Battery safety detection method, device, apparatus and storage medium

Abstract

The application discloses a battery safety detection method and device, equipment and storage medium, relates to the lithium battery detection technical field, and particularly relates to a battery safety detection method, device, equipment and storage medium. The battery safety detection method is applied to an electric vehicle, and the battery safety detection method comprises the following steps: reading time sequence data of the electric vehicle during charging to obtain initial time sequence data, performing change detection on the initial time sequence data to obtain a change detection result, resampling in the initial time sequence data according to the change detection result to obtain target time sequence data, performing fluctuation rate calculation processing according to the target time sequence data to obtain a target fluctuation rate, performing battery safety identification according to the target fluctuation rate to obtain a battery safety detection result. The application can perform safety diagnosis by acquiring the time sequence data of the electric vehicle during charging, and reduces the implementation difficulty of battery safety diagnosis.

Description

Technical Field

[0001] This invention relates to the field of lithium battery testing technology, and in particular to a battery safety testing method, apparatus, equipment, and storage medium. Background Technology

[0002] Currently, frequent safety accidents involving vehicles using lithium-ion batteries as their power source have become a major pain point in the promotion and use of electric vehicles. In terms of battery safety diagnosis, existing practices typically involve building diagnostic models or methods using big data. However, these models are structurally complex and costly to build. Furthermore, in practical applications, complete data from all individual cells in the battery system is required, resulting in a massive data volume, numerous constraints, and high data quality requirements. This necessitates the addition of more data acquisition devices to obtain data, increasing the difficulty of implementation. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a battery safety detection method that can perform safety diagnosis by acquiring time-series data during electric vehicle charging, thereby reducing the difficulty of implementing battery safety diagnosis.

[0004] The present invention also proposes a battery safety testing device.

[0005] The present invention also proposes a battery safety testing device.

[0006] The present invention also proposes a computer-readable storage medium.

[0007] In a first aspect, one embodiment of the present invention provides a battery safety testing method applied to electric vehicles, the battery safety testing method comprising:

[0008] Read the timing data of the electric vehicle during charging to obtain the initial timing data;

[0009] Change detection is performed on the initial time-series data to obtain change detection results;

[0010] Based on the change detection results, the initial time series data is resampled to obtain the target time series data;

[0011] The target volatility is obtained by performing volatility calculation processing based on the target time series data.

[0012] Battery safety is identified based on the target volatility, and battery safety test results are obtained.

[0013] The battery safety detection method of this invention has at least the following beneficial effects: During electric vehicle charging, the electric vehicle is monitored in real time, and its time-series data during charging is read to obtain initial time-series data. Corresponding data is then filtered from the initial time-series data for change detection to identify changes in the initial time-series data, obtaining change detection results. Based on the change detection results, the initial time-series data is resampled to resample initial time-series data that meets the conditions, obtaining target time-series data. The volatility of each target time-series data point is calculated to obtain the target volatility corresponding to the target time-series data. Battery safety is identified based on the target volatility, resulting in a battery safety detection result. By acquiring the initial time-series data of the electric vehicle during charging, detecting changes in the initial time-series data, obtaining change detection results, resampling the target time-series data based on the change detection results, calculating the target volatility of the target time-series data, and performing safety identification based on the target volatility to obtain a battery safety detection result, the method enables safety diagnosis by acquiring the time-series data of the electric vehicle during charging, reducing the implementation difficulty of battery safety diagnosis.

[0014] According to other embodiments of the battery safety detection method of the present invention, the initial timing data includes battery stack voltage data and individual cell voltage data, wherein the battery stack voltage data is the voltage data of the battery stack, and the individual cell voltage data is the voltage data of the individual cells in the battery stack. The step of reading the timing data of the electric vehicle during charging to obtain the initial timing data includes:

[0015] The voltage output by the battery stack during charging is collected to obtain the battery stack voltage, and the battery stack voltage is converted into voltage data to obtain battery stack voltage data;

[0016] The highest voltage output by the individual battery during charging is collected to obtain the individual battery voltage, and the individual battery voltage is converted into voltage data to obtain individual battery voltage data;

[0017] The battery stack voltage data and the individual cell voltage data are stored in a pre-established data set to obtain the initial time series data.

[0018] According to other embodiments of the battery safety detection method of the present invention, the step of performing change detection on the initial time-series data to obtain change detection results includes:

[0019] Obtain the single cell voltage data from the previous moment to obtain the first detected voltage data;

[0020] Obtain the current voltage data of the single cell to obtain the second detected voltage data;

[0021] The first detection voltage data and the second detection voltage data are compared to obtain the change detection result.

[0022] According to other embodiments of the battery safety detection method of the present invention, the step of comparing the first detection voltage data and the second detection voltage data to obtain the change detection result includes:

[0023] The first and second detected voltage data are input into a preset data change model for data change calculation and processing to obtain the change data.

[0024] The change data is compared with a preset change threshold to obtain the change detection result.

[0025] According to other embodiments of the battery safety detection method of the present invention, the target time series data includes total target time series data, the target volatility includes a first target volatility, and the step of performing volatility calculation processing based on the target time series data to obtain the target volatility includes:

[0026] Obtain the total target time series data at the current moment to obtain the first target time series data;

[0027] Obtain the total target time series data at the next time step to obtain the second target time series data;

[0028] The first target time series data and the second target time series data are input into a preset volatility model for volatility calculation to obtain the first target volatility.

[0029] According to other embodiments of the battery safety detection method of the present invention, the target time series data includes single-cell target time series data, the target volatility includes a second target volatility, and the step of performing volatility calculation processing based on the target time series data to obtain the target volatility further includes:

[0030] Obtain the time series data of the single target at the current moment to obtain the time series data of the third target;

[0031] Obtain the time series data of the single target at the next moment to obtain the time series data of the fourth target;

[0032] The third target time series data and the fourth target time series data are input into the volatility model for volatility calculation to obtain the second target volatility.

[0033] According to other embodiments of the battery safety detection method of the present invention, the battery safety detection result includes battery abnormal signals and battery normal signals, and the step of performing battery safety identification based on the target volatility to obtain the battery safety detection result includes:

[0034] The first target volatility and the second target volatility are input into a preset median smoothing model for median filtering to obtain the median volatility;

[0035] The median volatility is compared with a preset median threshold to obtain the median comparison result;

[0036] If the median comparison result indicates a large difference, then the battery abnormality signal is obtained;

[0037] If the median comparison result indicates a small difference, then the battery is considered to have a normal signal.

[0038] Secondly, one embodiment of the present invention provides a battery safety detection device for use in electric vehicles, the battery safety detection device comprising:

[0039] The initial data reading module is used to read the timing data of the electric vehicle during charging to obtain the initial timing data.

[0040] The data change detection module is used to perform change detection on the initial time-series data and obtain the change detection result;

[0041] The data resampling module is used to resample the initial time series data according to the change detection result to obtain the target time series data;

[0042] The volatility calculation module is used to perform volatility calculation processing based on the target time series data to obtain the target volatility.

[0043] The battery safety identification module is used to identify battery safety based on the target volatility and obtain battery safety detection results.

[0044] The battery safety detection device of this invention has at least the following beneficial effects: During electric vehicle charging, the initial data reading module performs real-time detection on the electric vehicle and reads the time-series data of the electric vehicle during charging to obtain initial time-series data. The data change detection module filters corresponding data in the initial time-series data for change detection to detect changes in the initial time-series data and obtains change detection results. The data resampling module resamples the initial time-series data according to the change detection results to resample initial time-series data that meets the conditions, obtaining target time-series data. The volatility calculation module calculates the volatility of each target time-series data point to obtain the target volatility corresponding to the target time-series data. The battery safety identification module identifies the battery safety based on the target volatility and obtains the battery safety detection result. By acquiring the initial time-series data of the electric vehicle during charging, detecting changes in the initial time-series data, obtaining change detection results, resampling the target time-series data based on the change detection results, calculating the target volatility of the target time-series data, and performing safety identification based on the target volatility to obtain the battery safety detection result, safety diagnosis can be performed by acquiring the time-series data of the electric vehicle during charging, reducing the implementation difficulty of battery safety diagnosis.

[0045] Thirdly, one embodiment of the present invention provides a battery safety testing device, comprising:

[0046] At least one processor, and,

[0047] A memory communicatively connected to the at least one processor; wherein,

[0048] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the battery safety detection method as described in the first aspect.

[0049] Fourthly, one embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the battery safety detection method as described in the first aspect.

[0050] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description and the accompanying drawings. Attached Figure Description

[0051] Figure 1 This is a schematic flowchart of a specific embodiment of the battery safety detection method in this invention;

[0052] Figure 2yes Figure 1 A schematic flowchart of a specific embodiment of step S101;

[0053] Figure 3 yes Figure 1 A schematic flowchart of a specific embodiment of step S102;

[0054] Figure 4 yes Figure 3 A schematic diagram of a specific embodiment of step S303;

[0055] Figure 5 yes Figure 1 A schematic flowchart of a specific embodiment of step S104;

[0056] Figure 6 yes Figure 1 A schematic diagram of another specific embodiment of step S104;

[0057] Figure 7 yes Figure 1 A schematic diagram of a specific embodiment of step S105;

[0058] Figure 8 This is a module block diagram of a specific embodiment of the battery safety detection device in this invention;

[0059] Figure 9 This is a module block diagram of another specific embodiment of the battery safety detection device in this invention;

[0060] Figure 10 This is a schematic diagram of a specific embodiment of volatility difference in the present invention.

[0061] Explanation of reference numerals in the attached figures:

[0062] Initial data reading module 801, data change detection module 802, data resampling module 803, volatility calculation module 804, and battery safety identification module 805. Detailed Implementation

[0063] The following will describe the concept and technical effects of the present invention clearly and completely with reference to embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

[0064] In the description of this invention, if directional descriptions are involved, such as "up," "down," "front," "back," "left," "right," etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, it is only for the convenience of describing the invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. If a feature is referred to as "set," "fixed," "connected," or "installed" on another feature, it can be directly set, fixed, or connected to the other feature, or it can be indirectly set, fixed, connected, or installed on the other feature.

[0065] In the description of the embodiments of the present invention, the term "several" means one or more, and the term "multiple" means two or more. The terms "greater than," "less than," and "exceeding" should be understood as excluding the stated number, while the terms "above," "below," and "within" should be understood as including the stated number. The terms "first" and "second" should be understood as distinguishing technical features, and not as indicating or implying relative importance, the number of indicated technical features, or the order of the indicated technical features.

[0066] Currently, frequent safety accidents involving vehicles using lithium-ion batteries as their power source have become a major pain point in the promotion and use of electric vehicles. In terms of battery safety diagnosis, existing practices typically involve building diagnostic models or methods using big data. However, these models are structurally complex and costly to build. Furthermore, in practical applications, complete data from all individual cells in the battery system is required, resulting in a massive data volume, numerous constraints, and high data quality requirements. This necessitates the addition of more data acquisition devices to obtain data, increasing the difficulty of implementation.

[0067] Existing battery safety warning systems used in the battery field mostly employ complex algorithm models and system architectures. These models and architectures are difficult and costly to implement, require large amounts of data with high dimensionality, and are only applicable to cloud systems, necessitating comprehensive battery data. Most importantly, existing methods and systems are largely inapplicable to the most prevalent charging scenarios in the market, as charging systems can only obtain the total voltage and highest single-cell voltage of electric vehicle batteries.

[0068] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a battery safety detection method, particularly suitable for situations where only the total battery voltage and the highest single-cell voltage can be collected. It enables safety diagnosis by acquiring time-series data during electric vehicle charging, thus reducing the difficulty of implementing battery safety diagnosis.

[0069] Please refer to Figure 1 , Figure 1 A schematic flowchart of a battery safety detection method according to an embodiment of the present invention is shown. In some embodiments, the battery safety detection method may include, but is not limited to, steps S101 to S105.

[0070] Step S101: Read the timing data of the electric vehicle during charging to obtain the initial timing data;

[0071] Step S102: Perform change detection on the initial time series data to obtain the change detection results;

[0072] Step S103: Resample the initial time series data based on the change detection results to obtain the target time series data;

[0073] Step S104: Perform volatility calculation processing based on the target time series data to obtain the target volatility;

[0074] Step S105: Perform battery safety identification based on the target volatility, obtain the battery safety detection result, obtain the current internal resistance of the battery, and obtain the target battery internal resistance.

[0075] Steps S101 to S105 of this embodiment involve real-time monitoring of the electric vehicle during charging, reading its time-series data to obtain initial time-series data, filtering relevant data within the initial time-series data for change detection to identify changes in the initial time-series data, obtaining change detection results, resampling the initial time-series data based on the change detection results to resample initial time-series data that meets the conditions, obtaining target time-series data, calculating the volatility of each target time-series data point to obtain the target volatility, and identifying battery safety based on the target volatility to obtain battery safety detection results. By acquiring the initial time-series data of the electric vehicle during charging, detecting changes in the initial time-series data, obtaining change detection results, resampling the target time-series data based on the change detection results, calculating the target volatility of the target time-series data, and performing safety identification based on the target volatility to obtain battery safety detection results, safety diagnosis can be performed by acquiring the time-series data of the electric vehicle during charging, reducing the implementation difficulty of battery safety diagnosis.

[0076] Please refer to Figure 2 , Figure 2 A schematic flowchart of a battery safety detection method according to an embodiment of the present invention is shown. In some embodiments, the initial timing data includes battery stack voltage data and individual cell voltage data. The battery stack voltage data is the voltage data of the battery stack, and the individual cell voltage data is the voltage data of the individual cell. Reading the timing data of the electric vehicle during charging to obtain the initial timing data may include, but is not limited to, steps S201 to S203.

[0077] Step S201: Collect the voltage output by the battery stack during charging to obtain the battery stack voltage, and convert the battery stack voltage into voltage data to obtain battery stack voltage data;

[0078] Step S202: Collect the highest voltage output of a single battery cell during charging to obtain the single battery cell voltage, and convert the single battery cell voltage into voltage data to obtain the single battery cell voltage data.

[0079] Step S203: Store the battery stack voltage data and individual cell voltage data into a pre-established data set to obtain initial time series data.

[0080] Steps S201 to S203 of this embodiment involve real-time detection of the voltage output from the battery stack, acquisition of the current voltage output from the battery stack, conversion of the battery stack voltage into voltage data, and real-time detection of the voltage output from individual cells, acquisition of the current voltage output from individual cells, conversion of the individual cell voltage into voltage data, and pre-establishment of a blank array set for data storage. The battery stack voltage data and individual cell voltage data are stored in this blank array set to obtain initial timing data. By acquiring the battery stack voltage data and the individual cell voltage data, and storing them as an array set to obtain initial timing data, the required voltage data can be stored for convenient subsequent retrieval.

[0081] It should be noted that battery voltage data is acquired by an externally configured charging system, BMS, or monitoring system. When charging an electric vehicle, the charging system can only collect the total output voltage of the battery stack and the highest single-cell output voltage. The battery stack voltage is the total output voltage of the battery stack, and the single-cell voltage is the highest single-cell output voltage. This allows for safety diagnostics by only acquiring the total and highest single-cell voltages, reducing the difficulty of implementing battery safety diagnostics. For example, the initial timing data D includes the battery stack voltage Vt and the single-cell voltage Vh.

[0082] In addition, after obtaining the initial timing data, the individual cell voltage data is input into the preset threshold dynamic capture device.

[0083] Please refer to Figure 3 , Figure 3A flowchart illustrating the battery safety detection method in an embodiment of the present invention is shown. In some embodiments, the change detection of initial time-series data to obtain the change detection result may include, but is not limited to, steps S301 to S303.

[0084] Step S301: Obtain the single cell voltage data from the previous moment to obtain the first detection voltage data;

[0085] Step S302: Obtain the current cell voltage data to obtain the second detection voltage data;

[0086] Step S303: Compare the first detection voltage data and the second detection voltage data to obtain the change detection result.

[0087] In steps S301 to S303 of this embodiment, the threshold dynamic capture device filters the input single-cell voltage data, filtering the single-cell voltage data input at the previous moment to obtain first detection voltage data. The threshold dynamic capture device then filters the input single-cell voltage data again, filtering the single-cell voltage data input at the current moment to obtain second detection voltage data. The first and second detection voltage data are compared to compare the changes in the single-cell voltage data at the two moments, thus obtaining a change detection result. By acquiring the single-cell voltage data from the previous and current moments, obtaining the first and second detection voltage data, and comparing them to obtain a change detection result, the change in the single-cell voltage data can be determined from the single-cell voltage data from the previous and current moments.

[0088] It should be noted that the first detected voltage data is the single cell voltage data Vh0 from the previous moment, and the second detected voltage data is the single cell voltage data Vh1 from the current moment.

[0089] Please refer to Figure 4 , Figure 4 A schematic flowchart of a battery safety detection method according to an embodiment of the present invention is shown. In some embodiments, comparing the first detection voltage data and the second detection voltage data to obtain a change detection result may include, but is not limited to, steps S401 to S402.

[0090] Step S401: Input the first detection voltage data and the second detection voltage data into a preset data change model to perform data change calculation processing to obtain change data;

[0091] Step S402: Compare the changed data with the preset change threshold to obtain the change detection result.

[0092] In steps S401 to S402 of this embodiment, the first and second detected voltage data are input into a preset data change model to calculate the data change based on the first and second detected voltage data, obtaining the change data of the single-cell voltage data at the previous and current times. The change data is then compared with a preset change threshold to obtain a change detection result. By calculating the data change of the first and second detected voltage data, obtaining the change data, and comparing the change data with the change threshold to obtain the change detection result, the stability of the voltage fluctuation of the single-cell output can be determined based on the single-cell voltage data at the previous and current times.

[0093] In step S401 of some embodiments, the ratio of the first detection voltage data to the second detection voltage data is calculated to obtain the detection voltage ratio. Then, the difference between the maximum ratio and the detection voltage ratio is calculated to obtain the change data.

[0094] It should be noted that the changes in the first detected voltage data Vh0 and the second detected voltage data Vh1 are... The change data is calculated using the following formula (1). Once the set change threshold is met, the current battery stack voltage data Vt0 and individual cell voltage data Vh0 are saved to the resampling data buffer.

[0095]

[0096] In step S103 of some embodiments, if the change detection result indicates that the changed data is greater than the change threshold, then the changed data does not meet the condition, and the current individual cell voltage data and battery stack voltage data are discarded. If the change detection result indicates that the changed data is less than the change threshold, then the changed data meets the condition, and the current individual cell voltage data and battery stack voltage data are resampled in the initial time series data, and the current individual cell voltage data and battery stack voltage data are stored to obtain the target time series data.

[0097] It should be noted that the current battery stack voltage data Vt0 and individual cell voltage data Vh0 are saved to the resampled data buffer to generate a new set of resampled two-dimensional arrays D1, which includes target time-series data. The target time-series data includes total target time-series data V1t and individual cell target time-series data V1h. The data in the new array set is a subset of the original data set, which is the valid data calculated and filtered according to the threshold dynamic capture device.

[0098] Please refer to Figure 5 , Figure 5A flowchart illustrating the battery safety detection method in an embodiment of the present invention is shown. In some embodiments, the target time series data includes total target time series data, and the target volatility includes a first target volatility. The target volatility is obtained by performing volatility calculation processing based on the target time series data, which may include, but is not limited to, steps S501 to S503.

[0099] Step S501: Obtain the total target time series data at the current moment to obtain the first target time series data;

[0100] Step S502: Obtain the total target time series data for the next time step to obtain the second target time series data;

[0101] Step S503: Input the first target time series data and the second target time series data into the preset volatility model for volatility calculation processing to obtain the first target volatility.

[0102] Steps S501 to S503, as illustrated in this embodiment, involve real-time acquisition of the total target time series data to obtain the current time series data, resulting in first target time series data. Real-time acquisition is then performed again to obtain the next time series data, resulting in second target time series data. The first and second target time series data are input into a preset volatility model to calculate volatility based on the first and second target time series data, thus obtaining the first target volatility corresponding to the total target time series data. By acquiring the total target time series data at the current and next times, obtaining the first and second target time series data, and calculating volatility based on the first and second target time series data, the first target volatility can be obtained. This allows for the calculation of data volatility using the total target time series data at the current and next times.

[0103] Please refer to Figure 6 , Figure 6 A schematic flowchart of a battery safety detection method according to an embodiment of the present invention is shown. In some embodiments, the target time series data includes single-cell target time series data, the target volatility includes a first target volatility, and the volatility calculation processing based on the target time series data to obtain the target volatility may also include, but is not limited to, steps S601 to S603.

[0104] Step S601: Obtain the time series data of the single target at the current moment to obtain the time series data of the third target;

[0105] Step S602: Obtain the time series data of the single target at the next moment to obtain the time series data of the fourth target;

[0106] Step S603: Input the third target time series data and the fourth target time series data into the volatility model for volatility calculation to obtain the second target volatility.

[0107] Steps S601 to S603, as illustrated in this embodiment, involve real-time acquisition of individual target time-series data to obtain the current time-series data of the individual target, resulting in third target time-series data. Real-time acquisition is then performed again to obtain the next time-series data of the individual target, resulting in fourth target time-series data. The third and fourth target time-series data are input into a preset volatility model to calculate volatility based on the third and fourth target time-series data, thus obtaining the second target volatility corresponding to the individual target time-series data. By acquiring the current and next time-series data of the individual target, obtaining the third and fourth target time-series data, and calculating volatility based on the third and fourth target time-series data to obtain the second target volatility, the volatility of the data can be calculated using the individual target time-series data from the current and next time-series data.

[0108] It should be noted that the resampled two-dimensional array D1 is fed into the volatility calculator to calculate the volatility Wt1 of the total target time series data V1t and the volatility Wh1 of the individual target time series data V1h, respectively. The volatility calculation formula is as follows: Formula (2):

[0109]

[0110] Where Vnh represents the time series data of a single target at time tn, and Vnt represents the time series data of the total target at time tn.

[0111] Please refer to Figure 7 , Figure 7 A flowchart illustrating the battery safety detection method in an embodiment of the present invention is shown. In some embodiments, the battery safety detection result includes an abnormal battery signal and a normal battery signal. Battery safety identification is performed based on the target volatility to obtain the battery safety detection result, which may include, but is not limited to, steps S701 to S704.

[0112] Step S701: Input the first target volatility and the second target volatility into the preset median smoothing model for median filtering to obtain the median volatility;

[0113] Step S702: Compare the median volatility with the preset median threshold to obtain the median comparison result;

[0114] Step S703: If the median comparison result indicates a large difference, then a battery abnormality signal is obtained;

[0115] Step S704: If the median comparison result indicates a small difference, then a normal battery signal is obtained.

[0116] In the embodiments of this application, steps S701 to S704 involve inputting a first target volatility and a second target volatility into a preset median smoothing model. Median filtering is then applied to the first and second target volatilitys using a smoothing algorithm set within the median smoothing model to obtain a median volatility. This median volatility is then compared numerically with a preset median threshold to obtain a median comparison result representing the magnitude of the volatility difference. If the median comparison result indicates a large volatility difference, a battery abnormality signal is obtained; if the median comparison result indicates a small volatility difference, a battery normality signal is obtained. By obtaining the median volatility of the first and second target volatilitys through median filtering, judging the difference between the median volatility and the preset median threshold, obtaining a median comparison result, and then obtaining the corresponding battery safety detection result based on the median comparison result, accurate battery safety detection results can be obtained by judging the difference between the first and second target volatilitys.

[0117] It should be noted that, referring to Figure 10 , Figure 10 A schematic diagram of volatility differences in an embodiment of the present invention is shown. Figure 10 This illustrates a situation where the difference between the first target volatility and the second target volatility is significant.

[0118] The median smoothing model assumes a one-dimensional sequence f1, f2, ..., fn, and a window with a window length (number of points) of m (m is an odd number). Median filtering is applied to the first and second target volatility by successively extracting m numbers from the first and second target volatility, and then performing median filtering on these m numbers to obtain fi-v, ..., fi-1, fi, fi+1, ..., fi+v (where fi is the center value of the window, and v = (m-1) / 2). The m numbers obtained after filtering are then sorted in numerical order, and the center value of the m numbers is taken as the filtered output. The formula (3) for the median smoothing model is expressed as:

[0119] Yi=Med{fi-v,…,fi-1,fi,fi+1,…,fi+v} i∈N v=(m-1) / 2 (3)

[0120] In addition, this application also discloses a battery safety detection device, please refer to... Figure 8 , Figure 8This invention discloses a module block diagram of a battery safety testing device according to an embodiment of the present invention. The battery safety testing device is applied to electric vehicles and can implement the aforementioned battery safety testing method. The battery safety testing device includes: an initial data reading module 801, a data change detection module 802, a data resampling module 803, a volatility calculation module 804, and a battery safety identification module 805. The initial data reading module 801, the data change detection module 802, the data resampling module 803, the volatility calculation module 804, and the battery safety identification module 805 are all communicatively connected.

[0121] The initial data reading module 801 reads the time-series data of the electric vehicle during charging to obtain initial time-series data. The data change detection module 802 performs change detection on the initial time-series data to obtain change detection results. The data resampling module 803 resamples the initial time-series data based on the change detection results to obtain target time-series data. The volatility calculation module 804 performs volatility calculation processing based on the target time-series data to obtain target volatility. The battery safety identification module 805 performs battery safety identification based on the target volatility to obtain battery safety detection results.

[0122] During electric vehicle charging, the initial data reading module 801 performs real-time monitoring of the electric vehicle and reads its time-series data during charging to obtain initial time-series data. The data change detection module 802 filters relevant data in the initial time-series data for change detection to detect changes in the initial time-series data and obtains change detection results. The data resampling module 803 resamples the initial time-series data based on the change detection results to resample initial time-series data that meets the conditions, obtaining target time-series data. The volatility calculation module 804 calculates the volatility of each target time-series data item to obtain the target volatility corresponding to the target time-series data. The battery safety identification module 805 identifies battery safety based on the target volatility and obtains battery safety detection results. By acquiring the initial time-series data of the electric vehicle during charging, detecting changes in the initial time-series data, obtaining change detection results, resampling the target time-series data based on the change detection results, calculating the target volatility of the target time-series data, and performing safety identification based on the target volatility to obtain battery safety detection results, safety diagnosis can be performed by acquiring the time-series data of the electric vehicle during charging, reducing the implementation difficulty of battery safety diagnosis.

[0123] The operation process of the battery safety detection device in this embodiment is specifically described above. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7The battery safety testing method steps S101 to S105, S201 to S203, S301 to S303, S401 and S402, S501 to S504, S601 to S603 and S701 to S704 are not described in detail here.

[0124] In some embodiments, refer to Figure 8 and Figure 9 , Figure 9 A block diagram of a battery safety detection device according to an embodiment of the present invention is shown. The electric vehicle includes a charging interface, and the electric vehicle charging system / BMS / monitoring system includes: an external system interface and an electric vehicle charging safety diagnostic system. The electric vehicle charging safety diagnostic system includes: a threshold dynamic capture unit, a data buffer, a volatility calculator, and a change rate follower. The system acquires battery stack voltage data Vt0 and individual cell voltage data Vh0 in real time through a threshold dynamic capture device. Change detection is performed on the battery stack voltage data Vt0 and individual cell voltage data Vh0 to obtain change detection results. Based on these results, the data is resampled and stored in a data buffer to obtain total target time-series data V1t and individual target time-series data V1h. The data buffer then inputs these two data into a volatility calculator to calculate volatility, resulting in the volatility Wt1 of the total target time-series data V1t and the volatility Wh1 of the individual target time-series data V1h. Finally, the volatility Wt1 of the total target time-series data V1t and the volatility Wh1 of the individual target time-series data V1h are input into a change rate follower for battery safety identification, yielding battery safety detection results. These results are then output to an external system interface for external system access.

[0125] Another embodiment of the present invention discloses a battery safety testing device, comprising: at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform, for example... Figure 1 Control method steps S101 to S105 Figure 2 Control method steps S201 to S203 Figure 3 Control method steps S301 to S303 Figure 4 Control method steps S401 and S402 Figure 5 Control method steps S501 to S503 Figure 6 The control method steps S601 to S603 and Figure 7The battery safety detection method in steps S701 to S704 of the control method.

[0126] Another embodiment of the present invention discloses a computer-readable storage medium, the storage medium comprising: storing computer-executable instructions for causing a computer to perform... Figure 1 Control method steps S101 to S105 Figure 2 Control method steps S201 to S203 Figure 3 Control method steps S301 to S303 Figure 4 Control method steps S401 and S402 Figure 5 Control method steps S501 to S503 Figure 6 The control method steps S601 to S603 and Figure 7 The battery safety detection method in steps S701 to S704 of the control method.

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0129] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention. Furthermore, the embodiments of the present invention and the features thereof can be combined with each other unless otherwise specified.

Claims

1. A battery safety testing method, characterized in that, The battery safety testing method, applied to electric vehicles, includes: Read the timing data of the electric vehicle during charging to obtain the initial timing data; Change detection is performed on the initial time-series data to obtain change detection results; Based on the change detection results, the initial time series data is resampled to obtain the target time series data; The target volatility is obtained by performing volatility calculation processing based on the target time series data. Battery safety is identified based on the target volatility to obtain battery safety detection results; Wherein, the target time series data includes total target time series data, the target volatility includes a first target volatility, and the step of performing volatility calculation processing based on the target time series data to obtain the target volatility includes: Obtain the total target time series data at the current moment to obtain the first target time series data; Obtain the total target time series data at the next time step to obtain the second target time series data; The first target time series data and the second target time series data are input into a preset volatility model for volatility calculation to obtain the first target volatility. Wherein, the target time series data includes individual target time series data, the target volatility also includes a second target volatility, and the step of performing volatility calculation processing based on the target time series data to obtain the target volatility further includes: Obtain the time series data of the single target at the current moment to obtain the time series data of the third target; Obtain the time series data of the single target at the next moment to obtain the time series data of the fourth target; The third target time series data and the fourth target time series data are input into the volatility model for volatility calculation to obtain the second target volatility. The battery safety detection results include abnormal battery signals and normal battery signals. The battery safety identification based on the target volatility to obtain the battery safety detection results includes: The first target volatility and the second target volatility are input into a preset median smoothing model for median filtering to obtain the median volatility; The median volatility is compared with a preset median threshold to obtain the median comparison result; If the median comparison result indicates a large difference, then the battery abnormality signal is obtained; If the median comparison result indicates a small difference, then the battery is considered to have a normal signal.

2. The battery safety testing method according to claim 1, characterized in that, The initial timing data includes battery stack voltage data and individual battery voltage data. The battery stack voltage data refers to the voltage data of the battery stack, and the individual battery voltage data refers to the voltage data of the individual battery. Reading the timing data of the electric vehicle during charging to obtain the initial timing data includes: The voltage output by the battery stack during charging is collected to obtain the battery stack voltage, and the battery stack voltage is converted into voltage data to obtain battery stack voltage data; The highest voltage output by the individual battery during charging is collected to obtain the individual battery voltage, and the individual battery voltage is converted into voltage data to obtain individual battery voltage data; The battery stack voltage data and the individual cell voltage data are stored in a pre-established data set to obtain the initial time series data.

3. The battery safety testing method according to claim 2, characterized in that, The step of performing change detection on the initial time-series data to obtain change detection results includes: Obtain the single cell voltage data from the previous moment to obtain the first detected voltage data; Obtain the current voltage data of the single cell to obtain the second detected voltage data; The first detection voltage data and the second detection voltage data are compared to obtain the change detection result.

4. The battery safety testing method according to claim 3, characterized in that, The step of comparing the first detected voltage data and the second detected voltage data to obtain the change detection result includes: The first and second detected voltage data are input into a preset data change model for data change calculation and processing to obtain the change data. The change data is compared with a preset change threshold to obtain the change detection result.

5. A battery safety testing device, characterized in that, The battery safety detection device, used in electric vehicles, includes: The initial data reading module is used to read the timing data of the electric vehicle during charging to obtain the initial timing data. The data change detection module is used to perform change detection on the initial time-series data and obtain the change detection result; The data resampling module is used to resample the initial time series data according to the change detection result to obtain the target time series data; The volatility calculation module is used to perform volatility calculation processing based on the target time series data to obtain the target volatility. A battery safety identification module is used to identify battery safety based on the target volatility and obtain battery safety detection results. Wherein, the target time series data includes total target time series data, the target volatility includes a first target volatility, and the volatility calculation module is used to perform volatility calculation processing based on the target time series data to obtain the target volatility, including: Obtain the total target time series data at the current moment to obtain the first target time series data; Obtain the total target time series data at the next time step to obtain the second target time series data; The first target time series data and the second target time series data are input into a preset volatility model for volatility calculation to obtain the first target volatility. The target time series data includes individual target time series data, and the target volatility also includes a second target volatility. The volatility calculation module is used to perform volatility calculation processing based on the target time series data to obtain the target volatility, and further includes: Obtain the time series data of the single target at the current moment to obtain the time series data of the third target; Obtain the time series data of the single target at the next moment to obtain the time series data of the fourth target; The third target time series data and the fourth target time series data are input into the volatility model for volatility calculation to obtain the second target volatility. The battery safety detection results include abnormal battery signals and normal battery signals. The battery safety identification based on the target volatility to obtain the battery safety detection results includes: The first target volatility and the second target volatility are input into a preset median smoothing model for median filtering to obtain the median volatility; The median volatility is compared with a preset median threshold to obtain the median comparison result; If the median comparison result indicates a large difference, then the battery abnormality signal is obtained; If the median comparison result indicates a small difference, then the battery is considered to have a normal signal.

6. A battery safety detection and control device, characterized in that, include: At least one processor, and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the battery safety detection method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the battery safety testing method as described in any one of claims 1 to 4.

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