Power battery safety early warning method based on direct current internal resistance and terminal

Abstract

The invention discloses a power battery safety early warning method based on direct-current internal resistance. The method comprises the following steps: acquiring power battery direct-current internal resistance data in a historically detected charging process, and preliminarily screening the data according to a current step state; grouping the data obtained after preliminary screening according to the temperature and the vehicle model during detection to obtain sample data; performing probability distribution fitting on the sample data by using the t distribution, and generating a corresponding cumulative distribution function according to a probability distribution fitting result; taking data corresponding to a preset quantile in the cumulative distribution function as an internal resistance upper limit threshold; and comparing the currently detected DC internal resistance of the power battery with the internal resistance upper limit threshold, and if the currently detected backflow internal resistance is greater than the internal resistance upper limit threshold, performing alarm processing.

Description

Technical Field

[0001] The present invention relates to the field of safety monitoring, and in particular to a power battery safety early warning method and terminal based on direct current internal resistance. Background Art

[0002] With the rapid development of new energy vehicles, power batteries, as core components, have a direct impact on the overall reliability of the vehicle due to their performance and safety. During vehicle operation and charging, power battery failure or performance degradation may lead to serious safety issues such as overheating, fire, and even explosion. Therefore, safety assessment and early warning of power battery operation and charging have become key technologies to ensure the safe use of vehicles. However, the current evaluation methods for power battery safety still have certain technical bottlenecks.

[0003] Traditional power battery safety assessment methods are mostly based on parameters such as voltage, temperature and current, and are unable to conduct comprehensive real-time monitoring and analysis of the internal state of the battery. These methods may have large errors in the assessment results when dealing with complex charging conditions, diversified vehicle models, and changes in the internal state of the battery. In addition, traditional methods based on battery capacity or energy decay often rely on long-term detection data and are difficult to quickly respond to sudden abnormalities that occur during the charging process. Especially under high current conditions during the charging process, the DC internal resistance of the power battery is an important parameter for evaluating the battery state, and its dynamic changes can directly reflect the health of the battery. However, how to efficiently obtain and analyze DC internal resistance data to achieve accurate safety warnings remains a difficulty in existing technologies. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a power battery safety warning method and terminal based on DC internal resistance to solve the problem of difficulty in safety assessment during power battery charging.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A power battery safety early warning method based on DC internal resistance comprises the following steps: S1. Obtaining the DC internal resistance data of the power battery during the charging process detected historically and preliminarily screening the data according to the current step state; S2, grouping the data obtained after the initial screening according to the temperature and vehicle model during the test to obtain sample data; S3, using t distribution to perform probability distribution fitting on sample data, and generating corresponding cumulative distribution function from the result of probability distribution fitting; S4, taking the data corresponding to the preset quantile in the cumulative distribution function as the upper limit threshold of the internal resistance; S5. Compare the currently detected DC internal resistance of the power battery with the internal resistance upper limit threshold, and if the currently detected reflux internal resistance is greater than the internal resistance upper limit threshold, perform alarm processing.

[0006] In order to solve the above technical problems, another technical solution adopted by the present invention is: A power battery safety warning terminal based on DC internal resistance includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed: S1. Obtaining the DC internal resistance data of the power battery during the charging process detected historically and preliminarily screening the data according to the current step state; S2, grouping the data obtained after the initial screening according to the temperature and vehicle model during the test to obtain sample data; S3, using t distribution to perform probability distribution fitting on sample data, and generating corresponding cumulative distribution function from the result of probability distribution fitting; S4, taking the data corresponding to the preset quantile in the cumulative distribution function as the upper limit threshold of the internal resistance; S5. Compare the currently detected DC internal resistance of the power battery with the internal resistance upper limit threshold, and if the currently detected reflux internal resistance is greater than the internal resistance upper limit threshold, perform alarm processing.

[0007] The beneficial effects of the present invention are: providing a power battery safety warning method and terminal based on DC internal resistance, screening out power battery DC internal resistance data with reference value through the current step when the battery is charging, and innovatively grouping the temperature when the internal resistance is detected and the corresponding vehicle model as sample data, and then using the sample data as the input of the t distribution for probability distribution fitting, and subsequently using the data corresponding to the preset quantile of the cumulative distribution function corresponding to the fitting result as the internal resistance upper limit threshold, and comparing it with the currently detected internal resistance data, thereby completing the safety assessment of the power battery charging process, and efficiently completing the safety assessment of the power battery charging process under complex working conditions and diverse vehicle models. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a flow chart of a power battery safety early warning method based on DC internal resistance in an embodiment of the present invention; Figure 2 This is a schematic diagram of data after the historical data is initially screened according to the current step state in an embodiment of the present invention; Figure 3 It is a schematic diagram of sample data obtained according to the temperature and vehicle model during detection in an embodiment of the present invention; Figure 4It is a schematic diagram of generating a cumulative distribution function after processing sample data using t distribution in an embodiment of the present invention; Figure 5 Schematic diagram of the functional relationship between the upper limit threshold of internal resistance and temperature in an embodiment of the present invention; Figure 6 Schematic diagram of a power battery safety warning terminal based on DC internal resistance in an embodiment of the present invention; Description of labels: 1. A power battery safety warning terminal based on DC internal resistance; 2. Memory; 3. Processor. DETAILED DESCRIPTION

[0009] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0010] Some of the terms used in this invention are explained as follows: Current step: During the charging process, the charging pile actively changes the current, for example, from 0.5C to 0.1C. The current change in this process is called a current step.

[0011] t distribution: t distribution is a probability distribution in statistics, often used to infer population parameters in small sample cases.

[0012] Probability density function: The probability density function (PDF) of the t distribution describes the probability distribution of a random variable that follows the t distribution.

[0013] CDF cumulative curve: The cumulative distribution function (CDF) of the t distribution describes the probability that a random variable is less than or equal to a given value.

[0014] Please refer to Figures 1 to 5 , a power battery safety early warning method based on DC internal resistance, comprising the steps of: S1. Obtaining the DC internal resistance data of the power battery during the charging process detected historically and preliminarily screening the data according to the current step state; S2, grouping the data obtained after the initial screening according to the temperature and vehicle model during the test to obtain sample data; S3, using t distribution to perform probability distribution fitting on sample data, and generating corresponding cumulative distribution function from the result of probability distribution fitting; S4. Take data corresponding to a preset quantile in the cumulative distribution function as the upper limit threshold of the internal resistance, wherein the preset quantile is greater than or equal to 99%, preferably 99% or 99.9%.

[0015] S5. Compare the currently detected DC internal resistance of the power battery with the internal resistance upper limit threshold, and if the currently detected reflux internal resistance is greater than the internal resistance upper limit threshold, perform alarm processing.

[0016] From the above description, it can be seen that the beneficial effects of the present invention are: the DC internal resistance data of the power battery with reference value is screened out through the current step when the battery is charged, and the temperature when the internal resistance is detected and the corresponding vehicle model are innovatively grouped as sample data, and then the sample data is used as the input of the t distribution for probability distribution fitting, and then the data corresponding to the preset quantile of the cumulative distribution function corresponding to the fitting result is used as the upper limit threshold of the internal resistance, which is compared with the currently detected internal resistance data, thereby completing the safety assessment of the power battery charging process, and efficiently completing the safety assessment of the power battery charging process under complex working conditions and diverse vehicle models.

[0017] Please refer to Figures 1 to 3 In an embodiment of the present invention, step S2 further includes: The grouping specifically includes the steps of: The DC internal resistance data corresponding to the temperature in each preset interval of each vehicle model is taken as a set of sample data.

[0018] From the above description, it can be seen that by grouping historical internal resistance data according to vehicle model and temperature, the performance characteristics of the battery under different working conditions can be more accurately reflected, avoiding the misjudgment that may be caused by directly using unclassified messy data. The independent grouping of each temperature range and vehicle model makes the sample data more targeted and comparable, which helps to improve the accuracy of probability distribution fitting and upper threshold calculation. This grouping method can also help discover abnormal internal resistance trends under specific vehicle models or special temperature conditions in big data analysis, and further optimize the battery charging strategy and early warning mechanism.

[0019] In an embodiment of the present invention, step S1 specifically includes the following steps: Obtain the DC internal resistance data of the power battery detected in history, and obtain the current step state during the power battery detection. If the current step value is less than the preset threshold, the corresponding data will be filtered out; The step S4 specifically comprises the following steps: Detect the DC internal resistance and current step state of the current power battery. If the current step value is less than the preset threshold, control the power battery to continue charging until charging is completed; if the current step value is greater than or equal to the preset threshold, compare the DC internal resistance of the currently detected power battery with the internal resistance upper limit threshold. If the currently detected reflux internal resistance is greater than the internal resistance upper limit threshold, perform alarm processing.

[0020] From the above description, it can be seen that by screening the current step state, invalid data caused by insufficient current change amplitude is filtered out, thereby improving the quality of historical data and the reliability of analysis results. At the same time, through dynamic analysis and screening of the current internal resistance detection data, the strategy can be adjusted in real time during the charging process, such as allowing charging to continue when insufficient current steps are detected, thereby avoiding unnecessary charging interruptions due to misjudgment. For situations where the current step is sufficient, it is possible to accurately determine whether the internal resistance exceeds the standard and issue an alarm in time. This method comprehensively considers the accuracy of historical data and the rationality of real-time operations, which not only reduces false alarms, but also improves the accuracy and efficiency of early warnings, and significantly optimizes the battery management strategy.

[0021] Specifically, the preset threshold value is in the range of 10-20A, preferably 15A.

[0022] In an embodiment of the present invention, the alarm processing in step S4 includes the following steps: if the detected internal resistance exceeding the standard is a slight abnormality, adjusting the charging power; if the detected internal resistance exceeding the standard is a moderate abnormality, limiting the charging SOC upper limit; if the detected internal resistance exceeding the standard is a serious abnormality, immediately stopping charging and switching to safety mode.

[0023] From the above description, it can be seen that selecting different charging strategies according to the severity of the internal resistance exceeding the standard provides a more flexible and scientific safety warning mechanism. In the case of minor abnormalities, only adjusting the charging power can avoid charging interruptions and ensure user experience; in the case of moderate abnormalities, by limiting the upper limit of charging SOC, the speed of further deterioration of the battery can be effectively slowed down; and in the case of serious abnormalities, immediately stopping charging and switching to safe mode can timely avoid safety accidents caused by battery overheating or internal failure. The hierarchical processing strategy not only reduces unnecessary alarm interference, but also can flexibly take measures according to actual risks, balancing safety and efficiency.

[0024] In an embodiment of the present invention, step S3 specifically includes the following steps: The mean and standard deviation of the sample data are estimated using t distribution, and then probability distribution fitting is performed, and the result of probability distribution fitting is used to generate the corresponding cumulative distribution function.

[0025] From the above description, it can be seen that by fitting and calculating the mean and standard deviation of the t distribution on the sample data, the probability distribution characteristics of the internal resistance data are accurately described, so that the cumulative distribution function can be generated scientifically and reasonably. This fitting method based on the t distribution can provide more accurate estimation results when the sample size is limited, and is particularly suitable for variables such as the internal resistance of power batteries that are greatly affected by the environment and working conditions. The generation of the cumulative distribution function can quantify the probability characteristics of the data and provide a reliable basis for the subsequent quantile threshold setting.

[0026] Please refer to Figure 6 A power battery safety warning terminal 1 based on DC internal resistance includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, the steps in the power battery safety warning method based on DC internal resistance are completed. That is, an execution carrier of the power battery safety warning method based on DC internal resistance is provided.

[0027] The present invention provides a power battery safety early warning method and terminal based on DC internal resistance, which are mainly used for safety detection of power batteries of electric vehicles during charging. The following is a specific description in conjunction with an embodiment: Please refer to Figures 1 to 5 , Embodiment 1 of the present invention is: A power battery safety early warning method based on DC internal resistance comprises the following steps: S1. Obtain the DC internal resistance data of the power battery during the charging process detected historically and preliminarily screen the data according to the current step state; S2, grouping the data obtained after the initial screening according to the temperature and vehicle model during the test to obtain sample data; S3. Use t distribution to estimate the mean and standard deviation of the sample data and then perform probability distribution fitting, and use the result of probability distribution fitting to generate the corresponding cumulative distribution function.

[0028] S4. Take the data corresponding to the preset quantile in the cumulative distribution function as the upper limit threshold of the internal resistance, and the preset quantile is greater than or equal to 99%; wherein, the preset quantile is selected to be greater than or equal to 99%, and its principle is: as the upper limit threshold, it is to ensure safety in most cases of data. Through this high quantile, it can be ensured that in most cases, the DC internal resistance of the battery will not exceed the set upper limit threshold, thereby avoiding extreme abnormal data or failures, and ensuring that the performance of the battery will not exceed the set DC internal resistance threshold in almost all normal use cases.

[0029] S5. Compare the DC internal resistance of the power battery currently detected with the internal resistance upper limit threshold. If the reflux internal resistance currently detected is greater than the internal resistance upper limit threshold, perform alarm processing.

[0030] That is, in this embodiment, the DC internal resistance data of the power battery with reference value is screened out through the current step when the battery is charging, and the temperature when the internal resistance is detected and the corresponding vehicle model are innovatively grouped as sample data, and then the sample data is used as the input of the t distribution for probability distribution fitting. Subsequently, the data corresponding to the preset quantile of the cumulative distribution function corresponding to the fitting result is used as the upper limit threshold of the internal resistance, which is compared with the currently detected internal resistance data, thereby completing the safety assessment of the power battery charging process, and efficiently completing the safety assessment of the power battery charging process under complex working conditions and diverse vehicle models.

[0031] Embodiment 2 of the present invention is: Based on the first embodiment, step S2 further includes: The grouping process includes the following steps: The DC internal resistance data corresponding to each preset temperature interval of each vehicle model is used as a set of sample data. Specifically, the preset interval is 1°C, that is, the DC internal resistance data corresponding to each temperature interval of each vehicle model is used as a set of sample data.

[0032] That is, by grouping historical internal resistance data according to vehicle model and temperature, the performance characteristics of the battery under different working conditions can be more accurately reflected, avoiding the misjudgment that may be caused by directly using unclassified messy data. The independent grouping of each temperature range and vehicle model makes the sample data more targeted and comparable, which helps to improve the accuracy of probability distribution fitting and upper threshold calculation.

[0033] Embodiment 3 of the present invention is: Based on the first embodiment, step S1 specifically includes the following steps: Obtain the DC internal resistance data of the power battery detected in history, and obtain the current step state during the power battery detection. If the current step value is less than the preset threshold, the corresponding data will be filtered out; Step S4 specifically includes the following steps: Detect the current DC internal resistance and current step status of the power battery. If the current step value is less than 15A, control the power battery to continue charging until charging is completed; if the current step value is greater than or equal to 15A, compare the current detected DC internal resistance of the power battery with the internal resistance upper limit threshold. If the current detected reflux internal resistance is greater than the internal resistance upper limit threshold, perform alarm processing.

[0034] That is, by screening the current step state, invalid data caused by insufficient current change amplitude is filtered out, improving the quality of historical data and the reliability of analysis results. At the same time, through dynamic analysis and screening of the current internal resistance detection data, the strategy can be adjusted in real time during the charging process, such as allowing charging to continue when insufficient current steps are detected, thereby avoiding unnecessary charging interruptions due to misjudgment. For situations where the current step is sufficient, it can accurately determine whether the internal resistance exceeds the standard and issue an alarm in time.

[0035] The alarm processing in step S4 includes the following steps: if the detected internal resistance exceeds the standard and is a slight abnormality, the charging power is adjusted; if the detected internal resistance exceeds the standard and is a moderate abnormality, the charging SOC upper limit is limited; if the detected internal resistance exceeds the standard and is a serious abnormality, the charging is stopped immediately and switched to the safety mode.

[0036] Please refer to Figure 6 , Embodiment 4 of the present invention is: A power battery safety warning terminal based on DC internal resistance includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the power battery safety warning method based on DC internal resistance are completed.

[0037] In summary, the present invention provides a power battery safety warning method and terminal based on DC internal resistance, which can monitor the health status of the power battery in real time under complex working conditions and provide early warning, effectively ensuring the safety and stability of the battery during use. By obtaining historical DC internal resistance data and combining it with the screening of current step states, the data quality is significantly improved, and the interference of invalid data on the analysis results is avoided, thereby improving the accuracy of the prediction. The grouping strategy refines and classifies historical data according to vehicle model and temperature, fully considering the impact of different vehicles and environments, making the analysis results more targeted. By using t distribution to fit the probability distribution, generate the cumulative distribution function and extract the upper limit threshold of the high quantile, the scientificity and reliability of the warning mechanism are effectively ensured, and the abnormal situation of DC internal resistance can be accurately identified.

[0038] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A power battery safety early warning method based on DC internal resistance, characterized in that: Includes steps: S1. Obtaining the DC internal resistance data of the power battery during the charging process detected historically and preliminarily screening the data according to the current step state; S2, grouping the data obtained after the initial screening according to the temperature and vehicle model during the test to obtain sample data; S3, using t distribution to perform probability distribution fitting on sample data, and generating corresponding cumulative distribution function from the result of probability distribution fitting; S4, taking the data corresponding to the preset quantile in the cumulative distribution function as the upper limit threshold of the internal resistance; S5. Compare the currently detected DC internal resistance of the power battery with the internal resistance upper limit threshold, and if the currently detected reflux internal resistance is greater than the internal resistance upper limit threshold, perform alarm processing.

2. A power battery safety early warning method based on DC internal resistance according to claim 1, characterized in that: The step S2 further comprises: The grouping specifically includes the steps of: The DC internal resistance data corresponding to the temperature in each preset interval of each vehicle model is taken as a set of sample data.

3. The power battery safety early warning method based on DC internal resistance according to claim 1, characterized in that: The step S1 specifically includes the following steps: Obtain the DC internal resistance data of the power battery detected in history, and obtain the current step state during the power battery detection. If the current step value is less than the preset threshold, the corresponding data will be filtered out; The step S4 specifically comprises the following steps: Detect the DC internal resistance and current step state of the current power battery. If the current step value is less than the preset threshold, control the power battery to continue charging until charging is completed; If the current step value is greater than or equal to the preset threshold, the DC internal resistance of the power battery currently detected is compared with the internal resistance upper limit threshold. If the reflux internal resistance currently detected is greater than the internal resistance upper limit threshold, an alarm is performed.

4. The power battery safety early warning method based on DC internal resistance according to claim 1, characterized in that: The alarm processing in step S4 includes the steps of: if the detected internal resistance exceeds the standard and is a slight abnormality, adjusting the charging power; if the detected internal resistance exceeds the standard and is a moderate abnormality, limiting the upper limit of the charging SOC; If the detected internal resistance exceeds the standard and is a serious abnormality, charging will be stopped immediately and the vehicle will be switched to safety mode.

5. The power battery safety early warning method based on DC internal resistance according to claim 1, characterized in that: The step S3 specifically comprises the following steps: The mean and standard deviation of the sample data are estimated using t distribution, and then probability distribution fitting is performed, and the result of probability distribution fitting is used to generate the corresponding cumulative distribution function.

6. A power battery safety warning terminal based on DC internal resistance, characterized in that: The invention comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are performed: S1. Obtaining the DC internal resistance data of the power battery during the charging process detected historically and preliminarily screening the data according to the current step state; S2, grouping the data obtained after the initial screening according to the temperature and vehicle model during the test to obtain sample data; S3, using t distribution to perform probability distribution fitting on sample data, and generating corresponding cumulative distribution function from the result of probability distribution fitting; S4, taking the data corresponding to the preset quantile in the cumulative distribution function as the upper limit threshold of the internal resistance; S5. Compare the currently detected DC internal resistance of the power battery with the internal resistance upper limit threshold, and if the currently detected reflux internal resistance is greater than the internal resistance upper limit threshold, perform alarm processing.

7. A power battery safety warning terminal based on DC internal resistance according to claim 6, characterized in that: The step S2 further comprises: The grouping specifically includes the steps of: The DC internal resistance data corresponding to the temperature in each preset interval of each vehicle model is taken as a set of sample data.

8. The power battery safety warning terminal based on DC internal resistance according to claim 6 is characterized by: The step S1 specifically includes the following steps: Obtain the DC internal resistance data of the power battery detected in history, and obtain the current step state during the power battery detection. If the current step value is less than the preset threshold, the corresponding data will be filtered out; The step S4 specifically comprises the following steps: Detect the DC internal resistance and current step state of the current power battery. If the current step value is less than the preset threshold, control the power battery to continue charging until charging is completed; If the current step value is greater than or equal to the preset threshold, the DC internal resistance of the power battery currently detected is compared with the internal resistance upper limit threshold. If the reflux internal resistance currently detected is greater than the internal resistance upper limit threshold, an alarm is performed.

9. The power battery safety warning terminal based on DC internal resistance according to claim 6, characterized in that: The alarm processing in step S4 includes the steps of: if the detected internal resistance exceeds the standard and is a slight abnormality, adjusting the charging power; if the detected internal resistance exceeds the standard and is a moderate abnormality, limiting the upper limit of the charging SOC; If the detected internal resistance exceeds the standard and is a serious abnormality, charging will be stopped immediately and the vehicle will be switched to safety mode.

10. The power battery safety warning terminal based on DC internal resistance according to claim 6, characterized in that: The step S3 specifically comprises the following steps: The mean and standard deviation of the sample data are estimated using t distribution, and then probability distribution fitting is performed, and the result of probability distribution fitting is used to generate the corresponding cumulative distribution function.

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