Battery risk identification method and device, equipment and storage medium

Abstract

The invention discloses a battery risk identification method and device, equipment and a storage medium, and relates to the technical field of batteries. According to the risk identification method of the battery, the voltage similarity between the median voltage time-domain data and the voltage time-domain data of each battery cell of the battery is calculated, and whether the risk exists in the battery is identified based on the voltage similarity. Therefore, potential abnormal values in the data can be identified by comparing the difference between the data and the median, so that the median voltage time domain data is taken as a reference for judging the voltage data of the battery cells and the overall voltage data of the battery, the battery cells with abnormal voltage data can be accurately identified, and meanwhile, the voltage data of the battery cells can be accurately identified. The qualitative identification between the median voltage time domain data and the voltage time domain data is converted into a quantitative voltage similarity value, so that the influence of subjective factors can be reduced, and the accuracy of a battery risk identification result is improved.

Description

Technical Field

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

[0002] With the rapid development of renewable energy, new energy vehicles have become an important category of vehicles because of their ability to balance renewable energy and reduce pollution. However, the power unit of new energy vehicles is a battery system, and the driving of the vehicle depends on the charging and discharging process of the battery system. Generally speaking, a battery system includes multiple cells. Due to reasons such as cell aging, overcharging and over-discharging, there are also certain risks in the battery charging and discharging process. Therefore, it is necessary to identify the risks of the battery during its use to reduce the risks of battery use.

[0003] In the related art, the voltage difference between the cells in the battery is obtained to identify whether the battery has risks. However, the process of judging whether the battery has risks by the voltage difference between the cells is a qualitative judgment process, which is easily affected by the subjective factors of engineers, and the obtained battery risk identification results are inaccurate. Therefore, how to accurately identify whether the battery has risks is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of the present invention is to provide a battery risk identification method, device, equipment and storage medium, aiming to solve the technical problem in the related art that the risk identification of batteries is easily affected by the subjective factors of engineers, resulting in inaccurate risk identification results.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme:

[0006] In a first aspect, the present application provides a battery risk identification method, comprising: obtaining median voltage time domain data of a battery in a current time period, and voltage time domain data of each battery cell of the battery; the voltage time domain data of the battery cell is used to characterize the voltage of the battery cell at multiple moments in the current time period, and the median voltage time domain data is used to characterize the median voltage at multiple moments in the current time period, and the median voltage at a moment is the median of the voltages of each battery cell of the battery at that moment; based on the median voltage time domain data and the voltage time domain data of each battery cell of the battery, determining the voltage similarity corresponding to each battery cell in the battery, the voltage similarity is used to characterize the degree of similarity between the fluctuation of the battery cell voltage and the fluctuation of the median voltage; determining the risk identification result of the battery according to the voltage similarity corresponding to each battery cell in the battery.

[0007] The battery risk identification method provided in the embodiment of the present application calculates the voltage similarity between the median voltage time domain data and the voltage time domain data of each battery cell, and identifies whether the battery is at risk based on the voltage similarity. Therefore, because the difference between each data and the median can identify potential abnormal values ​​in each data, the median voltage time domain data is used as a reference for judging the voltage data of each battery cell and the overall voltage data of the battery, and the battery cell with abnormal voltage data can be accurately identified. At the same time, the qualitative identification between the median voltage time domain data and the voltage time domain data is converted into a quantitative voltage similarity value, which can reduce the influence of subjective factors and thus improve the accuracy of the battery risk identification results.

[0008] In some embodiments, the above-mentioned determination of the voltage similarity corresponding to each battery cell in the battery based on the median voltage time domain data and the voltage time domain data of each battery cell of the battery includes: converting the median voltage time domain data into median voltage frequency domain data, and converting the voltage time domain data of each battery cell of the battery into voltage frequency domain data of each battery cell of the battery; determining the voltage similarity corresponding to each battery cell in the battery based on the median voltage frequency domain data and the voltage frequency domain data of each battery cell of the battery.

[0009] In some embodiments, the above-mentioned determination of the voltage similarity corresponding to each battery cell in the battery based on the median voltage frequency domain data and the voltage frequency domain data of each battery cell of the battery includes: for each battery cell in the battery, performing Euclidean distance calculation on the median voltage frequency domain data and the voltage frequency domain data of the battery cell to obtain the voltage similarity of the battery cell.

[0010] In some embodiments, the risk identification result of the battery is determined based on the voltage similarities corresponding to the battery cells in the battery, including: when the voltage similarities corresponding to the battery cells are less than or equal to a first similarity threshold, the risk identification result of the battery is determined to be that there is no risk; or, when there is a battery cell whose voltage similarity corresponding to the battery cells is greater than the first similarity threshold, the risk identification result of the battery is determined to be that there is a risk.

[0011] In some embodiments, after determining that the risk identification result of the battery is that there is a risk, the method also includes: obtaining the voltage similarity of the target battery cell in a historical time period, the target battery cell being a battery cell whose voltage similarity in a current time period is greater than a first similarity threshold; based on the voltage similarity of the target battery cell in the historical time period, determining the risk type corresponding to the target battery cell.

[0012] In some embodiments, the above-mentioned determination of the risk type corresponding to the target battery cell based on the voltage similarity of the target battery cell in the historical time period includes: when the voltage similarity of the target battery cell in the historical time period is less than or equal to the second similarity threshold, determining that the risk type corresponding to the target battery cell is poor consistency; or, when the voltage similarity of the target battery cell in the historical time period is greater than the second similarity threshold, determining that the risk type corresponding to the target battery cell is internal short circuit risk.

[0013] In some embodiments, the second similarity threshold is determined according to voltage similarities of the target battery cell in multiple historical time periods.

[0014] In a second aspect, the present application provides a battery risk identification device, including a first acquisition unit and a processing unit: the first acquisition unit is used to acquire the median voltage time domain data of the battery in the current time period, and the voltage time domain data of each battery cell of the battery; the voltage time domain data of the battery cell is used to characterize the voltage of the battery cell at multiple moments in the current time period, and the median voltage time domain data is used to characterize the median voltage at multiple moments in the current time period, and the median voltage at a moment is the median of the voltages of each battery cell of the battery at that moment; the processing unit is used to determine the voltage similarity corresponding to each battery cell in the battery based on the median voltage time domain data and the voltage time domain data of each battery cell of the battery, and the voltage similarity is used to characterize the similarity between the fluctuation of the battery cell voltage and the fluctuation of the median voltage; the processing unit is also used to determine the risk identification result of the battery according to the voltage similarity corresponding to each battery cell in the battery.

[0015] In some embodiments, the above-mentioned processing unit is specifically used to convert the median voltage time domain data into the median voltage frequency domain data, and to convert the voltage time domain data of each battery cell of the battery into the voltage frequency domain data of each battery cell of the battery; based on the median voltage frequency domain data and the voltage frequency domain data of each battery cell of the battery, determine the voltage similarity corresponding to each battery cell in the battery.

[0016] In some embodiments, the processing unit is specifically used to perform Euclidean distance calculation on the median voltage frequency domain data and the voltage frequency domain data of the battery cell for each battery cell in the battery to obtain the voltage similarity of the battery cell.

[0017] In some embodiments, the above-mentioned processing unit is specifically used to determine that the risk identification result of the battery is that there is no risk when the voltage similarities corresponding to each battery cell are less than or equal to a first similarity threshold; or, when there is a battery cell whose voltage similarity corresponding to the battery cell is greater than the first similarity threshold, determine that the risk identification result of the battery is that there is a risk.

[0018] In some embodiments, the risk identification device of the above-mentioned battery also includes: a second acquisition unit and a determination unit; the above-mentioned second acquisition unit is used to obtain the voltage similarity of the target battery cell in the historical time period, and the target battery cell is a battery cell whose voltage similarity in the current time period is greater than the first similarity threshold; the above-mentioned determination unit is used to determine the risk type corresponding to the target battery cell based on the voltage similarity of the target battery cell in the historical time period.

[0019] In some embodiments, the above-mentioned determination unit is specifically used to determine that the risk type corresponding to the target battery cell is poor consistency when the voltage similarity of the target battery cell in the historical time period is less than or equal to the second similarity threshold; or, when the voltage similarity of the target battery cell in the historical time period is greater than the second similarity threshold, determine that the risk type corresponding to the target battery cell is internal short circuit risk.

[0020] In some embodiments, the second similarity threshold is determined according to voltage similarities of the target battery cell in multiple historical time periods.

[0021] In a third aspect, the present application provides a device comprising: a processor and a memory configured to store processor executable instructions; wherein the processor is configured to execute the instructions to implement any one of the optional battery risk identification methods in the first aspect above.

[0022] In a fourth aspect, the present application provides a vehicle comprising the device of the third aspect.

[0023] In a fifth aspect, the present application provides a computer-readable storage medium having instructions stored thereon. When the instructions in the computer-readable storage medium are executed by a device, the device is enabled to execute any one of the optional battery risk identification methods in the first aspect described above.

[0024] In a sixth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions are executed on a processor of a device, the device is enabled to execute a battery risk identification method as optionally described in any one of the first aspects above. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 A flow chart of a battery risk identification method provided in an embodiment of the present application;

[0027] Figure 2 A flowchart of another battery risk identification method provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of a frequency spectrum of a battery cell provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram of a frequency spectrum of another battery cell provided in an embodiment of the present application;

[0030] Figure 5 A flowchart of another battery risk identification method provided in an embodiment of the present application;

[0031] Figure 6 A schematic diagram of voltage similarity provided in an embodiment of the present application;

[0032] Figure 7 A flowchart of another battery risk identification method provided in an embodiment of the present application;

[0033] Figure 8 A flowchart of another battery risk identification method provided in an embodiment of the present application;

[0034] Fig. 9 A schematic diagram of the structure of a battery risk identification device provided in an embodiment of the present application;

[0035] Fig.10 A schematic diagram of the structure of a device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0037] In the description of the present invention, it should be understood that the terms "upper", "lower", "left", "right", "front", "back", "inside", "outside" and the like indicate directions or positional relationships based on the directions or relative positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention. Unless otherwise specified, the above-mentioned directional description can be flexibly set in the process of actual application under the condition that the relative positional relationship shown in the accompanying drawings is satisfied.

[0038] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0039] In the embodiments of the present invention, the terms "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, article or device including the element.

[0040] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0041] In the description of this specification, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0042] With the rapid development of renewable energy, new energy vehicles have become an important category of vehicles because of their ability to balance renewable energy and reduce pollution. However, the power unit of new energy vehicles is a battery system, and the driving of the vehicle depends on the charging and discharging process of the battery system. Generally speaking, a battery system includes multiple cells. Due to reasons such as cell aging, overcharging and over-discharging, there are also certain risks in the battery charging and discharging process. Therefore, it is necessary to identify the risks of the battery during its use to reduce the risks of battery use.

[0043] In the related art, the voltage difference between the cells in the battery is obtained to identify whether the battery has risks. However, the process of judging whether the battery has risks by the voltage difference between the cells is a qualitative judgment process, which is easily affected by the subjective factors of engineers, and the obtained battery risk identification results are inaccurate. Therefore, how to accurately identify whether the battery has risks is a technical problem that needs to be solved urgently.

[0044] As described in the background technology, whether the battery is at risk is identified based on the voltage difference between the battery cells in the battery, and the accuracy of the battery risk identification result obtained according to this method is low. Based on this, the embodiments of the present application provide a battery risk identification method, device, equipment and storage medium, which calculates the voltage similarity between the median voltage time domain data and the voltage time domain data of each battery cell of the battery, and identifies whether the battery is at risk based on the voltage similarity. Therefore, because comparing the difference between each data and the median can identify potential outliers in each data, the median voltage time domain data is used as a reference for judging the voltage data of each battery cell and the overall voltage data of the battery, and the battery cells with abnormal voltage data can be accurately identified. At the same time, the qualitative identification between the median voltage time domain data and the voltage time domain data is converted into a quantitative voltage similarity value, which can reduce the influence of subjective factors and thereby improve the accuracy of the battery risk identification results.

[0045] It should be noted that the device that executes the battery risk identification method provided in the present application can be an electronic device with information processing capabilities, or a battery management system (BMS).

[0046] like Figure 1 As shown, the battery risk identification method includes S101-S103.

[0047] S101. Obtaining the median voltage time-domain data of the battery in the current time period and the voltage time-domain data of each cell of the battery.

[0048] Among them, the voltage time domain data of the battery cell is used to characterize the voltage of the battery cell at multiple moments in the current time period, and the median voltage time domain data is used to characterize the median voltage at multiple moments in the current time period. The median voltage at a moment is the median of the voltages of each battery cell of the battery at that moment.

[0049] In some embodiments, during the operation of the battery, the risk identification device of the battery can collect voltage data of each battery cell in the current time period based on a preset frequency to obtain voltage time domain data of each battery cell.

[0050] In other embodiments, after obtaining the voltage time domain data of each battery cell, the battery risk identification device can determine the voltage data of each battery cell at each moment, and determine the median voltage data of the battery according to the number of battery cells in the battery. If the number of battery cells is an even number, the median voltage data is the average value of the voltages of the two battery cells in the middle position of all battery cell voltage sorting. If the number of battery cells is an odd number, the median voltage data is the voltage of the battery cell in the middle position of all battery cell voltage sorting. The median voltage data corresponding to all moments in the current time period are combined to obtain the median voltage time domain data.

[0051] It should be understood that the time period in the embodiment of the present application can be expressed in the form of a time window, and the size of the time window can be reduced or increased according to actual conditions. By windowing the voltage time domain data, the analyzability of the voltage time domain data can be improved.

[0052] S102: Determine the voltage similarity corresponding to each battery cell in the battery based on the median voltage time domain data and the voltage time domain data of each battery cell of the battery.

[0053] The voltage similarity is used to characterize the similarity between the fluctuation of the voltage of the battery cell and the fluctuation of the median voltage.

[0054] It should be understood that the median voltage of the battery can reflect the general level and discreteness of the overall voltage of the battery. If the voltage data gap between the individual cells in the battery is too large, the median can provide a relatively stable reference point for the battery voltage. The process of identifying battery risks in this application is actually the process of detecting whether the voltage of the battery cells in the battery is abnormal, and by comparing the gap between each voltage data and the median, potential voltage anomalies can be identified. Therefore, using the median voltage time domain data as a benchmark to reflect the overall voltage of the battery can improve the accuracy of the battery risk identification results.

[0055] S103: Determine a risk identification result of the battery according to the voltage similarity corresponding to each battery cell in the battery.

[0056] It should be understood that by calculating the voltage similarity, the qualitative judgment between the voltage of each battery cell and the median voltage can be converted into a quantitative one, which can not only eliminate the influence of subjective factors on the identification results, but also improve the accuracy of the battery risk identification results.

[0057] As a possible implementation manner, the risk identification device of the battery can determine that the risk identification result of the battery is that there is a risk when the voltages corresponding to the battery cells are relatively similar.

[0058] As another possible implementation, the risk identification device for the battery may determine that the risk identification result of the battery is that there is no risk when the similarity of the voltages corresponding to the battery cells is small.

[0059] Furthermore, upon identifying that the battery is in a risky state, the risk identification device of the battery can issue a risk warning to remind the user to conduct further risk identification on the battery and determine the cause of the risk in order to process and maintain the battery. For example, if the cause is that some battery cells in the battery have poor consistency, these battery cells can be replaced and the battery risk can be identified again so that the processed battery is not in a risky state.

[0060] In the embodiment of the present application, by calculating the voltage similarity between the median voltage time domain data and the voltage time domain data of each battery cell, it is possible to identify whether the battery is at risk based on the voltage similarity. Therefore, since the difference between each data and the median can be compared to identify potential abnormal values ​​in each data, the median voltage time domain data is used as a reference for judging the voltage data of each battery cell and the overall voltage data of the battery, and the battery cell with abnormal voltage data can be accurately identified. At the same time, the qualitative identification between the median voltage time domain data and the voltage time domain data is converted into a quantitative voltage similarity value, which can reduce the influence of subjective factors and thus improve the accuracy of the battery risk identification results.

[0061] It should be noted that the above embodiment describes in detail the process of battery risk identification based on median voltage time domain data. The method of the embodiment of the present application is also applicable to methods that can reflect battery voltage, such as average voltage time domain data and standard deviation voltage time domain data.

[0062] Combination Figure 1 ,like Figure 2 As shown, in an implementation of the embodiment of the present application, the above-mentioned determination of the voltage similarity corresponding to each battery cell in the battery based on the median voltage time domain data and the voltage time domain data of each battery cell of the battery may specifically include S201-S202.

[0063] S201, converting the median voltage time domain data into the median voltage frequency domain data, and converting the voltage time domain data of each battery cell of the battery into the voltage frequency domain data of each battery cell of the battery.

[0064] As a possible implementation method, the battery risk identification device can perform Fourier transform on the median voltage time domain data and the voltage time domain data of each battery cell to obtain the median voltage frequency domain data and the voltage frequency domain data of each battery cell.

[0065] Optionally, the Fourier transform may be a discrete Fourier transform, a continuous Fourier transform, a short-time Fourier transform, an adaptive frequency modulation mode decomposition transform, a synchronous compression transform, a continuous wavelet transform, a discrete wavelet transform, etc., without specific limitation herein.

[0066] The median voltage frequency domain data and the voltage frequency domain data of each battery cell may be in the form of frequency spectrum lines.

[0067] For example, Figure 3 A schematic diagram of a frequency spectrum of a battery cell provided in an embodiment of the present application, Figure 4 A schematic diagram of a frequency spectrum of another battery cell provided in an embodiment of the present application. Figure 3 and Figure 4 It shows the change process of the voltage frequency of the battery cell over time, the horizontal axis represents the time window, and the vertical axis represents the frequency. Figure 3 As shown in the figure, the battery cell is a normal battery cell. Except for the influence of ultra-low frequency noise, the voltage and frequency of the battery cell have no large fluctuations and abnormal inversion. Figure 4 As shown in the figure, the cell is an abnormal cell. Before the 54th window, the cell voltage frequency had no abnormal fluctuations in the full frequency range. In the 54th window, the cell showed abnormal fluctuations in the frequency band less than 20Hz, which is when an abnormal signal appeared. In the 60th time window, the cell showed serious fluctuations in the full frequency range, indicating that the cell is in an abnormal state, that is, the battery is at risk. The abnormal state here can be the risk of internal short circuit in the cell.

[0068] S202: Determine the voltage similarity corresponding to each battery cell in the battery based on the median voltage frequency domain data and the voltage frequency domain data of each battery cell in the battery.

[0069] As a possible implementation method, the battery risk identification device performs Euclidean distance calculation on the median voltage frequency domain data and the voltage frequency domain data of each battery cell in the battery to obtain the voltage similarity of the battery cells.

[0070] Specifically, it can be calculated according to the following formula (1):

[0071]

[0072] in, represents the similarity of the kth battery cell corresponding to the uth time period, w u [k, i] represents the amplitude of the i-th frequency point of the k-th battery cell corresponding to the u-th time period, w u [m, i] represents the amplitude of the i-th frequency point of the median voltage corresponding to the u-th time period, and n represents the number of frequency points contained in the frequency spectrum.

[0073] In some embodiments, the frequency range for calculating the similarity may be a corresponding continuous frequency range in the voltage frequency domain data, or may be the entire frequency range, or may be multiple discontinuous frequency ranges.

[0074] It should be noted that the above embodiment uses the Euclidean distance formula as an example to illustrate the process of calculating the voltage similarity of the battery cells. Other methods that can calculate the voltage similarity can also be used to calculate the voltage similarity in the embodiment of the present application.

[0075] In the embodiment of the present application, the time domain data is converted into frequency domain data, so that the difference between normal cells and abnormal cells is significant, which facilitates the identification of abnormal cells. By calculating the voltage similarity corresponding to each cell, the qualitative identification between the median voltage time domain data and the voltage time domain data is converted into a quantitative voltage similarity value, which facilitates the battery risk identification device to identify the battery risk.

[0076] In one implementation of the embodiment of the present application, in combination Figure 1 ,like Figure 5 As shown, the above-mentioned determination of the risk identification result of the battery according to the voltage similarity may specifically include S501 and S502.

[0077] S501: When the voltage similarities corresponding to the respective battery cells are less than or equal to a first similarity threshold, determine that a risk identification result of the battery is that there is no risk.

[0078] It can be understood that when the voltage similarities corresponding to each battery cell are less than or equal to the first similarity threshold, it means that the voltage data of each battery cell of the battery is not much different from the median voltage data, that is, the voltage data of each battery cell is not much different. At this time, it can be determined that the risk identification result of the battery is that there is no risk.

[0079] The first similarity threshold is a preset value, which may be three times the standard deviation of the voltage similarity of all the cells in the current time period.

[0080] It should be noted that the first similarity threshold may also be an adaptive threshold. Since each battery has different corresponding operating conditions during operation, the first similarity threshold may be adjusted according to the operating conditions, historical data and engineer's experience.

[0081] It should be understood that the process of determining whether a battery is at risk is a process of horizontally comparing the voltage similarities between the cells in the battery, so the first similarity threshold should be determined based on the voltage similarities of all cells in the current time period of the battery.

[0082] S502: When there is a battery cell whose corresponding voltage similarity is greater than a first similarity threshold, determine that a risk identification result of the battery is that there is a risk.

[0083] It can be understood that when there are battery cells whose corresponding voltage similarity is greater than the first similarity threshold, it means that the voltage data of one or some battery cells of the battery are very different from the median voltage data. At this time, it can be determined that the risk identification result of the battery is risky.

[0084] Exemplarily, the time period is in the form of a time window. Figure 6 A schematic diagram of voltage similarity provided in an embodiment of the present application. Figure 6As shown, the voltage similarity of the battery cell fluctuates very little in the first 40 time windows, and remains at around 100. If the current time window is the 20th time window, it can be seen that the voltage similarity of the battery cell is around 100, which is less than the first similarity threshold, so the risk identification result of the battery is no risk. If the current time window is the 40th time window, it can be seen that the voltage similarity of the battery cell is 14000, which is greater than the first similarity threshold, so the risk identification result of the battery is that there is a risk.

[0085] In one implementation of the embodiment of the present application, in combination Figure 1 ,like Figure 7 As shown, after determining that the risk identification result of the battery is that there is a risk, the risk type of the battery cell in the battery may also be determined, which may specifically include S701 and S702.

[0086] S701. Obtain voltage similarity of a target battery cell in a historical time period.

[0087] The target battery cell is a battery cell whose voltage similarity in the current time period is greater than a first similarity threshold.

[0088] It should be noted that, to obtain the voltage similarity of the target battery cell in the historical time period, reference may be made to the above-mentioned process of obtaining the voltage similarity of each battery cell in the current time period, which will not be repeated here.

[0089] S702: Determine the risk type corresponding to the target battery cell based on the voltage similarity of the target battery cell in the historical time period.

[0090] As a possible implementation manner, when the voltage similarity of the target battery cell in the historical time period is less than or equal to the second similarity threshold, the battery risk identification device determines that the risk type corresponding to the target battery cell is poor consistency.

[0091] As another possible implementation, when the voltage similarity of the target battery cell in the historical time period is greater than a second similarity threshold, the risk identification device of the battery determines that the risk type corresponding to the target battery cell is an internal short circuit risk.

[0092] For example, Figure 6 As shown, in the 66th time window, the voltage similarity of the battery cell is 17800, which is obviously greater than the first similarity threshold. In the 40th time window, the voltage similarity of the battery cell is 14000, which is greater than the second similarity threshold. The risk type corresponding to the battery cell is internal short circuit risk.

[0093] Among them, the above-mentioned second similarity threshold is determined according to the voltage similarity of the target battery cell in multiple historical time periods. Specifically, the second similarity threshold can be 3 times the standard deviation of the voltage similarity of the battery cell in all historical windows. It should be noted that the second similarity threshold is similar to the first similarity threshold, and the second similarity threshold can also be an adaptive threshold. Since the operating conditions corresponding to each battery during operation are different, the second similarity threshold can be adjusted according to the operating conditions, historical data and the experience of engineers.

[0094] It should be understood that different battery cells have different performances. In the event of a risk, the voltage performance of each battery cell is also different. Therefore, the risk type corresponding to the battery cell is determined based on the voltage similarity of the battery cell in different time periods. This is a process of longitudinal comparison, so the second similarity threshold should be determined based on the voltage performance of the battery cell in the historical time period.

[0095] Furthermore, after determining the risk type corresponding to the target battery cell, the battery risk identification device can also issue a risk warning, and each risk type corresponds to a risk warning method, so that the user can obtain the risk type according to the risk warning. Among them, the risk warning method is not specifically limited here.

[0096] The embodiment of the present application can accurately determine the risk type of the target battery cell in the battery based on the voltage similarity between the current time period and the historical time period, and can further accurately identify the normal battery cells, battery cells with poor consistency and battery cells with internal short risk in the battery, making it easier for engineers to process the battery to reduce the risk of battery use.

[0097] After identifying battery risks, cells with poor consistency and cells with internal short circuit risks can be replaced or otherwise processed to reduce the risks of battery use.

[0098] The following is an example to illustrate the battery risk identification method provided in an embodiment of the present application.

[0099] For example, Figure 8 As shown, the battery risk identification method includes S801-S809.

[0100] S801. Obtain voltage data of all battery cells according to a preset collection frequency.

[0101] S802. Divide the voltage time domain data into multiple time windows according to a preset window division principle to obtain voltage time domain data.

[0102] It should be noted that the time window size can be reduced or increased according to actual conditions.

[0103] S803: Calculate the median voltage time domain data corresponding to all cells in the current time window.

[0104] S804 , converting the voltage time domain data and median voltage time domain data of all cells in the current time window into voltage frequency domain data and median voltage frequency domain data through Fourier transformation.

[0105] It should be understood that the Fourier transform can be discrete Fourier transform, continuous Fourier transform, short-time Fourier transform, adaptive frequency modulation mode decomposition transform, synchronous compression transform, continuous wavelet transform, discrete wavelet transform, etc., without specific limitation here.

[0106] S805: Calculate the voltage similarity between the voltage frequency domain data of all the cells in the time window and the median voltage frequency domain data.

[0107] S806 , determining whether there is a cell whose voltage similarity exceeds a first similarity threshold within the current time window.

[0108] When the voltage similarity of all the cells is less than or equal to the first similarity threshold, step S801 is executed and the process is repeated.

[0109] In the case that the voltage similarity of the battery cells is greater than the first similarity threshold, step S807 is executed.

[0110] S807 . For all cells whose voltage similarities exceed the first similarity threshold in the current time window, determine whether the cell similarities exceed the second similarity threshold in the historical time window.

[0111] Optionally, when the voltage similarity is less than or equal to the second similarity threshold, step S808 is performed.

[0112] Optionally, when the voltage similarity is greater than a second similarity threshold, step S809 is performed.

[0113] S808. For all battery cells whose current time window threshold exceeds the first similarity threshold and does not exceed the second similarity threshold in the historical time window, the system triggers a poor consistency warning for the battery cells.

[0114] S809. For all battery cells whose current time window threshold exceeds the first similarity threshold and whose historical time window threshold exceeds the second similarity threshold, the system triggers an internal short circuit risk alarm for the battery cells.

[0115] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to achieve the above functions, the battery risk identification device includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0116] The embodiment of the present application can divide the battery risk identification device into functional modules according to the above method. For example, the battery risk identification device can include functional modules corresponding to the functional divisions, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0117] Fig. 9 A possible structural diagram of the battery risk identification device involved in the above embodiment is shown. Fig. 9 The battery risk identification device 900 includes: a first acquisition unit 901 and a processing unit 902.

[0118] The first acquisition unit 901 is used to obtain the median voltage time domain data of the battery in the current time period, and the voltage time domain data of each battery cell of the battery; the voltage time domain data of the battery cell is used to characterize the voltage of the battery cell at multiple moments in the current time period, and the median voltage time domain data is used to characterize the median voltage at multiple moments in the current time period. The median voltage at a moment is the median of the voltages of each battery cell of the battery at that moment.

[0119] The processing unit 902 is used to determine the voltage similarity corresponding to each battery cell in the battery based on the median voltage time domain data and the voltage time domain data of each battery cell of the battery. The voltage similarity is used to characterize the similarity between the fluctuation of the voltage of the battery cell and the fluctuation of the median voltage.

[0120] The processing unit 902 is further configured to determine a risk identification result of the battery according to the voltage similarities corresponding to the respective cells in the battery.

[0121] In some embodiments, the processing unit 902 is specifically used to convert the median voltage time domain data into the median voltage frequency domain data, and to convert the voltage time domain data of each battery cell of the battery into the voltage frequency domain data of each battery cell of the battery; based on the median voltage frequency domain data and the voltage frequency domain data of each battery cell of the battery, determine the voltage similarity corresponding to each battery cell in the battery.

[0122] In some embodiments, the processing unit 902 is specifically configured to perform Euclidean distance calculation on the median voltage frequency domain data and the voltage frequency domain data of the battery cells for each battery cell in the battery to obtain the voltage similarity of the battery cells.

[0123] In some embodiments, the above-mentioned processing unit 902 is specifically used to determine that the risk identification result of the battery is that there is no risk when the voltage similarities corresponding to each battery cell are less than or equal to the first similarity threshold; or, when there is a battery cell whose voltage similarity corresponding to the battery cell is greater than the first similarity threshold, determine that the risk identification result of the battery is that there is a risk.

[0124] In some embodiments, the risk identification device of the above-mentioned battery also includes: a second acquisition unit and a determination unit; the above-mentioned second acquisition unit is used to obtain the voltage similarity of the target battery cell in the historical time period, and the target battery cell is a battery cell whose voltage similarity in the current time period is greater than the first similarity threshold; the above-mentioned determination unit is used to determine the risk type corresponding to the target battery cell based on the voltage similarity of the target battery cell in the historical time period.

[0125] In some embodiments, the above-mentioned determination unit is specifically used to determine that the risk type corresponding to the target battery cell is poor consistency when the voltage similarity of the target battery cell in the historical time period is less than or equal to the second similarity threshold; or, when the voltage similarity of the target battery cell in the historical time period is greater than the second similarity threshold, determine that the risk type corresponding to the target battery cell is internal short circuit risk.

[0126] In some embodiments, the second similarity threshold is determined according to voltage similarities of the target battery cell in multiple historical time periods.

[0127] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0128] Fig.10 FIG. 2 shows a schematic diagram of the structure of the equipment involved in the above embodiment. Fig.10 As shown, the device 1000 includes but is not limited to: a processor 1001 and a memory 1002.

[0129] The memory 1002 is used to store executable instructions of the processor 1001. It can be understood that the processor 1001 is configured to execute instructions to implement the group fault detection method in the above embodiment.

[0130] It should be noted that those skilled in the art can understand that Fig.10 The device structure shown in the figure does not constitute a limitation of the device, and the device may include Fig.10 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0131] The processor 1001 is the control center of the device. It uses various interfaces and lines to connect various parts of the entire device. By running or executing software programs and / or modules stored in the memory 1002 and calling data stored in the memory 1002, it executes various functions of the device and processes data, thereby monitoring the device as a whole. The processor 1001 may include one or more processing units. Optionally, the processor 1001 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 1001.

[0132] The memory 1002 may be used to store software programs and various data. The memory 1002 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one functional module (such as a determination unit, a processing unit, etc.), etc. In addition, the memory 1002 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0133] In an exemplary embodiment, the present application also provides a vehicle, comprising the above-mentioned device.

[0134] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 1002 including instructions, and the above instructions can be executed by the processor 1001 of the device 1000 to implement the method in the above embodiment.

[0135] In actual implementation, Fig. 9 The first acquisition unit 901 and the processing unit 902 in the embodiment can both be composed of Fig.10 The processor 1001 in the embodiment calls the computer program stored in the memory 1002 to implement. The specific execution process can refer to the description of the method part in the above embodiment, which will not be repeated here.

[0136] Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0137] In an exemplary embodiment, the present application also provides a computer program product including one or more instructions, and the one or more instructions can be executed by the processor 1001 of the device to complete the method in the above embodiment.

[0138] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the device, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect as the above-mentioned method can be achieved. To avoid repetition, they will not be repeated here.

[0139] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0140] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0142] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0143] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or CD and other media that can store program code.

[0144] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A battery risk identification method, characterized in that: include: Obtaining the median voltage time domain data of the battery in the current time period, and the voltage time domain data of each battery cell of the battery; the voltage time domain data of the battery cell is used to characterize the voltage of the battery cell at multiple moments in the current time period, and the median voltage time domain data is used to characterize the median voltage at multiple moments in the current time period, and the median voltage at a moment is the median of the voltages of each battery cell of the battery at that moment; Based on the median voltage time domain data and the voltage time domain data of each battery cell of the battery, determine the voltage similarity corresponding to each battery cell in the battery, wherein the voltage similarity is used to characterize the similarity between the fluctuation of the voltage of the battery cell and the fluctuation of the median voltage; A risk identification result of the battery is determined according to the voltage similarities corresponding to the respective battery cells in the battery.

2. The method according to claim 1, characterized in that The determining, based on the median voltage time-domain data and the voltage time-domain data of each battery cell of the battery, the voltage similarity corresponding to each battery cell in the battery includes: Converting the median voltage time domain data into median voltage frequency domain data, and converting the voltage time domain data of each battery cell of the battery into voltage frequency domain data of each battery cell of the battery; Based on the median voltage frequency domain data and the voltage frequency domain data of each battery cell of the battery, the voltage similarity corresponding to each battery cell in the battery is determined.

3. The method according to claim 2, characterized in that The determining, based on the median voltage frequency domain data and the voltage frequency domain data of each battery cell of the battery, the voltage similarity corresponding to each battery cell in the battery includes: For each battery cell in the battery, Euclidean distance calculation is performed on the median voltage frequency domain data and the voltage frequency domain data of the battery cell to obtain the voltage similarity of the battery cell.

4. The method according to claim 1, characterized in that The step of determining the risk identification result of the battery according to the voltage similarities corresponding to the respective cells in the battery includes: In a case where the voltage similarities corresponding to the respective battery cells are less than or equal to the first similarity threshold, determining that the risk identification result of the battery is that there is no risk; or, In the case that the voltage similarity corresponding to the existence of a battery cell is greater than the first similarity threshold, it is determined that the risk identification result of the battery is that there is a risk.

5. The method according to claim 4, characterized in that After determining that the risk identification result of the battery is that there is a risk, the method further includes: Obtaining voltage similarity of a target battery cell in a historical time period, wherein the target battery cell is a battery cell whose voltage similarity in a current time period is greater than a first similarity threshold; Based on the voltage similarity of the target battery cell in a historical time period, a risk type corresponding to the target battery cell is determined.

6. The method according to claim 5, characterized in that The determining, based on the voltage similarity of the target battery cell in a historical time period, a risk type corresponding to the target battery cell includes: When the voltage similarity of the target battery cell in the historical time period is less than or equal to the second similarity threshold, determining that the risk type corresponding to the target battery cell is poor consistency; or, When the voltage similarity of the target battery cell in the historical time period is greater than the second similarity threshold, it is determined that the risk type corresponding to the target battery cell is an internal short circuit risk.

7. The method according to claim 6, characterized in that The second similarity threshold is determined according to voltage similarities of the target battery cell in multiple historical time periods.

8. A battery risk identification device, characterized in that: It includes a first acquisition unit and a processing unit: The first acquisition unit is used to acquire the median voltage time domain data of the battery in the current time period, and the voltage time domain data of each battery cell of the battery; the voltage time domain data of the battery cell is used to characterize the voltage of the battery cell at multiple moments in the current time period, and the median voltage time domain data is used to characterize the median voltage at multiple moments in the current time period, and the median voltage at a moment is the median of the voltages of each battery cell of the battery at that moment; The processing unit is used to determine the voltage similarity corresponding to each battery cell in the battery based on the median voltage time domain data and the voltage time domain data of each battery cell of the battery, wherein the voltage similarity is used to characterize the similarity between the fluctuation of the voltage of the battery cell and the fluctuation of the median voltage; The processing unit is further used to determine the risk identification result of the battery according to the voltage similarity corresponding to each battery cell in the battery.

9. A device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 7.

10. A vehicle, characterized in that: Comprising the electronic device as claimed in claim 9.

11. A computer-readable storage medium, characterized in that: When the computer-executable instructions stored in the computer-readable storage medium are executed by a processor of a device, the device is capable of performing the method according to any one of claims 1 to 7.

12. A computer program product, comprising computer instructions, characterized in that: When the computer instructions are executed on a processor of a device, the device is enabled to perform the method according to any one of claims 1 to 7.

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