Anomaly detection method and system for secondary battery

Abstract

The invention discloses an anomaly detection method and system for a secondary battery, and belongs to the technical field of battery anomaly detection, and the method comprises the steps: obtaining the charging data of a secondary battery in a charging stage, the charging data comprising voltage data representing the change of the voltage of the secondary battery along with the time in the charging process; determining standard deviation data representing the change of the standard deviation of the voltage of the secondary battery along with time in the charging process according to the voltage data; and determining the time when the voltage data of the secondary battery enters the platform period through the standard deviation data, and detecting the secondary battery based on the time when the voltage data enters the platform period. According to the invention, the standard deviation data of the voltage is determined by detecting the voltage data of the charging data, the time when the voltage data enters the platform period is determined through the standard deviation data, and the secondary battery is detected based on the time when the voltage data enters the platform period. According to the method and the device, the secondary battery is detected through the charging data of the secondary battery, which is relatively easy to obtain and has numerous samples, so that the detection difficulty is reduced, and the detection efficiency is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of battery abnormality detection, and specifically relates to a secondary battery abnormality detection method and system. Background Art

[0002] Currently, battery capacity deviation and self-discharge anomalies are detected through the low SOC (State of Charge) interval; however, this method has high requirements and requires the vehicle to be driven to a low SOC state and left stationary for more than 3 to 4 hours. Users often charge the vehicle when it is driven to a low SOC state, so there is less data that meets this operating condition, making battery detection more difficult. Summary of the invention

[0003] Purpose of the invention: This application develops a secondary battery abnormality detection method and system, aiming to solve the technical problem in the prior art that it is difficult to detect the battery status in a low SOC state.

[0004] Technical solution: In a first aspect, the present application provides a secondary battery abnormality detection method, comprising:

[0005] Acquiring charging data of the secondary battery, wherein the charging data includes voltage data, and the voltage data is used to characterize a change state of the voltage of the secondary battery over time during the charging process;

[0006] Acquire standard deviation data based on the voltage data, wherein the standard deviation data is used to characterize a change state of the standard deviation of the voltage of the secondary battery over time during the charging process;

[0007] The time when the voltage data enters a plateau period is obtained based on the standard deviation data, which is the detection time;

[0008] The secondary battery is inspected based on the inspection time.

[0009] In some embodiments, the step of obtaining the time when the voltage data enters the plateau period based on the standard deviation data includes:

[0010] Determine the rate of change of the standard deviation within a target period, and obtain the standard deviation data that meets the rate of change threshold to construct detection data;

[0011] Obtaining Gaussian distribution of the detection data;

[0012] The time when the voltage data enters a plateau period is determined based on the Gaussian distribution.

[0013] In some embodiments, the step of obtaining the Gaussian distribution of the detection data includes:

[0014] Configure Gaussian mixture model;

[0015] The detection data is input into the Gaussian mixture model, and the model parameters of the Gaussian mixture model are determined by an expectation maximization algorithm to obtain the Gaussian distribution.

[0016] In some embodiments, the step of determining the time when the voltage data enters a plateau period based on the Gaussian distribution includes:

[0017] The time corresponding to the symmetry axis of the Gaussian distribution is determined as the time when the voltage data enters the plateau period.

[0018] In some embodiments, the detection data includes a two-dimensional array, and the parameters of the two-dimensional array include the time and the standard deviation.

[0019] In some embodiments, the voltage data includes a first plateau period and a second plateau period, the abnormality detection of the secondary battery includes a self-discharge detection, and the step of detecting the secondary battery based on the detection time includes:

[0020] Among the plurality of the secondary batteries, obtaining a voltage value of the secondary battery corresponding to the detection time in the second plateau period as a detection voltage;

[0021] Determining a median voltage value of the detection voltage among the plurality of detection voltages;

[0022] Determine the secondary battery corresponding to the median voltage value as a reference secondary battery;

[0023] Determine a time difference between the detection time in the second plateau period of the secondary battery to be tested and the detection time in the second plateau period of the reference secondary battery;

[0024] Determining a power deviation between the secondary battery to be tested and the reference secondary battery based on the time difference;

[0025] Determining a change state of the power deviation based on the plurality of charging data of the secondary battery to be tested sorted by time;

[0026] In response to the electric quantity deviation increasing and exceeding a deviation threshold, it is determined that the secondary battery to be tested has a self-discharge abnormality.

[0027] In some embodiments, the voltage data includes a first plateau period and a second plateau period, the abnormality detection of the secondary battery includes self-discharge increase degree detection, and the step of detecting the secondary battery based on the detection time includes:

[0028] Determine a reference power, where the reference power is the power that is first charged into the secondary battery to be tested from the detection time of the first plateau period to the detection time of the second plateau period;

[0029] Determine a first detection power, where the first detection power is the power charged into the secondary battery to be tested during a target time period from the detection time of the first plateau period to the detection time of the second plateau period;

[0030] In response to the first detected electrical quantity being greater than the reference electrical quantity, a self-discharge increase degree of the secondary battery to be tested is determined based on the first detected electrical quantity and the reference electrical quantity.

[0031] In some embodiments, the abnormality detection of the secondary battery includes capacity detection, and the step of detecting the secondary battery based on the detection time includes:

[0032] Among the plurality of the secondary batteries, obtaining the amount of electricity charged into the secondary battery during the plateau period as the second detection electricity;

[0033] Among the plurality of second detected electrical quantities, the second detected electrical quantities that are outliers downward are determined to be abnormal electrical quantities, and the secondary battery corresponding to the abnormal electrical quantities has abnormal capacity.

[0034] In some embodiments, the abnormality detection of the secondary battery includes capacity detection, and the step of detecting the secondary battery based on the detection time includes:

[0035] Determine the amount of electricity charged into the secondary battery under test during the plateau period as the third detection electricity;

[0036] In response to the third detected power levels acquired in chronological order decreasing and being lower than a power threshold, it is determined that the capacity of the secondary battery to be tested is abnormal.

[0037] In a second aspect, an embodiment of the present application further provides a secondary battery abnormality detection system, comprising:

[0038] A voltage data acquisition module, the voltage data acquisition module is used to acquire charging data of the secondary battery, the charging data includes voltage data, and the voltage data is used to characterize the change state of the voltage of the secondary battery over time during the charging process;

[0039] A standard deviation data acquisition module, the standard deviation data acquisition module is used to acquire standard deviation data based on the voltage data, the standard deviation data is used to characterize the change state of the standard deviation of the voltage of the secondary battery during the charging process with the time;

[0040] A detection time acquisition module, the detection time acquisition module is used to acquire the time when the voltage data enters the plateau period based on the standard deviation data, which is the detection time;

[0041] A detection module is used to detect the secondary battery based on the detection time.

[0042] Beneficial effect: Compared with the prior art, the embodiment of the present application provides a method for detecting abnormalities of a secondary battery, including obtaining charging data of the secondary battery during the charging stage, the charging data containing voltage data characterizing the change of the voltage of the secondary battery over time during the charging process; determining standard deviation data characterizing the change of the standard deviation of the voltage of the secondary battery over time during the charging process through the voltage data; determining the time when the voltage data of the secondary battery enters the plateau period through the standard deviation data, and detecting the secondary battery based on the time when the voltage data enters the plateau period. The present application determines the standard deviation data of the voltage by detecting the voltage data of the charging data, determines the time when the voltage data enters the plateau period through the standard deviation data, and detects the secondary battery based on the time when the voltage data enters the plateau period. The present application detects the secondary battery through charging data that is relatively easy to obtain from the secondary battery and has a large number of samples, without having to meet the many conditions required for low SOC state detection, thereby reducing the difficulty of detection and improving detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in 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 those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 A flowchart of the steps of the abnormality detection method of a secondary battery provided in an embodiment of the present application;

[0045] Figure 2 A specific flow chart of step 300 in the secondary battery abnormality detection method provided in an embodiment of the present application;

[0046] Figure 3 A specific flow chart of step 320 in the secondary battery abnormality detection method provided in an embodiment of the present application;

[0047] Figure 4 A specific flow chart of self-discharge detection in the abnormality detection method of a secondary battery provided in an embodiment of the present application;

[0048] Figure 5 A specific flow chart of detecting the degree of self-discharge increase in the abnormality detection method for a secondary battery provided in an embodiment of the present application;

[0049] Figure 6 A specific flow chart of a first capacity detection method in the abnormality detection method of a secondary battery provided in an embodiment of the present application;

[0050] Figure 7 A specific flow chart of the second capacity detection method in the abnormality detection method of the secondary battery provided in the embodiment of the present application;

[0051] Figure 8 A schematic diagram of module connections of a secondary battery abnormality detection system provided in an embodiment of the present application;

[0052] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0053] Fig.10 A curve diagram showing changes in voltage and standard deviation over time in the abnormality detection method for a secondary battery provided in an embodiment of the present application;

[0054] Reference numerals: 10, voltage data acquisition module; 20, standard deviation data acquisition module; 30, detection time acquisition module; 40, detection module; 100, memory; 200, processor. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0056] Secondary batteries are rechargeable batteries that can be recharged and reused by an external power source. After being discharged, these batteries can restore their energy storage capacity through the charging process and continue to be used.

[0057] With the development of new energy industry, new energy vehicles are becoming more and more popular among consumers. New energy vehicles are powered by lithium-ion battery packs, which are composed of dozens or even hundreds of batteries connected in parallel and series.

[0058] The current lithium-ion battery industry is developing rapidly, but secondary batteries may have internal and external short circuit faults during production and use, which are manifested as abnormally large self-discharge of secondary batteries and abnormally low voltage. Among them, the linear characteristics of the charge and discharge OCV (Open Circuit Voltage) of the ternary battery voltage are obvious, but the voltage of the iron-lithium battery has an obvious voltage platform area. The charge and discharge voltage changes in the platform area are not obvious, and the power deviation can only be calculated more accurately in the low SOC (State of Charge) range.

[0059] However, due to the different usage habits of each vehicle user, when the vehicle is used in a non-low SOC range for a long time, it is impossible to correctly identify the battery deviation of the vehicle, and it is also impossible to identify the secondary battery with abnormal self-discharge. Therefore, the vehicle needs to be driven to a low SOC state and left stationary for more than 3 to 4 hours. However, users often charge the vehicle when it is driven to a low SOC state. Therefore, there is less data that meets this working condition, and it is difficult to implement battery detection.

[0060] In view of this, an embodiment of the present application provides a method for detecting abnormalities of a secondary battery, including obtaining charging data of a secondary battery during a charging phase, the charging data containing voltage data characterizing the change of the voltage of the secondary battery over time during the charging process; determining standard deviation data characterizing the change of the standard deviation of the voltage of the secondary battery over time during the charging process through the voltage data; determining the time when the voltage data of the secondary battery enters a plateau period through the standard deviation data, and detecting the secondary battery based on the time when the voltage data enters the plateau period. The present application determines the standard deviation data of the voltage by detecting the voltage data of the charging data, determines the time when the voltage data enters a plateau period through the standard deviation data, and detects the secondary battery based on the time when the voltage data enters the plateau period. The present application detects the secondary battery through charging data that is relatively easy to obtain from the secondary battery and has a large number of samples, without having to meet the many conditions required for low SOC state detection, thereby reducing the difficulty of detection and improving detection efficiency.

[0061] In some embodiments, see Figure 1 , Figure 1 This is a flowchart of the steps of the secondary battery abnormality detection method provided in the embodiment of the present application. The secondary battery abnormality detection method provided in the embodiment of the present application is specifically implemented through steps 100 to 400:

[0062] Step 100: Acquire charging data of the secondary battery, where the charging data includes voltage data, and the voltage data is used to characterize a change in voltage of the secondary battery over time during the charging process.

[0063] In some embodiments, after the charging data is acquired, current processing and voltage processing need to be performed on the charging data to ensure the reliability of the data.

[0064] Specifically, when current processing is performed on the charging data, the charging data of non-constant current charging, that is, the charging data of non-slow charging, is eliminated, and the charging data is divided into charging segments according to the current.

[0065] In some embodiments, the charging data is divided into charging segments with a current difference as one charging segment. Optionally, a is any value among 3, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5, or any value in an interval consisting of any two values.

[0066] Specifically, when performing voltage processing on charging data, a box plot is used to identify outlier voltages in the charging segments obtained by dividing the charging data according to the current, and the outlier voltage data is reassigned based on the average value of the data before and after the outlier voltage to obtain stable voltage data.

[0067] Furthermore, the voltage is mean-smoothed by means of a sliding window; specifically, the number of frames of the sliding window is b, and optionally, b is any value among 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 or any value in an interval consisting of any two values.

[0068] Step 200: acquiring standard deviation data based on voltage data, where the standard deviation data is used to characterize the change state of the standard deviation of the voltage of the secondary battery over time during the charging process.

[0069] Specifically, see Fig.10 , Fig.10 The voltage and standard deviation of the abnormality detection method of the secondary battery provided in the embodiment of the present application are changed over time. The standard deviation of the voltage is calculated by sliding window to obtain the change state of the standard deviation of the voltage over time. Specifically, the number of frames of the sliding window is c, and optionally, c is any value among 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 or any value in the interval composed of any two values.

[0070] Step 300: Obtain the time when the voltage data enters the plateau period based on the standard deviation data, which is the detection time.

[0071] Understandably, see Fig.10 , Fig.10The curve diagram of the change of voltage and standard deviation over time in the abnormal detection method of the secondary battery provided in the embodiment of the present application, the target battery detected by the embodiment of the present application is an iron-lithium battery, and the iron-lithium battery has two obvious voltage plateaus, and the standard deviation corresponds to the start time of the plateau and there are also two peaks. In the early stage of the charging process, the voltage change rate tends to be stable, the standard deviation continues to decline, and the voltage curve seems to be stable, but the change rate is constantly changing, and the standard deviation is also jagged. Therefore, the present application removes the peak value in the standard deviation, and uses a Gaussian mixture model to identify the standard deviation data after removing the peak value to determine the time when the voltage data enters the plateau.

[0072] Specifically, see Figure 2 , Figure 2 This is a specific flow chart of step 300 in the abnormality detection method of a secondary battery provided in an embodiment of the present application. The method for obtaining the time when the voltage data enters the plateau period based on the standard deviation data is specifically implemented through steps 310 to 330:

[0073] Step 310: Determine the rate of change of the standard deviation within the target period, and obtain standard deviation data that meets the rate of change threshold to construct detection data.

[0074] It can be understood that the standard deviation data is cut to remove the peak data with a large standard deviation change rate within the target period, that is, the data with a continuously decreasing standard deviation in the front part and a continuously increasing standard deviation in the second half are removed to obtain usable detection data.

[0075] In some embodiments, the detection data is arrayed in two dimensions, and the time parameter and the standard deviation parameter in the detection data are used as two parameters to construct the two-dimensional array.

[0076] Step 320: Obtain Gaussian distribution of detection data.

[0077] In some embodiments, see Figure 3 , Figure 3 This is a specific flow chart of step 320 in the abnormality detection method of a secondary battery provided in an embodiment of the present application. The method for obtaining the Gaussian distribution of the detection data is specifically implemented through steps 321 to 322:

[0078] Step 321: Configure the Gaussian mixture model.

[0079] Step 322: Input the detection data into the Gaussian mixture model, and determine the model parameters of the Gaussian mixture model through the expectation maximization algorithm to obtain the Gaussian distribution.

[0080] Specifically, the two-dimensional array is introduced into the Gaussian model, the expectation-maximization algorithm is used for parameter estimation, the mean and standard deviation of each distribution are calculated, and the multimodal distribution is separated into two Gaussian original distributions, which correspond to the two plateaus of the secondary battery.

[0081] Step 330: Determine the time when the voltage data enters a plateau period based on the Gaussian distribution.

[0082] In some embodiments, when determining the time when the voltage data enters the plateau period based on the Gaussian distribution, the time corresponding to the symmetry axis of the Gaussian distribution is determined as the time when the voltage data enters the plateau period.

[0083] Step 400: Detect the secondary battery based on the detection time.

[0084] In some embodiments, see Figure 4 , Figure 4 A specific flow chart of self-discharge detection in the abnormality detection method of a secondary battery provided in an embodiment of the present application. Specifically, the target secondary battery in the embodiment of the present application is an iron-lithium battery, and its voltage data includes a first plateau period and a second plateau period. The method for performing self-discharge detection on the secondary battery based on the detection time is specifically implemented through steps 410 to 416:

[0085] Step 410: among the plurality of secondary batteries, a voltage value corresponding to the detection time of the secondary battery in the second plateau period is obtained as the detection voltage.

[0086] Step 411 : Determine a median voltage value of the detection voltage among a plurality of detection voltages.

[0087] Step 412: Determine the secondary battery corresponding to the median voltage value as the reference secondary battery.

[0088] Step 413: Determine the time difference between the detection time in the second plateau period of the secondary battery to be tested and the detection time in the second plateau period of the reference secondary battery.

[0089] Step 414: Determine the power deviation between the secondary battery to be tested and the reference secondary battery based on the time difference.

[0090] Specifically, the power deviation between the secondary battery to be tested and the reference secondary battery is calculated by ampere-hour integration.

[0091] Step 415: Determine the change state of the power deviation based on the multiple charging data of the secondary battery to be tested sorted by time.

[0092] Step 416: In response to the power deviation increasing and exceeding the deviation threshold, it is determined that the secondary battery to be tested has a self-discharge abnormality.

[0093] It is understandable that since the battery has a certain degree of normal self-discharge phenomenon, after determining that the power deviation has increased, it is necessary to further determine whether the increase in the power deviation exceeds the deviation threshold of the normal self-discharge phenomenon. When it exceeds the deviation threshold of the normal self-discharge phenomenon, it is determined that the secondary battery to be tested has an abnormally large self-discharge phenomenon.

[0094] In some embodiments, see Figure 5 , Figure 5 A specific flow chart of the self-discharge increase degree detection in the abnormality detection method of the secondary battery provided in the embodiment of the present application. Specifically, the target secondary battery of the embodiment of the present application is an iron-lithium battery, and its voltage data includes a first plateau period and a second plateau period. The method for detecting the self-discharge increase degree of the secondary battery based on the detection time is implemented by steps 420 to 422:

[0095] Step 420: Determine a reference power level, where the reference power level is the power level that is first charged into the secondary battery to be tested from the detection time of the first plateau period to the detection time of the second plateau period.

[0096] It is understandable that due to the normal self-discharge phenomenon of the battery during use, it is impossible to directly determine whether there is self-discharge abnormality during subsequent use. Therefore, this application selects the amount of electricity charged into the secondary battery to be tested for the first time from the detection time of the first plateau period to the detection time of the second plateau period as the benchmark electricity, in order to improve the reliability of the benchmark electricity value.

[0097] Step 421: determining a first detection power, where the first detection power is the power charged into the secondary battery to be tested during a target time period from the detection time of the first plateau period to the detection time of the second plateau period.

[0098] Specifically, the reference power and the first detection power are calculated by integrating ampere-hours.

[0099] Step 422: In response to the first detected power being greater than the reference power, determining the degree of increase in self-discharge of the secondary battery to be tested based on the first detected power and the reference power.

[0100] In some embodiments, see Figure 6 , Figure 6 This is a specific flow chart of the first capacity detection method in the abnormality detection method of the secondary battery provided in the embodiment of the present application. Specifically, the first method of performing capacity detection on the secondary battery based on the detection time is implemented by steps 430 to 431:

[0101] Step 430: Obtain, from among the multiple secondary batteries, the amount of electricity charged into the secondary battery during the plateau period as the second detected electricity.

[0102] Step 431: among a plurality of second detected electrical quantities, determine that a second detected electrical quantity that is outliers downward is an abnormal electrical quantity, and a secondary battery corresponding to the abnormal electrical quantity has a capacity abnormality.

[0103] Specifically, the present application uses the 6σ method to determine abnormal electricity. Sigma (σ) is a symbol used in statistics to measure the standard deviation of data distribution. It describes the degree of deviation of data points from the mean. In quality control and anomaly detection, Sigma is often used to identify outliers or abnormal points in data. For example, the 6σ method is used in production to define quality control standards to ensure that the defect rate is at an extremely low level.

[0104] It can be understood that by using a horizontal comparison method for multiple secondary batteries, based on the consistency of the charged power of multiple secondary batteries during the plateau period, if there is a downward outlier secondary battery, that is, a secondary battery with a lower charged power, then the secondary battery will have abnormal capacity attenuation.

[0105] In some embodiments, see Figure 7 , Figure 7 This is a specific flow chart of the second capacity detection method in the abnormality detection method of the secondary battery provided in the embodiment of the present application. Specifically, the second method for detecting the capacity of the secondary battery based on the detection time is implemented through steps 440 to 441:

[0106] Step 440: Determine the amount of electricity charged into the secondary battery to be tested during the plateau period, which is the third detection amount of electricity.

[0107] Step 441: In response to a decrease in a plurality of third detected power levels acquired in chronological order and being lower than a power threshold, it is determined that the capacity of the secondary battery to be tested is abnormal.

[0108] It can be understood that the capacity anomaly is evaluated using the longitudinal historical charging data of a single secondary battery. Based on the historical data of the amount of electricity charged into the secondary battery during the plateau period, if there is a phenomenon of continuous decrease in the amount of electricity charged and exceeds the electricity threshold range, then the secondary battery has abnormal capacity attenuation.

[0109] It can be understood that an embodiment of the present application provides a method for detecting abnormalities in a secondary battery, including obtaining charging data during the charging stage of the secondary battery, the charging data containing voltage data characterizing the change of the voltage of the secondary battery over time during the charging process; determining standard deviation data characterizing the change of the standard deviation of the voltage of the secondary battery over time during the charging process through the voltage data; determining the time when the voltage data of the secondary battery enters the plateau period through the standard deviation data, and detecting the secondary battery based on the time when the voltage data enters the plateau period. The present application determines the standard deviation data of the voltage by detecting the voltage data of the charging data, determines the time when the voltage data enters the plateau period through the standard deviation data, and detects the secondary battery based on the time when the voltage data enters the plateau period. The present application detects the secondary battery through charging data that is relatively easy to obtain from the secondary battery and has a large number of samples, without having to meet the many conditions required for low SOC state detection, thereby reducing the difficulty of detection and improving detection efficiency.

[0110] Accordingly, the embodiment of the present application also provides a secondary battery abnormality detection system, see Figure 8 , Figure 8 This is a schematic diagram of module connections of a secondary battery abnormality detection system provided in an embodiment of the present application. The secondary battery abnormality detection system provided in an embodiment of the present application includes:

[0111] The voltage data acquisition module 10 is used to acquire charging data of the secondary battery. The charging data includes voltage data. The voltage data is used to characterize the change state of the voltage of the secondary battery over time during the charging process.

[0112] The standard deviation data acquisition module 20 is used to acquire standard deviation data based on the voltage data. The standard deviation data is used to characterize the change state of the standard deviation of the voltage of the secondary battery over time during the charging process.

[0113] The detection time acquisition module 30 is used to acquire the time when the voltage data enters the plateau period based on the standard deviation data, which is the detection time.

[0114] The detection module 40 is used to detect the secondary battery based on the detection time.

[0115] In some embodiments, the detection time acquisition module 30 is specifically used for:

[0116] Determine the rate of change of the standard deviation within the target period, obtain the standard deviation data that meets the change rate threshold and construct the detection data;

[0117] Obtaining Gaussian distribution of detection data;

[0118] The time when the voltage data enters the plateau phase is determined based on the Gaussian distribution.

[0119] In some embodiments, the detection time acquisition module 30 is specifically used for:

[0120] Configure Gaussian mixture model;

[0121] The detection data is input into the Gaussian mixture model, and the model parameters of the Gaussian mixture model are determined by the expectation maximization algorithm to obtain the Gaussian distribution.

[0122] In some embodiments, the detection time acquisition module 30 is specifically used for:

[0123] The time corresponding to the symmetry axis of the Gaussian distribution is determined as the time when the voltage data enters the plateau period.

[0124] In some embodiments, the detection module 40 is specifically used to:

[0125] Among the plurality of secondary batteries, a voltage value corresponding to the detection time of the secondary battery in the second plateau period is obtained as the detection voltage;

[0126] Determining a median voltage value of the detection voltage among the multiple detection voltages;

[0127] Determine the secondary battery corresponding to the median voltage value as the reference secondary battery;

[0128] Determine a time difference between a detection time in a second plateau period of the secondary battery to be tested and a detection time in a second plateau period of a reference secondary battery;

[0129] Determining a power deviation between the secondary battery to be tested and the reference secondary battery based on the time difference;

[0130] Determine a change state of power deviation based on a plurality of charging data of the secondary battery to be tested sorted in time;

[0131] In response to the electric quantity deviation increasing and exceeding the deviation threshold, it is determined that the secondary battery to be tested has a self-discharge abnormality.

[0132] In some embodiments, the detection module 40 is specifically used to:

[0133] Determine a reference power, where the reference power is the power that is first charged into the secondary battery to be tested from the detection time of the first plateau period to the detection time of the second plateau period;

[0134] Determine a first detection power, where the first detection power is the power charged into the secondary battery to be tested during a target time period from the detection time of the first plateau period to the detection time of the second plateau period;

[0135] In response to the first detected electric quantity being greater than the reference electric quantity, the self-discharge increase degree of the secondary battery to be tested is determined based on the first detected electric quantity and the reference electric quantity.

[0136] In some embodiments, the detection module 40 is specifically used to:

[0137] From the plurality of secondary batteries, the amount of electricity charged into the secondary battery during the plateau period is obtained as the second detection electricity;

[0138] Among the plurality of second detected electrical quantities, a second detected electrical quantity that is outliers downward is determined to be an abnormal electrical quantity, and a secondary battery corresponding to the abnormal electrical quantity has a capacity abnormality.

[0139] In some embodiments, the detection module 40 is specifically used to:

[0140] Determine the amount of electricity charged into the secondary battery under test during the plateau period, which is the third test electricity;

[0141] In response to a plurality of third detected power levels acquired in chronological order decreasing and being lower than a power threshold, it is determined that the capacity of the secondary battery to be tested is abnormal.

[0142] Accordingly, an embodiment of the present application further provides an electronic device, see Fig. 9 , Fig. 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device provided in an embodiment of the present application includes:

[0143] The memory 100 and the processor 200 are connected to each other in communication, the memory 100 stores computer instructions, and the processor 200 executes the abnormality detection method of the secondary battery provided in the embodiment of the present application by executing the computer instructions. Specifically, the memory 100 and the processor 200 are connected via a communication bus.

[0144] In some embodiments, the processor 200 may be a central processing unit (CPU). The processor 200 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0145] In some embodiments, the memory 100 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the methods in the method embodiments of the present application. The processor 200 executes various functional applications and data processing of the processor 200 by running the non-transitory software programs, instructions and modules stored in the memory 100, that is, implementing the methods in the above method embodiments.

[0146] In some embodiments, the memory 100 may include a program storage area and a data storage area, wherein the program storage area may store an application required for operating the device or at least one function; the data storage area may store data created by the processor 200, etc. In addition, the memory 100 may include a high-speed random access memory 100, and may also include a non-volatile memory 100, such as at least one disk memory 100, a flash memory device, or other non-volatile solid-state memory 100. In some embodiments, the memory 100 may optionally include a memory 100 remotely arranged relative to the processor 200, and these remote memories 100 may be connected to the processor 200 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0147] In some embodiments, one or more modules are stored in the memory 100 and when executed by the processor 200, the methods in the above method embodiments are performed.

[0148] In some embodiments, the specific details of the above-mentioned electronic device can be understood by referring to the corresponding related descriptions and effects in the above-mentioned method embodiments, and will not be repeated here.

[0149] The present application has introduced in detail a secondary battery abnormality detection method and system provided in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A secondary battery abnormality detection method, characterized in that: include: Acquiring charging data of the secondary battery, wherein the charging data includes voltage data, and the voltage data is used to characterize a change state of the voltage of the secondary battery over time during the charging process; Acquire standard deviation data based on the voltage data, wherein the standard deviation data is used to characterize a change state of the standard deviation of the voltage of the secondary battery over time during the charging process; The time when the voltage data enters a plateau period is obtained based on the standard deviation data, which is the detection time; The secondary battery is detected to be abnormal based on the detection time.

2. The method for detecting abnormality of a secondary battery according to claim 1, characterized in that: The step of obtaining the time when the voltage data enters the plateau period based on the standard deviation data includes: Determine the rate of change of the standard deviation within a target period, and obtain the standard deviation data that meets the rate of change threshold to construct detection data; Obtaining Gaussian distribution of the detection data; The time when the voltage data enters a plateau period is determined based on the Gaussian distribution.

3. The method for detecting abnormality of a secondary battery according to claim 2, characterized in that: The step of obtaining the Gaussian distribution of the detection data comprises: Configure Gaussian mixture model; The detection data is input into the Gaussian mixture model, and the model parameters of the Gaussian mixture model are determined by an expectation maximization algorithm to obtain the Gaussian distribution.

4. The method for detecting abnormality of a secondary battery according to claim 2, wherein: The step of determining the time when the voltage data enters a plateau period based on the Gaussian distribution comprises: The time corresponding to the symmetry axis of the Gaussian distribution is determined as the time when the voltage data enters the plateau period.

5. The method for detecting abnormality of a secondary battery according to claim 2, wherein: The detection data includes a two-dimensional array, and the parameters of the two-dimensional array include the time and the standard deviation.

6. The method for detecting abnormality of a secondary battery according to claim 1, wherein: The voltage data includes a first plateau period and a second plateau period, the abnormality detection of the secondary battery includes a self-discharge detection, and the step of performing abnormality detection on the secondary battery based on the detection time includes: Among the plurality of the secondary batteries, obtaining a voltage value of the secondary battery corresponding to the detection time in the second plateau period as a detection voltage; Determining a median voltage value of the detection voltage among the plurality of detection voltages; Determine the secondary battery corresponding to the median voltage value as a reference secondary battery; Determine a time difference between the detection time in the second plateau period of the secondary battery to be tested and the detection time in the second plateau period of the reference secondary battery; Determining a power deviation between the secondary battery to be tested and the reference secondary battery based on the time difference; Determining a change state of the power deviation based on the plurality of charging data of the secondary battery to be tested sorted by time; In response to the electric quantity deviation increasing and exceeding a deviation threshold, it is determined that the secondary battery to be tested has a self-discharge abnormality.

7. The method for detecting abnormality of a secondary battery according to claim 1, characterized in that: The voltage data includes a first plateau period and a second plateau period, the abnormality detection of the secondary battery includes self-discharge increase degree detection, and the step of performing abnormality detection on the secondary battery based on the detection time includes: Determine a reference power, where the reference power is the power that is first charged into the secondary battery to be tested from the detection time of the first plateau period to the detection time of the second plateau period; Determine a first detection power, where the first detection power is the power charged into the secondary battery to be tested during a target time period from the detection time of the first plateau period to the detection time of the second plateau period; In response to the first detected electrical quantity being greater than the reference electrical quantity, a self-discharge increase degree of the secondary battery to be tested is determined based on the first detected electrical quantity and the reference electrical quantity.

8. The method for detecting abnormality of a secondary battery according to claim 1, wherein: The abnormality detection of the secondary battery includes capacity detection, and the step of performing abnormality detection on the secondary battery based on the detection time includes: Among the plurality of the secondary batteries, obtaining the amount of electricity charged into the secondary battery during the plateau period as the second detection electricity; Among the plurality of second detected electrical quantities, the second detected electrical quantities that are outliers downward are determined to be abnormal electrical quantities, and the secondary battery corresponding to the abnormal electrical quantities has abnormal capacity.

9. The method for detecting abnormality of a secondary battery according to claim 1, characterized in that: The abnormality detection of the secondary battery includes capacity detection, and the step of performing abnormality detection on the secondary battery based on the detection time includes: Determine the amount of electricity charged into the secondary battery under test during the plateau period as the third detection electricity; In response to the third detected power levels acquired in chronological order decreasing and being lower than a power threshold, it is determined that the capacity of the secondary battery to be tested is abnormal.

10. A secondary battery abnormality detection system, characterized in that: include: A voltage data acquisition module, the voltage data acquisition module is used to acquire charging data of the secondary battery, the charging data includes voltage data, and the voltage data is used to characterize the change state of the voltage of the secondary battery over time during the charging process; A standard deviation data acquisition module, the standard deviation data acquisition module is used to acquire standard deviation data based on the voltage data, the standard deviation data is used to characterize the change state of the standard deviation of the voltage of the secondary battery during the charging process with the time; A detection time acquisition module, the detection time acquisition module is used to acquire the time when the voltage data enters the plateau period based on the standard deviation data, which is the detection time; A detection module is used to perform abnormality detection on the secondary battery based on the detection time.

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