CN118091420A - Battery self-discharge detection method, system, equipment and medium - Google Patents
Battery self-discharge detection method, system, equipment and medium Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
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Abstract
The invention provides a battery self-discharge detection method, a system, equipment and a medium. The method comprises the following steps: acquiring the voltage of a battery cell in a battery in a continuous time period, and analyzing all discharge starting moments of the corresponding battery; detecting the voltage of each cell based on an outlier detection algorithm, and screening out outlier cells; determining the residual electric quantity of each electric core at different discharge starting moments according to the voltages of each electric core in the target battery at different discharge starting moments; screening a plurality of detection moments from all discharge starting moments; calculating the self-discharge rate of the outlier cells at a plurality of detection moments according to the residual electric quantity of each cell; and detecting whether the outlier battery cells are abnormal according to the self-discharge rates at a plurality of detection moments, and judging the self-discharge faults of the target batteries corresponding to the outlier battery cells when the outlier battery cells are abnormal. The method provides a comprehensive, efficient and accurate fault diagnosis method for battery management of the electric vehicle.
Description
Technical Field
The present invention relates to the field of battery detection, and in particular, to a method, a system, an apparatus, and a medium for detecting self-discharge of a battery.
Background
The lithium battery has a complex structure and covers components such as a positive electrode, a negative electrode, a diaphragm, electrolyte, a current collector, a binder, a conductive agent and the like. The production process flow is long, and more than 50 complex procedures are involved. Problems, such as the introduction of impurities into the battery cells, metal particle contamination during system integration, and the like, are unavoidable during battery manufacturing, system integration, and application. These problems often lead to cell voltages below normal levels, manifesting as increased self-discharge rates, affecting the charge and discharge performance of the overall battery pack, and possibly even causing safety risks when severe.
At present, two main methods for early warning of self-discharge faults of batteries exist: (1) The open-circuit voltage attenuation measurement method is used for rapidly screening abnormal cells by monitoring the voltage change rate or using the principle of accelerating internal side reactions at high temperature, and screening by matching with a re-screening and a final screening at normal temperature through open-circuit voltage, alternating-current internal resistance and self-discharge rate detection. (2) And monitoring the voltage or capacity change rate of the single battery cell in real time, and calculating the self-discharge rate. However, both of the above two main methods have certain drawbacks: the first method is time consuming and space consuming and is not suitable for batch detection. Along with the increase of the number of electric vehicles, the regular recall of the vehicles is complex in detection operation and poor in real-time performance, and is difficult to widely popularize. In the second method, due to factors such as signal loss or batch difference of the battery cells, the abnormal state of the battery cells is difficult to accurately judge by the voltage or the capacity value of the battery cells, so that the detection effect of the self-discharge abnormality of the battery is affected. Accordingly, there is a need to provide a battery self-discharge detection method, system, apparatus, and medium.
Disclosure of Invention
The invention provides a battery self-discharge detection method. The problem of the abnormal positive detection rate of battery self discharge among the prior art is low is solved.
The invention provides a battery self-discharge detection method, which comprises the following steps: acquiring the voltage of a battery cell in the battery in a continuous time period, and analyzing all discharge starting moments of the corresponding battery according to the voltage of the battery cell; wherein the batteries are from different vehicles; detecting the voltage of each cell based on an outlier detection algorithm, and screening out outlier cells; wherein the battery containing the outlier cell is used as a target battery; determining the residual electric quantity of each electric core at different discharge starting moments according to the voltages of each electric core in the target battery at different discharge starting moments; screening a plurality of detection moments from all discharge starting moments; calculating the self-discharge rate of the outlier cells at a plurality of detection moments according to the residual electric quantity of each cell; and detecting whether the outlier battery cells are abnormal according to the self-discharge rates at a plurality of detection moments, and judging the self-discharge faults of the target batteries corresponding to the outlier battery cells when the outlier battery cells are abnormal.
In an embodiment of the present invention, the detecting the voltage of each cell based on the outlier detection algorithm, and screening out the outlier cell includes: clustering the voltages of all the battery cells according to an outlier detection algorithm to obtain different voltage clusters; and screening out the voltages which do not belong to any voltage cluster from the voltages of the electric cores, and taking the electric core corresponding to the voltage as an outlier electric core.
In an embodiment of the present invention, the outlier detection algorithm is a local outlier detection algorithm based on density.
In an embodiment of the invention, after analyzing all discharge start moments of the corresponding battery according to the voltage of the battery cell, the method further includes: the first discharge start time after each charge is completed is deleted from all the discharge start times, and each discharge start time is updated.
In an embodiment of the present invention, the screening the plurality of detection moments from all discharge start moments includes: determining the discharge end time of each battery in the vehicle corresponding to the target battery according to the voltage of each battery in the vehicle corresponding to the target battery in a continuous time period, and calculating the time difference between the current discharge start time and the previous discharge end time according to all the discharge start times; screening out discharge starting time corresponding to the time difference being greater than or equal to the preset standing time, and taking the screened discharge starting time as the detection time.
In an embodiment of the present invention, the calculating the self-discharge rate of the outlier cells at a plurality of detection moments according to the remaining power of each cell includes: calculating an average value of the residual electric quantity of all the non-outlier electric cores in the target battery and a residual electric quantity difference value of the average value of the residual electric quantity and the residual electric quantity of the outlier electric cores according to each detection moment; and calculating the variation of the residual electric quantity difference value of the current detection time relative to the outlier electric core of the previous detection time and the self-discharge rate according to the time sequence for each detection time.
In an embodiment of the present invention, the detecting whether the outlier battery cell is abnormal according to the self-discharge rates at a plurality of detection moments, and determining the self-discharge fault of the target battery corresponding to the outlier battery cell when the outlier battery cell is abnormal includes: screening detection moments when the self-discharge rate is larger than a preset self-discharge threshold value; analyzing the detection time of screening: if the screened detection time exceeds the preset number of continuous detection time, the outlier battery cells are abnormal, and the corresponding target battery fails in self-discharge; otherwise, the outlier battery cells are normal, and the corresponding target batteries are normal in self-discharge.
In an embodiment of the invention, the determining the self-discharge failure of the target battery further includes: and acquiring vehicle information containing the target battery, and forming early warning information of self-discharge abnormality.
In another aspect of the present invention, there is also provided a battery self-discharge detection system including: the data acquisition module is used for acquiring the voltage of the battery core in the battery in a continuous time period, and analyzing all discharge starting moments of the corresponding battery according to the voltage of the battery core; wherein the batteries are from different vehicles; the target battery detection module is used for detecting the voltage of each battery cell based on an outlier detection algorithm and screening out outlier battery cells; wherein the battery containing the outlier cell is used as a target battery; the residual electric quantity acquisition module is used for determining the residual electric quantity of each electric core at different discharge starting moments according to the voltages of each electric core in the target battery at different discharge starting moments; the detection moment screening module is used for screening a plurality of detection moments from all discharge starting moments; the self-discharge rate acquisition module is used for calculating the self-discharge rate of the outlier battery cells at a plurality of detection moments according to the residual electric quantity of each battery cell; the self-discharge abnormality judging module is used for detecting whether the outlier battery cells are abnormal according to the self-discharge rates at a plurality of detection moments and judging the self-discharge faults of the target batteries corresponding to the outlier battery cells when the outlier battery cells are abnormal.
In an embodiment of the present invention, there is also provided an electronic device including: one or more processors; and a storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the battery self-discharge detection method of any of the above.
In an embodiment of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to perform the battery self-discharge detection method according to any one of the above.
The invention provides a battery self-discharge detection method, a system, equipment and a medium. By acquiring voltage data of the battery cells in different vehicle batteries and analyzing all discharge starting moments, the discharge behavior of the batteries can be accurately tracked. The battery cell voltage is analyzed by using an outlier detection algorithm, so that outlier battery cells with abnormal performances can be effectively screened, and batteries containing the outlier battery cells can be further identified as target batteries. The battery health condition is accurately estimated by calculating the residual electric quantity of the outlier battery cores at different discharge starting moments. And selecting a plurality of detection moments from discharge starting moments, and calculating the self-discharge rate of the outlier cells according to the residual electric quantity of the cells at the moments. And judging whether the battery corresponding to the battery cell has self-discharge faults or not by analyzing the self-discharge rates. The invention provides a comprehensive, efficient and accurate fault diagnosis method for the battery management of the electric vehicle, is beneficial to timely finding and solving the battery performance problem, and improves the safety of the vehicle and the service life of the battery.
Drawings
Fig. 1 is a schematic flow chart of a method for detecting self-discharge of a battery according to an embodiment of the present invention;
FIG. 2 is a block diagram showing a battery self-discharge detection system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In the following description, numerous details are set forth in order to provide a more thorough explanation of embodiments of the present invention, it will be apparent, however, to one skilled in the art that embodiments of the present invention may be practiced without these specific details, in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the embodiments of the present invention.
The invention provides a battery self-discharge detection method which can efficiently and accurately monitor and diagnose the self-discharge problem in an electric vehicle battery. The battery packs with the outlier cells are screened out through an outlier detection algorithm and are monitored only for the vehicles, so that the computing resources are saved and the early warning efficiency is improved. The calculated cell self-discharge rate may be converted and compared to a monthly self-discharge rate provided by the cell provider to verify compliance. The invention can accumulate and count the electric quantity loss caused by self-discharge of the battery in a non-standing state, improves the accuracy and efficiency of battery health monitoring through an innovative method, and is particularly suitable for a battery management system of a modern electric vehicle.
Referring to fig. 1, the method for detecting self-discharge of a battery includes the following steps:
s1, acquiring the voltage of a battery cell in a battery in a continuous time period, and analyzing all discharge starting moments of the corresponding battery according to the voltage of the battery cell; wherein the batteries are from different vehicles;
s2, detecting the voltage of each cell based on an outlier detection algorithm, and screening out outlier cells; wherein the battery containing the outlier cell is used as a target battery;
S3, determining the residual electric quantity of each electric core at different discharge starting moments according to the voltages of each electric core in the target battery at different discharge starting moments;
S4, screening out a plurality of detection moments from all discharge starting moments;
s5, calculating the self-discharge rate of the outlier battery cells at a plurality of detection moments according to the residual electric quantity of each battery cell;
And S6, detecting whether the outlier battery cells are abnormal according to the self-discharge rates at a plurality of detection moments, and judging the self-discharge faults of the target batteries corresponding to the outlier battery cells when the outlier battery cells are abnormal.
The following details the steps:
S1, acquiring the voltage of a battery cell in a battery in a continuous time period, and analyzing all discharge starting moments of the corresponding battery according to the voltage of the battery cell; wherein the batteries are from different vehicles.
The invention relates to a battery data analysis method for electric vehicles, which aims to acquire voltage data of each battery cell in batteries of different vehicles and conduct deep analysis on the data. Firstly, a continuous time period (such as one week) is selected, during which the battery data of the vehicle to be tested with the total driving mileage within the preset mileage range is collected, and the battery data is data under pseudo-static (i.e. non-charge-discharge state). The battery data mainly comprises voltage information of electric cores in the battery of the vehicle to be tested, the voltage information can be acquired through a cloud system of the Internet of vehicles, and the voltage information can also be collected through a client.
In consideration of the influence of polarization voltage in the charging and discharging process of the battery, voltage data of a first frame of battery cells which are electrified after the vehicle is electrified and is kept stand are selected as open circuit voltage data of the corresponding battery cells, wherein the voltage data of each frame of battery cells comprises voltage data of all battery cells in the current vehicle. The time corresponding to the open circuit voltage is taken as the discharge start time (namely, the power-on start time) of the corresponding battery in the vehicle, and the battery is marked to start discharging. Further, in an embodiment of the present invention, after analyzing all discharge start moments of the corresponding battery according to the voltage of the battery cell, the method further includes: the first discharge start time after each charge is completed is deleted from all the discharge start times, and each discharge start time is updated. In the invention, in order to eliminate calculation deviation caused by charge balance current, after the discharge starting moments of all vehicles to be tested are identified, the first discharge starting moment after each charge cycle period is completed is deleted from all the discharge starting moments, so that the update of each discharge starting moment is realized. So that the updated discharge start time does not include any first discharge start time after the end of the charge cycle period, thereby ensuring more accurate data analysis and calculation.
It should be noted that the present invention is applicable to various electric vehicles, including all electric vehicles and hybrid electric vehicles. It will be appreciated that the invention is not limited to a specific duration of the continuous time period, and may be one week, one month, etc., as will be appreciated by those skilled in the art based on the rate of data processing and the needs of the application. The invention is beneficial to vehicle enterprises to more accurately locate and analyze specific fault reasons by limiting the total number of the driving mileage of the vehicle to be tested so as to pay special attention to the vehicles of which the self-discharge is possibly caused by the production process problems.
S2, detecting the voltage of each cell based on an outlier detection algorithm, and screening out outlier cells; wherein the battery containing the outlier cell is used as a target battery.
And carrying out fine batch processing on the voltage data of each battery cell in all vehicles to be tested by utilizing an outlier detection algorithm. By applying the sliding window technology, the cloud continuously collects a certain amount of voltage data, and then analyzes the voltage data according to an outlier detection algorithm, so that an abnormal value in the voltage data is identified. The battery cell corresponding to the abnormal value in the voltage data is taken as an outlier battery cell, and the battery containing the outlier battery cell is taken as a target battery, so that the vehicle to be tested containing the target battery can be accurately determined. The method is beneficial to effectively identifying and analyzing potential problems in battery performance and providing key information for battery health monitoring of the vehicle.
In an embodiment of the present invention, the detecting the voltage of each cell based on the outlier detection algorithm, and screening out the outlier cell includes:
Clustering the voltages of all the battery cells according to an outlier detection algorithm to obtain different voltage clusters;
and screening out the voltages which do not belong to any voltage cluster from the voltages of the electric cores, and taking the electric core corresponding to the voltage as an outlier electric core.
The outlier detection algorithm includes, but is not limited to, a local outlier factor (Local Outlier Factor, LOF) algorithm, a k-nearest neighbor algorithm, a Density-based local outlier detection (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) algorithm, etc., and specifically, in an embodiment of the present invention, the outlier detection algorithm is a Density-based local outlier detection algorithm. The density-based local outlier detection algorithm clusters the voltages according to different voltage distributions and the number of minimum points in a preset field radius and a preset neighborhood radius to form different voltage clusters, wherein each voltage cluster comprises a group of dense and similar voltage data reflecting the normal working mode of the corresponding battery cell under a certain state. For voltage values that do not belong to any voltage cluster, the voltage values are marked as abnormal values, which indicate that the corresponding cell may have a performance abnormality or failure, and thus the cell corresponding to the abnormal value is identified as an outlier cell. In this way, the DBSCAN can effectively identify potentially problematic cells in the cell voltage data, thereby providing critical information for battery health monitoring of the vehicle.
And S3, determining the residual electric quantity of each electric core at different discharge starting moments according to the voltages of each electric core in the target battery at different discharge starting moments.
Values at different discharge start times for voltages of all cells in an outlier cell: according to a preset laboratory data comparison table of open circuit voltage (Open circuit voltage, OCV) and residual Charge (SOC) corresponding to different battery materials, the residual Charge corresponding to each voltage value at each discharge starting moment can be determined. For example, as shown in table 1, after a certain open circuit voltage (e.g., 3.458) is obtained, a residual electric power value corresponding to the open circuit voltage is obtained based on a linear interpolation method according to an open circuit voltage interval (e.g., 3.456 to 3.471) to which the open circuit voltage belongs and an interval (e.g., 3 to 5) corresponding to the residual electric power. It is to be understood that the present embodiment only schematically illustrates a comparison table of open circuit voltage and residual capacity laboratory data corresponding to ternary battery materials, and for other battery materials, those skilled in the art can adapt specific data in the comparison table based on actual experimental data, which is not limited herein.
Table 1: open circuit voltage and residual capacity laboratory data comparison table of ternary battery material
S4, screening a plurality of detection moments from all discharge starting moments.
Specifically, in an embodiment of the present invention, the screening the plurality of detection moments from all the discharge start moments includes:
Determining the discharge end time of each battery in the vehicle corresponding to the target battery according to the voltage of each battery in the vehicle corresponding to the target battery in a continuous time period, and calculating the time difference between the current discharge start time and the previous discharge end time according to all the discharge start times;
Screening out discharge starting time corresponding to the time difference being greater than or equal to the preset standing time, and taking the screened discharge starting time as the detection time.
For a vehicle with a target battery, all discharge end moments of each battery cell in a selected continuous time period are identified according to the acquired voltage data of each battery cell in the vehicle. For each cell: and traversing all updated discharge starting moments, and calculating the time difference between the current starting moment and the previous discharge ending moment. All the updated discharge start moments are all the discharge start moments left after the first discharge start moment after each charge is deleted from the discharge start moments. Considering that a short rest time (i.e., time difference) after a large current discharge may cause a large influence of polarization voltage, thereby increasing false alarm rate. Therefore, only when the time difference is greater than or equal to the preset standing time, the corresponding discharge start time is taken as the detection time of the battery cell. By the method, the battery performance problem can be more effectively identified and analyzed, so that the accuracy and reliability of the battery management system are improved. It will be appreciated that for a vehicle in which a target battery is present, each cell thereon may have a plurality of different detection instants. Further, in the invention, detection moments corresponding to the residual electric quantity of the outlier battery cells being smaller than or equal to a preset electric quantity threshold (such as 50%) are removed from all the obtained detection moments, so that a plurality of final detection moments are formed, wherein the electric quantity threshold can be adaptively set, and the detection moments are not limited. It should be noted that the specific duration of the standing duration may be adaptively set based on the actual battery performance, and is not limited herein.
And S5, calculating the self-discharge rate of the outlier battery cells at a plurality of detection moments according to the residual electric quantity of each battery cell.
Specifically, in an embodiment of the present invention, the calculating the self-discharge rate of the outlier cells at a plurality of detection moments according to the remaining power of each cell includes:
Calculating an average value of the residual electric quantity of all the non-outlier electric cores in the target battery and a residual electric quantity difference value of the average value of the residual electric quantity and the residual electric quantity of the outlier electric cores according to each detection moment;
And calculating the variation of the residual electric quantity difference value of the current detection time relative to the outlier electric core of the previous detection time and the self-discharge rate according to the time sequence for each detection time.
Because the target battery has a plurality of normal cells (i.e., non-outlier cells) besides the outlier cells, firstly, for each detection time, according to the residual electric quantity SOC of the normal cells and the outlier cells in the target battery obtained in the step S3, an average value of the residual electric quantity of the normal cells is obtained based on the residual electric quantity value of the normal cells. Since the remaining power of the normal cell and the outlier cell are generally inconsistent, the remaining power difference Δsoc between the two can be calculated. Then traversing all detection moments according to the sequence of time, aiming at each detection moment t: the amount of change delta (Δsoc) between the remaining amount difference Δsoc t at the current detection time t and the remaining amount difference Δsoc t-1 at the previous detection time t-1 is calculated. And calculates the time difference deltat between two adjacent detection moments. Thereafter, the self-discharge rate k of the outlier cell with respect to the previous detection time at the current detection time is obtained by calculating the ratio k (k=Δ (Δsoc)/Δt) of the amount of change in the difference in the remaining power and the time difference at the detection time. The calculation process of the self-discharge rate k is repeatedly executed, and the self-discharge rate data of the outlier battery cells can be provided for each detection moment, so that the health condition of the battery can be effectively monitored.
And S6, detecting whether the outlier battery cells are abnormal according to the self-discharge rates at a plurality of detection moments, and judging the self-discharge faults of the target batteries corresponding to the outlier battery cells when the outlier battery cells are abnormal.
After obtaining the self-discharge rates at a plurality of detection moments in step S5, analyzing the obtained self-discharge rate data to determine whether the outlier cells are abnormal, wherein abnormal refers to that the self-discharge rate is obviously higher than a normal level, or the self-discharge rate at a plurality of detection moments is continuously higher than a preset threshold. If the data analysis of a plurality of detection moments confirms that the outlier cells do have abnormality, the battery with the outlier cells is further judged to have self-discharge faults. This means that the performance and health of the battery may be affected, requiring further inspection or maintenance.
Specifically, in an embodiment of the present invention, the detecting whether the outlier battery cell is abnormal according to the self-discharge rates at a plurality of detection moments, and determining the self-discharge fault of the target battery corresponding to the outlier battery cell when the outlier battery cell is abnormal includes:
screening detection moments when the self-discharge rate is larger than a preset self-discharge threshold value;
Analyzing the detection time of screening:
If the screened detection time exceeds the preset number of continuous detection time, the outlier battery cells are abnormal, and the corresponding target battery fails in self-discharge;
otherwise, the outlier battery cells are normal, and the corresponding target batteries are normal in self-discharge.
And screening a plurality of detection moments with the self-discharge rate being greater than a preset self-discharge threshold value from the plurality of detection moments, and analyzing the screened detection moments to judge whether a plurality of continuous detection moments exist in the detection moments, wherein the self-discharge rate of the outlier battery cells is always greater than the preset self-discharge threshold value in the continuous detection moments. If such continuous detection time exists, the self-discharge abnormality of the outlier cell is indicated, and the self-discharge fault of the battery containing the outlier cell is determined. Conversely, if no such continuation is found beyond the threshold, it indicates that the self-discharge rate of the outlier cell is within the normal range and the cell in which it is located has no self-discharge failure. Still further, in an embodiment of the present invention, the determining the self-discharge failure of the target battery further includes: and acquiring vehicle information containing the target battery, and forming early warning information of self-discharge abnormality. After the cloud judges that a certain target battery has a self-discharge fault, vehicle related information containing the target battery is obtained, early warning information is generated according to the vehicle related information and is sent to a vehicle enterprise platform, and therefore the vehicle enterprise can be conveniently inspected according to the early warning information.
Referring to fig. 2, the battery self-discharge detection system 100 includes: a data acquisition module 110, a target battery detection module 120, a remaining power acquisition module 130, a detection time screening module 140, a self-discharge rate acquisition module 150, and a self-discharge abnormality judgment module 160. The data obtaining module 110 is configured to obtain a voltage of a battery cell in the battery in a continuous time period, and analyze all discharge start moments of the corresponding battery according to the voltage of the battery cell; wherein the batteries are from different vehicles. The target battery detection module 120 is configured to detect voltages of each battery cell based on an outlier detection algorithm, and screen out outlier battery cells; wherein the battery containing the outlier cell is used as a target battery. The remaining power obtaining module 130 is configured to determine the remaining power of each cell at different discharge start moments according to the voltages of each cell in the target battery at different discharge start moments. The detection time screening module 140 is configured to screen a plurality of detection times from all discharge start times. The self-discharge rate obtaining module 150 is configured to calculate self-discharge rates of the outlier cells at a plurality of detection moments according to the remaining power of each cell. The self-discharge abnormality determination module 160 is configured to detect whether the outlier battery cell is abnormal according to the self-discharge rates at a plurality of detection moments, and determine a self-discharge fault of the target battery corresponding to the outlier battery cell when the outlier battery cell is abnormal.
For specific limitations of the battery self-discharge detection system, reference may be made to the above limitations of the battery self-discharge detection method, and no further description is given here. The above-described respective modules in the battery self-discharge detection system may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in a hardware format or may be independent of a processor in the computer device, or may be stored in a software format in a memory in the computer device, so that the processor may call for operations corresponding to the above modules.
It should be noted that, in order to highlight the innovative part of the present invention, no module that is not very close to solving the technical problem presented by the present invention is introduced in the present embodiment, but it does not indicate that other modules are not present in the present embodiment.
Referring to fig. 3, the electronic device 1 may include a memory 12, a processor 13, and a bus, and may further include a computer program, such as a battery self-discharge detection program, stored in the memory 12 and executable on the processor 13.
The memory 12 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 12 may in some embodiments be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. The memory 12 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 12 may be used not only for storing application software installed in the electronic apparatus 1 and various types of data, such as a code for battery self-discharge detection, etc., but also for temporarily storing data that has been output or is to be output.
The processor 13 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, various control chips, and the like. The processor 13 is a Control Unit (Control Unit) of the electronic device 1, connects the respective components of the entire electronic device 1 using various interfaces and lines, and executes various functions of the electronic device 1 and processes data by running or executing programs or modules (e.g., a battery self-discharge detection program, etc.) stored in the memory 12, and calling data stored in the memory 12.
The processor 13 executes the operating system of the electronic device 1 and various types of applications installed. The processor 13 executes the application program to implement the steps in the battery self-discharge detection method described above.
Illustratively, the computer program may be split into one or more modules that are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules may be a series of instruction segments of a computer program capable of performing a specific function for describing the execution of the computer program in the electronic device 1. For example, the computer program may be divided into a data acquisition module 110, a target battery detection module 120, a remaining power acquisition module 130, a detection time screening module 140, a self-discharge rate acquisition module 150, and a self-discharge abnormality determination module 160.
The integrated units implemented in the form of software functional modules may be stored in a computer readable storage medium, which may be non-volatile or volatile. The software functional module is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor (processor) to perform part of the functions of the method for detecting self-discharge of a battery according to the embodiments of the present application.
In summary, according to the battery self-discharge detection method, system, device and medium disclosed by the invention, the difference between the residual electric quantity of the outlier battery core and the residual electric quantity of the normal battery core when the vehicle which stands for a long time is just electrified is counted, and the difference is gradually increased along with the time. And calculating the self-discharge rate in the period by calculating the ratio of the residual electric quantity deviation variation quantity and the time difference of the two adjacent power-up moments, and converting the self-discharge rate into the month self-discharge rate. And when the month self-discharge rate of the continuous fragments continuously exceeds a certain threshold value, carrying out self-discharge abnormality early warning on the outlier cells. Compared with the traditional method, the method for online monitoring of the abnormal self-discharge rate of the battery cells in the battery pack can calculate the self-discharge rate of the battery cells in the outlier without keeping the vehicle in a standing state for a long time, and can accurately identify the battery cells with abnormal self-discharge in the battery pack in a short time. By utilizing cloud data, the method and the device can identify the self-discharge abnormal battery cells under the condition that the vehicle does not need recall, and early warning and identification are carried out on batch problems possibly caused by a production process in advance. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (11)
1. A method for detecting self-discharge of a battery, the method comprising:
Acquiring the voltage of a battery cell in the battery in a continuous time period, and analyzing all discharge starting moments of the corresponding battery according to the voltage of the battery cell; wherein the batteries are from different vehicles;
Detecting the voltage of each cell based on an outlier detection algorithm, and screening out outlier cells; wherein the battery containing the outlier cell is used as a target battery;
Determining the residual electric quantity of each electric core at different discharge starting moments according to the voltages of each electric core in the target battery at different discharge starting moments;
screening a plurality of detection moments from all discharge starting moments;
calculating the self-discharge rate of the outlier cells at a plurality of detection moments according to the residual electric quantity of each cell;
and detecting whether the outlier battery cells are abnormal according to the self-discharge rates at a plurality of detection moments, and judging the self-discharge faults of the target batteries corresponding to the outlier battery cells when the outlier battery cells are abnormal.
2. The method for detecting the self-discharge of the battery according to claim 1, wherein the detecting the voltage of each cell based on the outlier detection algorithm, and the screening out the outlier cell comprises:
Clustering the voltages of all the battery cells according to an outlier detection algorithm to obtain different voltage clusters;
and screening out the voltages which do not belong to any voltage cluster from the voltages of the electric cores, and taking the electric core corresponding to the voltage as an outlier electric core.
3. The battery self-discharge detection method according to claim 2, wherein the outlier detection algorithm is a local outlier detection algorithm based on density.
4. The method for detecting the self-discharge of the battery according to claim 1, wherein after analyzing all discharge start moments of the corresponding battery according to the voltage of the battery cell, further comprising: the first discharge start time after each charge is completed is deleted from all the discharge start times, and each discharge start time is updated.
5. The method of claim 4, wherein the screening out a plurality of detection moments from all discharge start moments comprises:
Determining the discharge end time of each battery in the vehicle corresponding to the target battery according to the voltage of each battery in the vehicle corresponding to the target battery in a continuous time period, and calculating the time difference between the current discharge start time and the previous discharge end time according to all the discharge start times;
screening out adjacent discharge starting moments corresponding to the time difference being greater than or equal to the preset standing time length, and taking the screened discharge starting moments as detection moments.
6. The method of claim 4, wherein calculating the self-discharge rate of the outlier cells at a plurality of detection times based on the remaining power of each cell comprises:
Calculating an average value of the residual electric quantity of all the non-outlier electric cores in the target battery and a residual electric quantity difference value of the average value of the residual electric quantity and the residual electric quantity of the outlier electric cores according to each detection moment;
And calculating the variation of the residual electric quantity difference value of the current detection time relative to the outlier electric core of the previous detection time and the self-discharge rate according to the time sequence for each detection time.
7. The method according to claim 4, wherein detecting whether the outlier cell is abnormal according to the self-discharge rates at a plurality of detection times, and determining that the target battery corresponding to the outlier cell has a self-discharge failure when abnormal, comprises:
screening detection moments when the self-discharge rate is larger than a preset self-discharge threshold value;
Analyzing the detection time of screening:
If the screened detection time exceeds the preset number of continuous detection time, the outlier battery cells are abnormal, and the corresponding target battery fails in self-discharge;
otherwise, the outlier battery cells are normal, and the corresponding target batteries are normal in self-discharge.
8. The battery self-discharge detection method according to claim 1, wherein the determination of the target battery self-discharge failure further comprises: and acquiring vehicle information containing the target battery, and forming early warning information of self-discharge abnormality.
9. A battery self-discharge detection system, the system comprising:
The data acquisition module is used for acquiring the voltage of the battery core in the battery in a continuous time period, and analyzing all discharge starting moments of the corresponding battery according to the voltage of the battery core; wherein the batteries are from different vehicles;
The target battery detection module is used for detecting the voltage of each battery cell based on an outlier detection algorithm and screening out outlier battery cells; wherein the battery containing the outlier cell is used as a target battery;
The residual electric quantity acquisition module is used for determining the residual electric quantity of each electric core at different discharge starting moments according to the voltages of each electric core in the target battery at different discharge starting moments;
The detection moment screening module is used for screening a plurality of detection moments from all discharge starting moments;
The self-discharge rate acquisition module is used for calculating the self-discharge rate of the outlier battery cells at a plurality of detection moments according to the residual electric quantity of each battery cell;
The self-discharge abnormality judging module is used for detecting whether the outlier battery cells are abnormal according to the self-discharge rates at a plurality of detection moments and judging the self-discharge faults of the target batteries corresponding to the outlier battery cells when the outlier battery cells are abnormal.
10. An electronic device, characterized in that: the electronic device includes:
one or more processors;
storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the battery self-discharge detection method of any of claims 1-8.
11. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor of a computer, causes the computer to perform the battery self-discharge detection method according to any one of claims 1 to 8.
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