Method and system for detecting self-discharge state in battery based on online data
By finely processing and analyzing the battery discharge segment data, we can determine whether there is self-discharge phenomenon in the battery pack, and solve the problem of misjudgment or misjudgment of existing detection methods, and realize accurate detection and timely response of the self-discharge state in the battery.
Patent Information
- Application Number
- CN202411401058.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-09
- Publication Date
- 2025-02-25
AI Technical Summary
The existing self-discharge state detection methods in batteries rely on fixed thresholds or pattern matching, which are prone to misjudgment or misjudgment due to differences in battery types and states, and the impact of battery temperature on self-discharge is not fully considered, affecting detection accuracy.
By obtaining vehicle discharge fragment data and marking, dividing the discharge fragments, screening the discharge fragments based on the order of incremental discharge duration and screening rules, calculating the power and SOC changes, and determining whether there is self-discharge phenomenon in the battery pack.
It realizes accurate detection of self-discharge status in the battery, can promptly detect the self-discharge phenomenon in the battery pack, and provides accurate data support for subsequent processing measures, improving the real-time, accuracy and automation of detection.
Smart Images
Figure CN119502705A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of battery status detection, and in particular relates to a method and system for detecting the self-discharge status of a battery based on online data. Background Art
[0002] With the popularity of new energy vehicles, the performance of battery packs, as their core components, has a direct impact on the vehicle's range and service life. Battery self-discharge, that is, the loss of power when it is stationary or not actively charged or discharged, is an important aspect of battery performance degradation. In order to detect and manage this phenomenon in a timely manner, a variety of battery self-discharge status detection methods based on BMS data collection have been developed. These methods analyze the collected data to preliminarily determine the battery self-discharge status. Some advanced methods can also analyze the battery working status in real time and evaluate the self-discharge phenomenon.
[0003] However, existing detection methods still have shortcomings. First, they mostly rely on fixed thresholds or pattern matching, which may lead to misjudgment or missed judgment due to differences in battery type and status. Second, most methods do not fully consider the impact of battery temperature on self-discharge, which affects the accuracy of detection. Furthermore, the data processing and judgment logic of existing systems are relatively simple, lacking in-depth analysis and comparison of battery discharge characteristics in different time periods, and are easily interfered by accidental factors. Finally, these systems only provide simple alarms after self-discharge is detected, lacking differentiated processing measures for different degrees, reducing user experience and the reliability of the battery system. Summary of the invention
[0004] The purpose of the present invention is to provide a method and system for detecting the self-discharge state of a battery based on online data, so as to solve the problems raised in the background technology.
[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:
[0006] In a first aspect, the present invention proposes a method for detecting a self-discharge state in a battery based on online data, the method comprising:
[0007] S1. Obtain vehicle discharge segment data and mark it, including marking the full charge time point A and the start charging time point B;
[0008] S2. The time period between the full charge time point A and the next start charge time point B in the discharge segment data is defined as a discharge sub-segment T, and discharge sub-segments T1, T2, ..., T are defined in increasing increments according to the discharge duration. n ;
[0009] S3. Screen the discharge sub-segments based on the order of increasing discharge duration and the set screening rules to determine the target discharge segments T1′, T2′, T3′, ..., T m ', m≤n; the screening rule includes: on the basis of screening out the minimum number of discharge sub-segments, the discharge duration of any target discharge segment is at least K times the discharge duration of the previous adjacent target discharge segment;
[0010] S4. For the discharge electronic segments T1′, T2′, T3′, ..., T m ', calculate the amount of electricity Q discharged in the corresponding time period and the change in SOC SOC dif , and then calculate the amount of electricity q released for every 1% change in SOC in the corresponding time period m , where q m =Q / SOC dif , m represents the corresponding target discharge segment number;
[0011] S5. When the power q1, q2, q3, ..., q corresponding to the target discharge segment m When a monotonically decreasing trend is shown, it is determined that the battery pack has self-discharge.
[0012] Further preferably, in step S1, the obtained discharge segment includes the charging current, discharging current, charging time point, discharging time point, SOC value and battery temperature of the battery.
[0013] Further preferably, in step S3, the calculating the amount of electricity Q discharged in the corresponding time period includes: taking the product of the discharge current and the discharge time interval of the corresponding discharge sub-segment as the amount of electricity Q.
[0014] Further preferably, in step S3, the amount of electricity q released for each 1% change in SOC is calculated. m When the battery temperature is adjusted, m The value is temperature corrected.
[0015] Further preferably, in step S4, the multiple K is set to a constant greater than 1.
[0016] Further preferably, after determining that the battery pack has self-discharge, it also includes recording and storing self-discharge related data, including the time point when self-discharge starts, the duration of self-discharge, the power loss during self-discharge, and the corresponding battery temperature.
[0017] Further preferably, after determining that the battery pack has self-discharged, the method also includes triggering corresponding processing measures according to the severity of the self-discharge, including sending a warning message to the user, limiting the power usage of the battery pack or prohibiting the use of the battery pack, and automatically or prompting the user to repair or replace the battery pack.
[0018] In a second aspect, the present invention proposes a battery self-discharge state detection system based on online data, the system comprising:
[0019] Data acquisition module: used to obtain and mark vehicle discharge segment data, including marking the full charge time point A and the time point B when charging starts. The vehicle discharge segment data includes charging current, discharging current, charging time point, discharging time point, SOC value and battery temperature;
[0020] Data processing module: used to execute the processing steps of the vehicle battery pack self-discharge detection method. Specifically, the module includes:
[0021] Time segment division unit: used to define the time period between the full charge time point A and the next start charge time point B in the discharge segment data as the discharge sub-segment T, and define the discharge sub-segments T1, T2, ..., T2 in increasing increments according to the discharge duration. n ;
[0022] A specific time period screening unit is used to screen the discharge sub-segments based on the order of increasing discharge duration and the set screening rules to determine the target discharge segments T1′, T2′, T3′, ..., T m ', m≤n; the screening rule includes: on the basis of screening out the minimum number of discharge sub-segments, the discharge duration of any target discharge segment is at least K times the discharge duration of the previous adjacent target discharge segment;
[0023] The power calculation unit is used for calculating the discharge sub-segments T1′, T2′, T3′, ..., T m ', calculate the amount of electricity Q discharged in the corresponding time period and the change in SOC SOC dif , and then calculate the amount of electricity q released for every 1% change in SOC in the corresponding time period m , where q m =Q / SOC dif , m represents the corresponding target discharge segment number;
[0024] Self-discharge judgment unit: used to determine the amount of electricity q1, q2, q3, ..., q corresponding to the target discharge segment m When a monotonically decreasing trend is shown, it is determined that the battery pack has self-discharge.
[0025] Further preferably, the system further comprises:
[0026] The judgment module receives the self-discharge judgment result from the data processing module. If the judgment result is that the battery pack has self-discharge, the self-discharge related data is recorded and stored, including the time point when the self-discharge starts, the duration of the self-discharge, the power loss during the self-discharge period, and the corresponding battery temperature.
[0027] Further preferably, the judgment module also includes a response unit for sending the self-discharge related data to the user, and triggering corresponding processing measures according to the severity of the self-discharge, including sending a warning message to the user, limiting the power usage of the battery pack or prohibiting the use of the battery pack, and automatically or prompting the user to repair or replace the battery pack.
[0028] The beneficial effects of the present invention are:
[0029] The present invention realizes accurate detection of the self-discharge state in the battery by real-time monitoring and analysis of online data. Through refined data acquisition and marking, time segment division, calculation of power and SOC changes, as well as specific time period screening and self-discharge judgment, the method can timely detect the self-discharge phenomenon in the battery pack and provide accurate data support for subsequent treatment measures. This detection method based on online data has the advantages of high real-time performance, good accuracy, and high degree of automation, which is of great significance for improving the safety and performance of the battery system. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Figure 1 A schematic flow chart of a method for detecting the self-discharge state of a battery based on online data provided in an embodiment of the present application.
[0031] Figure 2 This is a system architecture diagram of a battery self-discharge status detection system based on online data provided in an embodiment of the present application.
[0032] Figure 3 This is a structural diagram of a data processing module in a battery self-discharge state detection system based on online data provided in an embodiment of the present application. DETAILED DESCRIPTION
[0033] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.
[0034] Example 1
[0035] like Figure 1 As shown, this embodiment proposes a method for detecting the self-discharge state of a battery based on online data, the method comprising:
[0036] S1. Obtain the vehicle discharge segment data and mark it, including marking the full charge time point A and the time point B when charging starts; the discharge segment data includes the battery's charging current, discharge current, charging time point, discharge time point, SOC value and battery temperature.
[0037] S2. The time period between the full charge time point A and the next start charge time point B in the discharge segment data is defined as a discharge sub-segment T, and discharge sub-segments T1, T2, ..., T are defined in increasing increments according to the discharge duration. n ;
[0038] S3. Taking the discharge sub-segment T1 with the shortest discharge duration as the benchmark, the discharge sub-segments are screened based on the order of increasing discharge duration and the set screening rules to determine the target discharge segments T1′, T2′, T3′, ..., T m ', m≤n; the screening rule includes: on the basis of screening out the minimum number of discharge sub-segments, the discharge duration of any target discharge segment is at least K times the discharge duration of the previous adjacent target discharge segment; wherein K is a constant greater than 1;
[0039] S4. For the discharge electronic segments T1′, T2′, T3′, ..., T m ', calculate the amount of electricity Q discharged in the corresponding time period and the change in SOC (State of Charge) dif , and then calculate the amount of electricity q released for every 1% change in SOC in the corresponding time period m , where q m =Q / SOC dif , m represents the corresponding target discharge segment number; wherein the product of the discharge current and the discharge time interval of the corresponding discharge segment is taken as the electric quantity Q;
[0040] S5. When the power q1, q2, q3, ..., q corresponding to the target discharge segment m When a monotonically decreasing trend is shown, it is determined that the battery pack has self-discharge.
[0041] In this embodiment, the detection method aims to monitor the self-discharge state of the battery in real time through online data, and includes the following core steps:
[0042] (1) Data acquisition and labeling
[0043] Obtain vehicle discharge segment data in real time, including battery charging current, discharge current, charging time point, discharge time point, SOC value and battery temperature.
[0044] The full charge time point A and the start charging time point B are marked to ensure the accuracy of the time series of the data.
[0045] (2) Time segment division
[0046] The time period between the full charge time point A and the next charge start time point B in the discharge segment data is defined as the discharge segment T.
[0047] The discharge time increments to define the discharge segments T1, T2, ..., T n , ensuring that each fragment is independent and analyzable.
[0048] (3) Filtering during a specific time period
[0049] Taking the discharge sub-segment T1 with the shortest discharge time as the benchmark, in the order of increasing time, the duration of the target discharge segment is at least K times the duration of the previous adjacent target discharge segment, and several target discharge segments T1′, T2′, T3′, ..., T m ′, m≤n; since the discharge sub-segment T1 with the shortest discharge time is taken as the reference, T1′=T1.
[0050] As an example, the discharge sub-segments increasing in accordance with the discharge time are set to be 10min, 20min, 25min, 40min, 60min, and 100min, respectively, and the multiple K is specifically set to be 2. Based on the above screening rules, the target discharge segments obtained are 10min, 20min, 40min, and 100min. The number of the target discharge segments obtained above is kept at the maximum, that is, in the screening process based on the above screening rules, the minimum number of discharge sub-segments is screened out.
[0051] (4) Calculation of power and SOC changes
[0052] For each discharge sub-segment, the amount of electricity Q discharged in the corresponding time period is calculated by multiplying the discharge current by the discharge time interval.
[0053] Calculate the change in SOC SOC dif , and based on this, we can get the amount of electricity q released for every 1% change in SOC m , where q m =Q / SOC dif .
[0054] (5) Self-discharge judgment
[0055] When the power of the corresponding target discharge segment is q1, q2, q3, ..., q m When a monotonically decreasing trend is shown, it is determined that the battery pack has self-discharge.
[0056] Furthermore, based on the battery temperature, qm The value is temperature corrected to improve the accuracy of the judgment.
[0057] (6) Data recording and processing
[0058] Once it is determined that the battery pack has self-discharge, the self-discharge related data is recorded and stored, including the time point when the self-discharge starts, the duration of the self-discharge, the power loss during the self-discharge period, and the corresponding battery temperature.
[0059] Depending on the severity of the self-discharge, corresponding processing measures are triggered, such as sending a warning message to the user, limiting the power usage of the battery pack or prohibiting the use of the battery pack, and prompting the user to repair or replace the battery pack.
[0060] According to the above embodiment of the present invention, the above detection method realizes accurate detection of the self-discharge state in the battery by real-time monitoring and analysis of online data. Through refined data acquisition and marking, time segment division, calculation of power and SOC changes, as well as specific time period screening and self-discharge judgment, this method can timely detect the self-discharge phenomenon of the battery pack and provide accurate data support for subsequent treatment measures. This detection method based on online data has the advantages of high real-time performance, good accuracy, and high degree of automation, which is of great significance for improving the safety and performance of the battery system.
[0061] like Figure 2-3 As shown, based on the same inventive concept, this embodiment also proposes a battery self-discharge state detection system based on online data, the system comprising:
[0062] Data acquisition module: used to obtain and mark vehicle discharge segment data, including marking the full charge time point A and the time point B when charging starts. The vehicle discharge segment data includes charging current, discharging current, charging time point, discharging time point, SOC value and battery temperature;
[0063] Data processing module: used to execute the processing steps of the vehicle battery pack self-discharge detection method. Specifically, the module includes:
[0064] Time segment division unit: used to define the time period between the full charge time point A and the next start charge time point B in the discharge segment data as the discharge sub-segment T, and define the discharge sub-segments T1, T2, ..., T2 in increasing increments according to the discharge duration. n ;
[0065] A specific time period screening unit is used to screen the discharge sub-segments based on the order of increasing discharge duration and the set screening rules to determine the target discharge segments T1′, T2′, T3′, ..., T m', m≤n; the screening rule includes: on the basis of screening out the minimum number of discharge sub-segments, the discharge duration of any target discharge segment is at least K times the discharge duration of the previous adjacent target discharge segment;
[0066] The power calculation unit is used for calculating the discharge sub-segments T1′, T2′, T3′, ..., T m ', calculate the amount of electricity Q discharged in the corresponding time period and the change in SOC SOC dif , and then calculate the amount of electricity q released for every 1% change in SOC in the corresponding time period m , where q m =Q / SOC dif , m represents the corresponding target discharge segment number;
[0067] Self-discharge judgment unit: used to determine the amount of electricity q1, q2, q3, ..., q corresponding to the target discharge segment m When a monotonically decreasing trend is shown, it is determined that the battery pack has self-discharge.
[0068] Further preferably, the system further comprises:
[0069] Judgment module: receives the self-discharge judgment result from the data processing module. If the judgment result is that the battery pack has self-discharge, it records and stores self-discharge related data, including the time point when self-discharge starts, the duration of self-discharge, the power loss during self-discharge, and the corresponding battery temperature.
[0070] Further preferably, the judgment module also includes a response unit for sending self-discharge related data to the user, and triggering corresponding processing measures according to the severity of the self-discharge, including sending a warning message to the user, limiting the power usage of the battery pack or prohibiting the use of the battery pack, and automatically or prompting the user to repair or replace the battery pack.
[0071] It should be noted here that each module in the above-mentioned detection system corresponds to steps S1 to S5 in the detection method in the above-mentioned embodiment 1, and the instances and application scenarios implemented by multiple modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment 1.
[0072] It can be understood that the core point of this system is to build a battery self-discharge state detection system that integrates data acquisition, processing and judgment. The data acquisition module obtains vehicle discharge fragment data in real time, the data processing module performs fine processing and analysis on the data, and the judgment module triggers corresponding treatment measures according to the processing results. This systematic design not only improves the efficiency and accuracy of self-discharge detection, but also realizes automatic processing and timely response, greatly reducing the cost and risk of manual intervention. This system provides strong technical support for the safety monitoring and performance management of battery systems.
[0073] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0074] In addition, each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0075] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.
[0076] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims
1. A method for detecting the self-discharge state of a battery based on online data, characterized in that: The method comprises: S1. Obtain vehicle discharge segment data and mark it, including marking the full charge time point A and the start charging time point B; S2. The time period between the full charge time point A and the next start charge time point B in the discharge segment data is defined as a discharge sub-segment T, and discharge sub-segments T1, T2, ..., T are defined in increasing increments according to the discharge duration. n ; S3. Screen the discharge sub-segments based on the order of increasing discharge duration and the set screening rules to determine the target discharge segments T1′, T2′, T3′, ..., T m ', m≤n; the screening rule includes: on the basis of screening out the minimum number of discharge sub-segments, the discharge duration of any target discharge segment is at least K times the discharge duration of the previous adjacent target discharge segment; S4. For the discharge electronic segments T1′, T2′, T3′, ..., T m ', calculate the amount of electricity Q discharged in the corresponding time period and the change in SOC SOC dif , and then calculate the amount of electricity q released for every 1% change in SOC in the corresponding time period m , where q m =Q / SOC dif , m represents the corresponding target discharge segment number; S5. When the power q1, q2, q3, ..., q corresponding to the target discharge segment m When a monotonically decreasing trend is shown, it is determined that the battery pack has self-discharge.
2. A method for detecting the self-discharge state of a battery based on online data according to claim 1, characterized in that: In step S1 , the obtained discharge segment includes the charging current, discharging current, charging time point, discharging time point, SOC value and battery temperature of the battery.
3. A method for detecting the self-discharge state of a battery based on online data according to claim 2, characterized in that: In step S3, the calculation of the amount of electricity Q discharged in the corresponding time period includes: taking the product of the discharge current and the discharge time interval of the corresponding discharge sub-segment as the amount of electricity Q.
4. The method for detecting the self-discharge state of a battery based on online data according to claim 2, characterized in that: In step S3, the amount of electricity q released for each 1% change in SOC is calculated. m When the battery temperature is adjusted, m The value is temperature corrected.
5. The method for detecting the self-discharge state of a battery based on online data according to claim 1, characterized in that: In step S4, the multiple K is set to a constant greater than 1.
6. A method for detecting self-discharge status in a battery based on online data according to claim 1, characterized in that: After determining that the battery pack has self-discharge, the method also includes recording and storing self-discharge related data, including the time point when self-discharge starts, the duration of self-discharge, the power loss during self-discharge, and the corresponding battery temperature.
7. A method for detecting self-discharge status in a battery based on online data according to claim 1, characterized in that: After determining that the battery pack has self-discharged, the system also triggers corresponding processing measures based on the severity of the self-discharge, including sending a warning message to the user, limiting the power usage of the battery pack or prohibiting the use of the battery pack, and automatically or prompting the user to repair or replace the battery pack.
8. A battery self-discharge state detection system based on online data, characterized in that: The system comprises: Data acquisition module: used to obtain and mark vehicle discharge segment data, including marking the full charge time point A and the time point B when charging starts. The vehicle discharge segment data includes charging current, discharging current, charging time point, discharging time point, SOC value and battery temperature; Data processing module: used to execute the processing steps of the vehicle battery pack self-discharge detection method. Specifically, the module includes: Time segment division unit: used to define the time period between the full charge time point A and the next start charge time point B in the discharge segment data as the discharge sub-segment T, and define the discharge sub-segments T1, T2, ..., T2 in increasing increments according to the discharge duration. n ; A specific time period screening unit is used to screen the discharge sub-segments based on the order of increasing discharge duration and the set screening rules to determine the target discharge segments T1′, T2′, T3′, ..., T m ', m≤n; the screening rule includes: on the basis of screening out the minimum number of discharge sub-segments, the discharge duration of any target discharge segment is at least K times the discharge duration of the previous adjacent target discharge segment; The power calculation unit is used for calculating the discharge sub-segments T1′, T2′, T3′, ..., T m ', calculate the amount of electricity Q discharged in the corresponding time period and the change in SOC SOC dif , and then calculate the amount of electricity q released for every 1% change in SOC in the corresponding time period m , where q m =Q / SOC dif , m represents the corresponding target discharge segment number; Self-discharge judgment unit: used to determine the amount of electricity q1, q2, q3, ..., q corresponding to the target discharge segment m When a monotonically decreasing trend is shown, it is determined that the battery pack has self-discharge.
9. A battery self-discharge state detection system based on online data according to claim 8, characterized in that: The system further comprises: The judgment module receives the self-discharge judgment result from the data processing module. If the judgment result is that the battery pack has self-discharge, the self-discharge related data is recorded and stored, including the time point when the self-discharge starts, the duration of the self-discharge, the power loss during the self-discharge period, and the corresponding battery temperature.
10. A battery self-discharge state detection system based on online data according to claim 9, characterized in that: The judgment module also includes a response unit for sending the self-discharge related data to the user, and triggering corresponding processing measures according to the severity of the self-discharge, including sending a warning message to the user, limiting the power usage of the battery pack or prohibiting the use of the battery pack, and automatically or prompting the user to repair or replace the battery pack.