient, is the ambient temperature change, is the original fault judgment threshold, is the target fault judgment threshold.

2. The battery failure detection method according to claim 1, characterized in that: The collecting of historical cell temperature data, historical ambient temperature data, and an average value of the current ambient temperature of the battery to be tested, and determining a cell temperature difference value sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data, includes: Determine a battery to be tested based on preset requirements, obtain historical cell temperature data, historical ambient temperature data, and an average value of the current ambient temperature corresponding to the battery to be tested, and determine a target time point based on the preset requirements; Determining, based on the target time point, battery cell temperature data at each time point corresponding to the target time point in the historical battery cell temperature data, and setting the battery cell temperature data with the largest temperature value among the battery cell temperature data at each time point as the maximum battery cell temperature, and then setting the battery cell temperature data with the smallest temperature value among the battery cell temperature data at each time point as the minimum battery cell temperature; A cell temperature difference value relative to the target time point is determined based on the difference between the maximum cell temperature and the minimum cell temperature, and a cell temperature difference value sequence is determined based on each of the cell temperature difference values.

3. The battery failure detection method according to claim 1, wherein: The method further comprises: determining a historical ambient temperature average based on the historical ambient temperature data, determining an ambient temperature change based on the historical ambient temperature average and the current ambient temperature average, dividing the battery cell temperature difference value sequence according to a preset group length to obtain battery cell temperature difference value groups, and determining an intra-group average value corresponding to each battery cell temperature difference value group to obtain a group average temperature sequence, including: Counting the number of data of each of the historical ambient temperature data to determine a historical ambient temperature average based on each of the historical ambient temperature data and the number of data, and determining an ambient temperature change based on a difference between the historical ambient temperature average and the current ambient temperature average; The cell temperature difference value sequence is divided according to the chronological order and the preset group length to obtain each cell temperature difference value group, and the group average value corresponding to each cell temperature difference value group is determined, and a group average temperature sequence is constructed based on the group average value and the chronological order.

4. The battery failure detection method according to claim 1, wherein: After determining the intra-group average values ​​corresponding to the cell temperature difference groups and obtaining the group average temperature sequence, the method further includes: Determining the corresponding average values ​​to be calculated based on the respective average values ​​within each group, determining the standard deviation determination results of the respective average values ​​within each group, and determining the results to be compared based on the absolute value of the difference between the respective average values ​​within each group and the corresponding average value to be calculated; the standard deviation determination result is the standard deviation; It is determined in turn whether each of the results to be compared is greater than the corresponding standard deviation determination result. If each of the results to be compared is greater than the corresponding standard deviation determination result, the cell temperature difference value group corresponding to the result to be compared is deleted from the group average temperature sequence to obtain a new group average temperature sequence.

5. The battery failure detection method according to any one of claims 1 to 4, characterized in that: The determining whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold includes: determining whether the temperature trend slope is greater than the target fault determination threshold; If the temperature trend slope is greater than the target fault judgment threshold, the operating state corresponding to the battery to be detected is determined to be a fault state; if the temperature trend slope is not greater than the target fault judgment threshold, the operating state corresponding to the battery to be detected is determined to be a normal operating state.

6. A battery fault detection device, characterized in that: include: a temperature difference value sequence determination module, configured to collect historical cell temperature data, historical ambient temperature data, and an average value of the current ambient temperature of the battery to be tested, and determine a cell temperature difference value sequence based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data; an average temperature sequence determination module, configured to determine a historical ambient temperature average based on the historical ambient temperature data, determine an ambient temperature change based on the historical ambient temperature average and the current ambient temperature average, then divide the battery cell temperature difference sequence according to a preset group length to obtain battery cell temperature difference groups, and determine an intra-group average corresponding to each battery cell temperature difference group to obtain a group average temperature sequence; a fault judgment threshold determination module, configured to perform a linear fit on the group average temperature series to obtain a temperature trend slope corresponding to the cell temperature difference, and adjust an original fault judgment threshold using the ambient temperature change and a correction coefficient to obtain a target fault judgment threshold, so as to determine whether the battery to be tested has a fault based on the temperature trend slope and the target fault judgment threshold; Specifically, a preset fitting formula is used to perform a linear fit on the group average temperature series based on a time index to obtain a temperature trend slope corresponding to the battery cell temperature difference, and a current heat dissipation efficiency is determined to determine a correction coefficient using the current heat dissipation efficiency; The value range corresponding to the correction coefficient satisfies the preset value range condition; the target fault judgment threshold is obtained by adjusting the original fault judgment threshold based on the correction coefficient using a preset function; wherein the expression of the preset function is as follows: ； in, is the correction coefficient, is the ambient temperature change, is the original fault judgment threshold, is the target fault judgment threshold.

7. The battery failure detection device according to claim 6, characterized in that: The average temperature sequence determination module includes: an ambient temperature change determination unit, configured to count the number of data of each of the historical ambient temperature data, determine a historical ambient temperature average based on each of the historical ambient temperature data and the number of data, and determine an ambient temperature change based on a difference between the historical ambient temperature average and the current ambient temperature average; The average temperature sequence determination subunit is used to divide the battery cell temperature difference value sequence according to the chronological order and the preset grouping length to obtain each battery cell temperature difference value group, and determine the group average value corresponding to each battery cell temperature difference value group, and construct a group average temperature sequence based on each group average value, the chronological order and the historical ambient temperature average value.

8. The battery failure detection device according to claim 6, characterized in that: The fault judgment threshold determination module includes: a temperature trend slope judgment unit, configured to judge whether the temperature trend slope is greater than the target fault judgment threshold; An operating status determination unit is configured to determine that the operating status corresponding to the battery to be detected is a fault status if the temperature trend slope is greater than the target fault judgment threshold, and to determine that the operating status corresponding to the battery to be detected is a normal operating status if the temperature trend slope is not greater than the target fault judgment threshold.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the battery fault detection method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that Used to store a computer program, wherein when the computer program is executed by a processor, the battery failure detection method according to any one of claims 1 to 5 is implemented.

Images