Battery fault detection method, device and equipment and storage medium

Abstract

The invention discloses a battery fault detection method, device and equipment and a storage medium, and relates to the technical field of batteries, and the method comprises the steps: collecting historical cell temperature data, historical environment temperature data and a current environment temperature average value of a to-be-detected battery, determining a battery cell temperature difference value sequence based on the maximum battery cell temperature and the minimum battery cell temperature of each time point in the historical battery cell temperature data; determining an environment temperature variation based on a historical environment temperature average value determined based on the historical environment temperature data and a current environment temperature average value, dividing the battery cell temperature difference value sequence to obtain battery cell temperature difference value groups, and determining an intra-group average value of each battery cell temperature difference value group to obtain a group average temperature sequence; and performing linear fitting on the group average temperature sequence to obtain a temperature trend slope, determining a target fault judgment threshold based on the environment temperature variation and the correction coefficient, and judging whether the to-be-detected battery has a fault based on the temperature trend slope and the target fault judgment threshold. Therefore, the accuracy of battery detection can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and particularly 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 dispatching. The health status of energy storage batteries has a crucial impact on their performance and lifespan. Therefore, how to detect battery faults in real time and take timely repair or replacement measures has become a key issue to ensure the stable operation of the battery system.

[0003] Currently, common battery fault detection methods mainly rely on the monitoring of parameters such as temperature, charge and discharge current, etc. However, these methods often have certain limitations. Especially in the case of a complex battery working environment or relatively minor self-damage of the battery, it 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 change: when the ambient temperature rises, the heat dissipation efficiency of the energy storage cabinet decreases, resulting in a natural increase in the temperature difference between battery cells, which may thus cause 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 an urgent problem to be solved currently. 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, and further enhance the safety of the production process. The specific scheme is as follows:

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

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

[0008] Determine the historical ambient temperature average value based on the historical ambient temperature data, so as to determine the ambient temperature change amount based on the historical ambient temperature average value and the average value of the current ambient temperature. Then, divide the battery cell temperature difference value sequence according to a preset grouping length to obtain each battery cell temperature difference value group, and determine the average value within each group corresponding to each battery cell temperature difference value group to obtain the group average temperature sequence;

[0009] Perform a linear fit on the group average temperature sequence to obtain the temperature trend slope corresponding to the cell temperature difference, and adjust the original fault determination threshold using the environmental temperature change amount and the correction coefficient to obtain the target fault determination threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold.

[0010] Optionally, collecting the historical cell temperature data, historical environmental temperature data, and current average environmental temperature of the battery to be detected, and determining 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, includes:

[0011] Determine the battery to be detected based on preset requirements, so as to obtain the historical cell temperature data, historical environmental temperature data, and current average environmental temperature corresponding to the battery to be detected, and determine the target time point based on the preset requirements;

[0012] Based on the target time point, determine the cell temperature data corresponding to each time point in the historical cell temperature data, set the cell temperature data with the maximum temperature value among the cell temperature data of each time point as the maximum cell temperature, and then set the cell temperature data with the minimum temperature value among the cell temperature data of each time point as the minimum cell temperature;

[0013] Determine the cell temperature difference value corresponding to the target time point based on the difference between the maximum cell temperature and the minimum cell temperature, and determine the cell temperature difference value sequence based on each cell temperature difference value.

[0014] Optionally, determining the historical average environmental temperature based on the historical environmental temperature data, so as to determine the environmental temperature change amount based on the historical average environmental temperature and the current average environmental temperature, and then dividing the cell temperature difference value sequence according to a preset grouping length to obtain each cell temperature difference value group, and determining the within-group average value corresponding to each cell temperature difference value group to obtain the group average temperature sequence, includes:

[0015] Count the data quantity of each historical environmental temperature data, so as to determine the historical average environmental temperature based on each historical environmental temperature data and the data quantity, and determine the environmental temperature change amount based on the difference between the historical average environmental temperature and the current average environmental temperature;

[0016] Divide the cell temperature difference value sequence according to the time sequence and the preset grouping length to obtain each cell temperature difference value group, and determine the within-group average value corresponding to each cell temperature difference value group respectively, and set each within-group average value as the new historical average environmental temperature, so as to construct the group average temperature sequence based on the time sequence and each historical average environmental temperature.

[0017] Optionally, after determining the within-group average value corresponding to each group of cell temperature difference values and obtaining the group average temperature sequence, the method further includes:

[0018] Determining the average value to be calculated and the standard deviation determination result corresponding to each of the within-group average values, and determining a comparison result to be compared based on each of the within-group average values and the corresponding average value to be calculated;

[0019] Sequentially determining whether each of the comparison results to be compared is greater than the corresponding standard deviation determination result. If each of the comparison results to be compared is greater than the corresponding standard deviation determination result, deleting the group of cell temperature difference values corresponding to the comparison result to be compared from the group average temperature sequence to obtain a new group average temperature sequence.

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

[0021] Performing linear fitting on the group average temperature sequence based on a time index using a preset fitting formula to obtain a temperature trend slope corresponding to the cell temperature difference, and determining a current heat dissipation efficiency, so as to determine a correction coefficient using the current heat dissipation efficiency; the value range of the correction coefficient satisfies a preset value range condition;

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

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

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

[0025] A temperature difference value sequence determination module, configured to collect historical cell temperature data, historical environmental temperature data, and a current average environmental temperature of a battery to be detected, 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 mean based on the historical ambient temperature data, determine an ambient temperature change amount based on the historical ambient temperature mean and the current ambient temperature average, then divide the cell temperature difference value sequence according to a preset grouping length to obtain each cell temperature difference value group, and determine the within-group average value corresponding to each cell temperature difference value group to obtain a group average temperature sequence;

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

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

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

[0030] An average temperature sequence determination subunit, configured to divide the cell temperature difference value sequence according to the time sequence and a preset grouping length to obtain each cell temperature difference value group, determine the within-group average value corresponding to each cell temperature difference value group respectively, and set each within-group average value as a new historical ambient temperature mean, so as to construct a group average temperature sequence based on the time sequence and each historical ambient temperature mean.

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

[0032] A correction coefficient determination unit, configured to perform linear fitting on the group average temperature sequence based on a preset fitting formula and a time index to obtain a temperature trend slope corresponding to the cell temperature difference, and determine the current heat dissipation efficiency, so as to determine a correction coefficient by using the current heat dissipation efficiency; the value range of the correction coefficient satisfies a preset value range condition;

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

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

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

[0036] A memory, configured to store a computer program;

[0037] A processor, configured to execute the computer program to implement the foregoing battery fault detection method.

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

[0039] As can be seen from the above, before performing battery fault detection in the present application, it is necessary to collect historical cell temperature data, historical ambient temperature data, and the current ambient temperature average value of the battery to be detected, and determine a sequence of cell temperature difference values 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 value based on the historical ambient temperature data, so as to determine the ambient temperature change amount based on the historical ambient temperature average value and the current ambient temperature average value, and then divide the sequence of cell temperature difference values according to a preset grouping length to obtain each group of cell temperature difference values, and determine the average value within each group corresponding to each group of cell temperature difference values to obtain a sequence of group average temperatures; perform linear fitting on the sequence of group average temperatures to obtain the temperature trend slope corresponding to the cell temperature difference, and adjust the original fault determination threshold using the ambient temperature change amount and a correction coefficient to obtain the target fault determination threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold.

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

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0042] Figure 1 It is a flowchart of a battery fault detection method disclosed in the present application;

[0043] Figure 2 It is a schematic structural diagram of a battery fault detection device disclosed in the present application;

[0044] Figure 3 It is a structural diagram of an electronic device disclosed in the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope 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-discharge current. However, these methods often have certain limitations. Especially in the case of a complex battery working environment or relatively minor self-damage of the battery, it 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, resulting in a natural increase in the temperature difference between battery cells, which may cause misjudgment. Therefore, this application provides a battery fault detection method that can improve the accuracy of battery fault detection and thus enhance the safety of the production process.

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

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

[0049] In this embodiment, during the process of detecting the battery, the data of the battery needs to be collected and preprocessed for the smooth implementation of this application. Among them, during the process of data collection, this embodiment of the application needs to collect the historical battery cell temperature data corresponding to the battery to be detected and the ambient temperature data and the average value of the ambient temperature in the recent period , and define the difference between the maximum battery cell temperature and the minimum battery cell temperature at each time point as the battery cell temperature difference , and the definition formula of the battery cell temperature difference is as follows:

[0050] .

[0051] Specifically, collect the historical cell temperature data, historical ambient temperature data, and the average value of the current ambient temperature 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, which may include: determining the battery to be detected based on a preset requirement to obtain the historical cell temperature data, historical ambient temperature data, and the average value of the current ambient temperature corresponding to the battery to be detected, and determining the target time point based on the preset requirement; determining the cell temperature data corresponding to each time point corresponding to the target time point in the historical cell temperature data, setting the cell temperature data with the maximum temperature value among the cell temperature data of each time point as the maximum cell temperature, and then setting the cell temperature data with the minimum temperature value among the cell temperature data of each time point as the minimum cell temperature; determining the cell temperature difference value corresponding to the target time point based on the difference between the maximum cell temperature and the minimum cell temperature, and determining the cell temperature difference value sequence based on each cell temperature difference value.

[0052] Step S12: Determine the average historical ambient temperature based on the historical ambient temperature data, so as to determine the ambient temperature change amount based on the average historical ambient temperature and the average value of the current ambient temperature, and then divide the cell temperature difference value sequence according to a preset grouping length to obtain each cell temperature difference value group, and determine the average value within each group corresponding to each cell temperature difference value group to obtain the group average temperature sequence.

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

[0054] ;

[0055] Subsequently, in the embodiment of the present application, it is necessary to group the cell temperature difference according to the set group size, such as , and determine the average temperature corresponding to each group , so as to use the average temperature to replace the average temperature of the group, reducing the temperature fluctuation problem.

[0056] Specifically, the historical ambient temperature mean is determined based on historical ambient temperature data, and the ambient temperature change amount is determined based on the historical ambient temperature mean and the current ambient temperature average. Then, the cell temperature difference value sequence is divided according to a preset grouping length to obtain each cell temperature difference value group, and the within-group average value corresponding to each cell temperature difference value group is determined to obtain a group average temperature sequence, which may include: counting the data quantity of each historical ambient temperature data, determining the historical ambient temperature mean based on each historical ambient temperature data and the data quantity, and determining the ambient temperature change amount based on the difference between the historical ambient temperature mean and the current ambient temperature average; dividing the cell temperature difference value sequence according to the chronological order and the preset grouping length to obtain each cell temperature difference value group, determining the within-group average value corresponding to each cell temperature difference value group respectively, and setting each within-group average value as a 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] Subsequently, in a specific embodiment, after obtaining the group average temperature sequence including the within-group average values corresponding to each cell temperature difference value group, the embodiments of the present application need to determine the average temperature of each group The corresponding average value And the standard deviation , and remove the corresponding data when . Specifically, after determining the within-group average values corresponding to each cell temperature difference value group and obtaining the group average temperature sequence, it may further include: determining the average value to be calculated and the standard deviation determination result corresponding to each within-group average value respectively, and determining the comparison result to be compared based on each within-group average value and the corresponding average value to be calculated; sequentially determining whether each comparison result to be compared is greater than the corresponding standard deviation determination result. If each comparison result to be compared is greater than the corresponding standard deviation determination result, the cell temperature difference value group corresponding to the comparison result to be compared is deleted 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 cell temperature difference, and adjust the original fault determination threshold using the ambient temperature change amount and the correction coefficient to obtain a target fault determination threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination 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. In a specific embodiment, the embodiments of the present application need to perform linear fitting on the group average temperature sequence To obtain a fitting formula: . Wherein, Is the time index, Is the slope, and the unit is .

[0060] Subsequently, in a specific embodiment, the embodiment of the present application needs to use a preset formula to perform a dynamic adjustment operation on the original threshold according to the environmental temperature change amount and the correction coefficient (obtained by experimental calibration), and the expression of the preset formula is as follows: ;

[0061] ;

[0062] wherein, The value range of is (calibrated according to the experimental data of the decrease in heat dissipation efficiency), and when

[0063] the state of the battery to be detected is determined to be a fault state. Specifically, performing linear fitting on the group average temperature sequence to obtain the temperature trend slope corresponding to the cell temperature difference, and using the environmental temperature change amount and the correction coefficient to adjust the original fault determination threshold to obtain the target fault determination threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold may include: performing linear fitting on the group average temperature sequence based on the time index using a preset fitting formula to obtain the temperature trend slope corresponding to the cell temperature difference, and determining the current heat dissipation efficiency to determine the correction coefficient using the current heat dissipation efficiency; the value range of the correction coefficient satisfies the preset value range condition; using a preset function and based on the correction coefficient to adjust the original fault determination threshold to obtain the target fault determination threshold, and then determining whether the temperature trend slope is greater than the target fault determination threshold; the preset function is a non-linear correction function; if the temperature trend slope is greater than the target fault determination threshold, it is determined that the operating state of the battery to be detected is a fault state, and if the temperature trend slope is not greater than the target fault determination threshold, it is determined that the operating state of the battery to be detected is a normal operating state.

[0064] As can be seen from the above, in the embodiment of the present application, it is first necessary to collect the historical cell temperature data, historical ambient temperature data and the current average ambient temperature 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; subsequently, determine the historical ambient temperature average value based on the historical ambient temperature data, so as to determine the ambient temperature change amount based on the historical ambient temperature average value and the current average ambient temperature, and then divide the cell temperature difference value sequence according to the preset grouping length to obtain each cell temperature difference value group, and determine the within-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 amount and the correction coefficient to adjust the original fault determination threshold to obtain the target fault determination threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold. In this way, the accuracy of fault detection of the battery is improved during the battery fault detection process, thereby enhancing the safety of the production process.

[0065] Correspondingly, as shown in Figure 2 the present application also provides a battery fault detection device, including:

[0066] A temperature difference value sequence determination module 11, configured to collect the historical cell temperature data, historical ambient temperature data and the current average ambient temperature 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;

[0067] An average temperature sequence determination module 12, configured to determine the historical ambient temperature average value based on the historical ambient temperature data, so as to determine the ambient temperature change amount based on the historical ambient temperature average value and the current average ambient temperature, and then divide the cell temperature difference value sequence according to the preset grouping length to obtain each cell temperature difference value group, and determine the within-group average value corresponding to each cell temperature difference value group to obtain the group average temperature sequence;

[0068] A fault determination threshold determination module 13, configured to 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 amount and the correction coefficient to adjust the original fault determination threshold to obtain the target fault determination threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold.

[0069] As can be seen from the above, before performing battery fault detection in the embodiments of the present application, it is first necessary to collect the historical cell temperature data, historical ambient temperature data, and the average value of the current ambient temperature 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; subsequently, determine the historical ambient temperature average value based on the historical ambient temperature data, so as to determine the ambient temperature change amount based on the historical ambient temperature average value and the average value of the current ambient temperature, and then divide the cell temperature difference value sequence according to the preset grouping length to obtain each cell temperature difference value group, and determine the average value within each group 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 adjust the original fault determination threshold using the ambient temperature change amount and the correction coefficient to obtain the target fault determination threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold. In this way, the accuracy of battery fault detection is improved during the battery fault detection process, thereby enhancing the safety of the production process.

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

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

[0072] A cell temperature data determination unit, configured to determine the cell temperature data corresponding to each time point corresponding to the target time point in the historical cell temperature data based on the target time point, set the cell temperature data with the maximum temperature value among the cell temperature data of each time point as the maximum cell temperature, and then set the cell temperature data with the minimum temperature value among the cell temperature data of each time point as the minimum cell temperature;

[0073] A temperature difference value sequence determination subunit, configured to determine the cell temperature difference value corresponding to the target time point based on the difference between the maximum cell temperature and the minimum cell temperature, and determine the cell temperature difference value sequence based on each cell temperature difference value.

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

[0075] An ambient temperature change amount determination unit, configured to count the data quantity of each historical ambient temperature data, so as to determine the historical ambient temperature average value based on each historical ambient temperature data and the data quantity, and determine the ambient temperature change amount based on the difference between the historical ambient temperature average value and the average value of the current ambient temperature;

[0076] An average temperature sequence determination subunit is configured to divide the cell temperature difference value sequence according to the chronological order and a preset grouping length to obtain each cell temperature difference value group, determine the within-group average value corresponding to each cell temperature difference value group, and set each within-group average value 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.

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

[0078] A to-be-compared result determination unit is configured to determine the to-be-calculated average value and the standard deviation determination result corresponding to each within-group average value, and determine a to-be-compared result based on each within-group average value and the corresponding to-be-calculated average value;

[0079] A to-be-compared result judgment unit is configured to sequentially judge 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, delete the cell temperature difference value group corresponding to the to-be-compared result in the group average temperature sequence to obtain a new group average temperature sequence.

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

[0081] A correction coefficient determination unit is configured to perform linear fitting on the group average temperature sequence based on a time index by using a preset fitting formula to obtain a temperature trend slope corresponding to the cell temperature difference, and determine the current heat dissipation efficiency, so as to determine a correction coefficient by using the current heat dissipation efficiency; the value range of the correction coefficient satisfies a preset value range condition;

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

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

[0084] Furthermore, an embodiment of the present application also discloses an electronic device, Figure 3It is a structural diagram of an electronic device 20 shown according to an exemplary embodiment. The content in the figure should not be regarded as any limitation on 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. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the battery fault detection method disclosed in any of the foregoing embodiments. Additionally, 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 external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of this application, and no specific limitation is imposed on it here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application needs, and no specific limitation is made here.

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

[0087] Among them, the operating system 221 is used to manage and control each hardware device on the electronic device 20 and the computer program 222, and it can be Windows Server, Netware, Unix, Linux, etc. The computer program 222, in addition to including the computer program capable of completing the battery fault detection method executed by the electronic device 20 disclosed in any of the foregoing embodiments, may further include computer programs capable of completing other specific tasks.

[0088] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the battery fault detection method disclosed above. For the specific steps of this method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be repeated here.

[0089] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0090] Those skilled in the art may further realize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

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

[0092] Finally, it should also be noted that in this document, relational terms such as "first" and "second" are only used 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 term "comprising", "including" or any other variant thereof is 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 expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0093] The technical solutions provided in this application have been introduced in detail above. Specific examples have been used herein to illustrate the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A battery fault detection method, characterized in that, Including: Collecting historical cell temperature data, historical ambient temperature data and the average value of the current ambient temperature of the battery to be detected, and determining a sequence of cell temperature difference values based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data; Determining the average value of the historical ambient temperature based on the historical ambient temperature data, so as to determine the change amount of the ambient temperature based on the average value of the historical ambient temperature and the average value of the current ambient temperature, then dividing the sequence of cell temperature difference values according to a preset grouping length to obtain each group of cell temperature difference values, and determining the average value within each group corresponding to each group of cell temperature difference values to obtain a sequence of average group temperatures; Performing linear fitting on the sequence of average group temperatures to obtain the temperature trend slope corresponding to the cell temperature difference, and adjusting the original fault determination threshold by using the change amount of the ambient temperature and a correction coefficient to obtain a target fault determination threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold.

2. The battery fault detection method according to claim 1, wherein The step of collecting historical cell temperature data, historical ambient temperature data and the average value of the current ambient temperature of the battery to be detected, and determining a sequence of cell temperature difference values based on the maximum cell temperature and the minimum cell temperature corresponding to each time point in the historical cell temperature data includes: Determining the battery to be detected based on a preset requirement, so as to obtain the historical cell temperature data, historical ambient temperature data and the average value of the current ambient temperature corresponding to the battery to be detected, and determining a target time point based on the preset requirement; Determining the cell temperature data corresponding to each time point corresponding to the target time point in the historical cell temperature data based on the target time point, setting the cell temperature data with the maximum temperature value among the cell temperature data of each time point as the maximum cell temperature, and then setting the cell temperature data with the minimum temperature value among the cell temperature data of each time point as the minimum cell temperature; Determining the cell temperature difference value corresponding to the target time point based on the difference between the maximum cell temperature and the minimum cell temperature, and determining a sequence of cell temperature difference values based on each cell temperature difference value.

3. The battery fault detection method according to claim 1, wherein The step of determining the average value of the historical ambient temperature based on the historical ambient temperature data, so as to determine the change amount of the ambient temperature based on the average value of the historical ambient temperature and the average value of the current ambient temperature, then dividing the sequence of cell temperature difference values according to a preset grouping length to obtain each group of cell temperature difference values, and determining the average value within each group corresponding to each group of cell temperature difference values to obtain a sequence of average group temperatures includes: Counting the number of data in each historical ambient temperature data, so as to determine the average value of the historical ambient temperature based on each historical ambient temperature data and the number of data, and determining the change amount of the ambient temperature based on the difference between the average value of the historical ambient temperature and the average value of the current ambient temperature; Dividing the sequence of cell temperature difference values according to the chronological order and a preset grouping length to obtain each group of cell temperature difference values, and determining the average value within each group corresponding to each group of cell temperature difference values, and setting each average value within the group as the new average value of the historical ambient temperature, so as to construct a sequence of average group temperatures based on the chronological order and each average value of the historical ambient temperature.

4. The battery fault detection method according to claim 1, wherein After determining the within-group average value corresponding to each group of cell temperature difference values and obtaining the group average temperature sequence, the following steps are further included: Determine the average value to be calculated and the standard deviation determination result corresponding to each of the within-group average values, and determine the result to be compared based on each of the within-group average values and the corresponding average value to be calculated; Successively determine 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, delete the group of cell temperature difference values corresponding to the result to be compared in the group average temperature sequence to obtain a new group average temperature sequence.

5. The battery fault detection method according to any one of claims 1 to 4, characterized in that, The linear fitting of the group average temperature sequence to obtain the temperature trend slope corresponding to the cell temperature difference, and using the environmental temperature change amount and the correction coefficient to adjust the original fault determination threshold to obtain the target fault determination threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold, includes: Use a preset fitting formula and based on the time index, perform linear fitting on the group average temperature sequence to obtain the temperature trend slope corresponding to the cell temperature difference, and determine the current heat dissipation efficiency, so as to determine the correction coefficient using the current heat dissipation efficiency; the value range of the correction coefficient satisfies the preset value range condition; Use a preset function and based on the correction coefficient, adjust the original fault determination threshold to obtain the target fault determination threshold, and then determine whether the temperature trend slope is greater than the target fault determination threshold; the preset function is a non-linear correction function; If the temperature trend slope is greater than the target fault determination threshold, determine that the operating state of the battery to be detected is a fault state. If the temperature trend slope is not greater than the target fault determination threshold, determine that the operating state of the battery to be detected is a normal operating state.

6. A battery fault detection device, characterized in that, Includes: A temperature difference value sequence determination module, configured to collect historical cell temperature data, historical environmental temperature data, and the current average environmental temperature of the battery to be detected, 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 the historical average environmental temperature based on the historical environmental temperature data, so as to determine the environmental temperature change amount based on the historical average environmental temperature and the current average environmental temperature, and then divide the cell temperature difference value sequence according to a preset grouping length to obtain each group of cell temperature difference values, and determine the within-group average value corresponding to each group of cell temperature difference values to obtain a group average temperature sequence; A fault determination threshold determination module, configured to perform linear fitting on the group average temperature sequence to obtain the temperature trend slope corresponding to the cell temperature difference, and use the environmental temperature change amount and the correction coefficient to adjust the original fault determination threshold to obtain the target fault determination threshold, so as to determine whether the battery to be detected has a fault based on the temperature trend slope and the target fault determination threshold.

7. The battery fault detection device according to claim 6, characterized in that, The average temperature sequence determination module includes: An ambient temperature change amount determination unit, configured to count the data quantities of the respective historical ambient temperature data, determine a historical ambient temperature mean value based on the respective historical ambient temperature data and the data quantities, and determine an ambient temperature change amount based on the difference between the historical ambient temperature mean value and the current ambient temperature average value; An average temperature sequence determination subunit, configured to divide the cell temperature difference value sequence according to the chronological order and a preset grouping length to obtain respective cell temperature difference value groups, determine the within-group average values corresponding to the respective cell temperature difference value groups, and set the within-group average values as new historical ambient temperature mean values, so as to construct a group average temperature sequence based on the chronological order and the respective historical ambient temperature mean values.

8. The battery fault detection device according to claim 6, wherein, The fault determination threshold determination module includes: A correction coefficient determination unit, configured to perform linear fitting on the group average temperature sequence based on a time index by using a preset fitting formula to obtain a temperature trend slope corresponding to the cell temperature difference, determine a current heat dissipation efficiency, and determine a correction coefficient by using the current heat dissipation efficiency; a value range corresponding to the correction coefficient satisfies a preset value range condition; A temperature trend slope judgment unit, configured to adjust an original fault determination threshold based on the correction coefficient by using a preset function to obtain a target fault determination threshold, and then judge whether the temperature trend slope is greater than the target fault determination threshold; the preset function is a non-linear correction function; An operating state determination unit, configured to, if the temperature trend slope is greater than the target fault determination threshold, determine that the operating state corresponding to the battery to be detected is a fault state, and if the temperature trend slope is not greater than the target fault determination threshold, determine that the operating state corresponding to the battery to be detected is a normal operating state.

9. An electronic device, characterized in that, It includes: A memory, configured to store a computer program; 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, For storing a computer program, wherein the computer program, when executed by a processor, implements the battery fault detection method according to any one of claims 1 to 5.

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