Power battery safety state detection method, electronic device, storage medium and apparatus

Abstract

The application discloses a power battery safety state detection method, electronic equipment, storage medium and device. The method comprises the following steps: charging the power battery based on a pulse charging strategy, collecting battery pulse charging data of each pulse charging stage, battery quasi-static data of each quasi-static stage and charging end time data, and performing data cleaning; determining the battery monomer with the highest voltage in each quasi-static stage based on the battery quasi-static data after data cleaning, and calculating the frequency of each battery monomer appearing in all quasi-static stages; identifying whether there is an outlier battery monomer in all battery monomers based on the frequency through a k-means clustering algorithm; and analyzing the safety state of the power battery based on the identification result, the battery pulse charging data, the battery quasi-static data and the charging end time data. The power battery is charged by adopting the pulse charging strategy, and the pulse charging data is collected, so that the battery safety state detection is realized without cloud data.

Description

Technical Field

[0001] This invention belongs to the field of power battery testing technology, and more specifically, relates to a method, electronic device, storage medium and apparatus for testing the safety status of a power battery. Background Technology

[0002] As a key component of new energy vehicles, the power battery is crucial to their development. However, with increased usage time, charging cycles, and abnormal operating conditions, power batteries inevitably age, leading to performance degradation and even safety hazards such as internal short circuits, thermal runaway, and incalculable consequences. Therefore, regular analysis of battery operating data is essential to evaluate battery safety and ensure reliable operation. However, vehicle operating data is only uploaded to the OEM and national monitoring platforms, ensuring strict confidentiality and preventing external access. Furthermore, vehicle charging strategies are fixed, making it impossible to adjust them based on algorithm requirements. While data uploads are generally available in real-time during charging and driving, most vehicles do not upload data in real-time during stationary states. Data uploads are typically timed or not uploaded at all, making it impossible to analyze the changes in battery voltage, temperature, and other external characteristics during stationary states.

[0003] Currently, battery safety status assessments are all based on actual vehicle operating data. These data collection methods are rich in fields and involve long time periods. Technical solutions employ multi-dimensional analysis of battery safety levels, or analyze battery internal resistance, lithium plating levels, and battery defects to identify battery safety risks. Patent application CN202210664642.6 discloses acquiring multiple charging segments, calculating the standard deviation and variance entropy consistency characteristics of the single-cell voltage for each charging process, constructing a status assessment alarm level model, and outputting the battery safety level result. Patent application CN202210798403.X discloses acquiring constant current charging segments, calculating voltage range, temperature inconsistency, overall temperature rise rate, statistically analyzing the proportion of outlier cells at the start and end of charging, and the Mahalanobis distance of various indicators, and evaluating battery safety performance based on the Mahalanobis distance distribution. Patent application CN202310582532.X discloses a method for calculating the ratio of the absolute value of the integral change in current and time to the absolute value of the voltage change under different vehicle discharge voltages. This method establishes a curve function with the absolute value ratio as the Y-axis and voltage as the X-axis. If the curve function first decreases, then increases, and then decreases again, it indicates a lithium plating safety risk during the previous charging process. Patent application CN202110953856.0 discloses a method for obtaining the open-circuit voltage, AC internal resistance, and self-discharge rate of a battery under test at high temperatures. It then performs capacity anomaly screening based on machine learning, obtains the open-circuit voltage, AC internal resistance, and self-discharge rate of the battery under test at room temperature, performs battery internal resistance anomaly screening, and finally performs a final anomaly screening of the battery under test.

[0004] The aforementioned technical solutions are mostly based on cloud-based big data, requiring large amounts of data and extracting numerous fields. However, they are unsuitable for offline battery safety status testing, and some solutions can only identify safety risk levels but cannot pinpoint the root cause. For example, CN202210664642.6 requires acquiring multiple charging segments to construct a status assessment alarm level model; CN202210798403.X acquires constant current charging segments and individual cell voltages, then evaluates battery safety performance based on Mahalanobis distance distribution; however, these data requirements cannot be met by offline data acquisition equipment. CN202310582532.X identifies lithium plating safety risks during charging by acquiring discharge data, but for data from actual operating vehicles, the discharge conditions are complex, data upload frequency is high, making it difficult to identify lithium plating safety risks. Furthermore, this solution requires discharge data to correspond with charging data, making it unsuitable for battery swapping vehicles and offline testing scenarios. The CN202110953856.0 solution can identify abnormal battery safety status types, but requires high-temperature battery data, making it unsuitable for normal vehicle operating conditions.

[0005] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to propose a method, electronic device, storage medium, and apparatus for detecting the safety status of a power battery. This invention enables the offline collection of pulse charging data during the pulse charging phase and quasi-static data during the adjacent quasi-static phase of the entire charging process, without cloud data. This allows for the analysis of the variation patterns of battery characteristic parameters, thereby identifying whether the battery exhibits lithium plating or high-charge-low-discharge phenomena. This enables battery safety status detection without cloud data.

[0007] To achieve the above objectives, the present invention proposes a method, electronic device, storage medium, and apparatus for detecting the safety status of a power battery.

[0008] According to a first aspect of the present invention, a method for detecting the safety status of a power battery is provided, comprising:

[0009] The power battery is charged based on a pulse charging strategy, and battery pulse charging data for each pulse charging stage and battery quasi-static data for the adjacent quasi-static stage after each pulse charging stage are collected, as well as the charging end time data of the power battery at the charging end time.

[0010] After charging is completed, all battery pulse charging data, all battery quasi-static data, and charging end time data are cleaned.

[0011] Based on the cleaned battery quasi-static data, the battery cell with the highest voltage in each quasi-static stage is determined, and the frequency of each battery cell appearing in all quasi-static stages is calculated.

[0012] Based on the frequency, the k-means clustering algorithm is used to identify whether there are outlier battery cells among all the battery cells.

[0013] The safety status of the power battery is determined based on the analysis of the identification results, all the battery pulse charging data, all the battery quasi-static data, and the charging end time data.

[0014] Optionally, the pulse charging strategy includes:

[0015] The power battery is charged alternately during the charging phase and the quasi-static phase until the charging cutoff condition is triggered to complete the charging process.

[0016] The charging phase includes a pulse charging phase and a normal charging phase. The pulse charging phase and the normal charging phase alternately charge the power battery to obtain battery pulse charging data under different SOC states and charge the power battery.

[0017] During the quasi-static stage, the charging rate of the power battery is 0.05C;

[0018] During the charging phase, the charging rate of the power battery is 1C;

[0019] The 0.05C is 0.05 times the maximum allowable charging current required by the BMS for the power battery under the current SOC and temperature conditions;

[0020] The 1C refers to the maximum allowable charging current required by the BMS for the power battery under the current SOC and temperature conditions.

[0021] Optionally, the state of charge (SOC) of the power battery is less than 40% before charging.

[0022] Optionally, the data cleaning includes:

[0023] Delete duplicate and abnormal data from the battery pulse charging data, the battery quasi-static data, and the charging end time data.

[0024] Optionally, the analysis of the safety status of the power battery based on the identification results includes:

[0025] If a single outlier battery cell exists, the power battery is analyzed for lithium plating safety risks based on the battery pulse charging data and the battery quasi-static data.

[0026] When multiple outlier battery cells exist, the power battery is analyzed for potential safety risks related to high charging and low discharging based on the battery pulse charging data, the battery quasi-static data, and the charging end time data.

[0027] Optionally, the analysis of whether the power battery poses a lithium plating safety risk includes:

[0028] Based on the battery pulse charging data and the battery quasi-static data, calculate the voltage drop and corresponding internal resistance value of the outlier battery cell at the end of each pulse charging phase and at each quasi-static moment of the quasi-static phase adjacent to each pulse charging phase.

[0029] Based on the total voltage of the power battery at the end of each pulse charging phase and the number of series connections of the power battery, the average voltage of the battery cell at the end of each pulse charging phase is calculated, and then the average voltage drop and the corresponding average internal resistance value are calculated at each quasi-static moment of the quasi-static phase adjacent to each pulse charging phase at the end of each pulse charging phase.

[0030] Based on the average internal resistance value and the internal resistance value, calculate the ratio of the average internal resistance value to the internal resistance value at each quasi-static moment in the intermediate stage of each quasi-static stage, and obtain the maximum value of the ratio of the average internal resistance value to the internal resistance value in each quasi-static stage.

[0031] When the average internal resistance value is greater than the internal resistance value, and the maximum value of the ratio of the average internal resistance value to the internal resistance value is greater than the set safety threshold during the quasi-static stage, the power battery is subject to the lithium plating safety risk.

[0032] Optionally, the analysis of whether the power battery has a high-charge-low-discharge safety risk includes:

[0033] Based on the battery pulse charging data, the numbers of the battery cells with the highest voltage at the start and end of each pulse charging phase are determined and denoted as the first number and the second number, respectively.

[0034] Based on the battery quasi-static data, the numbers of the battery cells with the highest voltage at the beginning and end of each quasi-static stage are determined and denoted as the third number and the fourth number, respectively.

[0035] Based on the charging end time data, the SOC values ​​of the first battery cell with the highest voltage, the second battery cell with the lowest voltage, and the power battery at the charging end time are determined respectively. The first voltage value corresponding to the first battery cell and the second voltage value of the second battery cell are obtained. The voltage difference at the charging end time is calculated based on the first voltage value and the second voltage value. The voltage difference threshold of the power battery is determined based on the SOC value and the material system of the power battery.

[0036] When, in a single charging-resting stage consisting of a pulse charging stage and an adjacent quasi-resting stage, the second number is the same as the third number and the second number and the third number are different from both the first number and the fourth number, and the voltage difference is greater than the voltage difference threshold corresponding to the power battery, then the power battery has a high-charge-low-discharge safety risk.

[0037] According to a second aspect of the present invention, a method for detecting the safety status of a power battery is provided, comprising:

[0038] The charging and data acquisition module is used to charge the power battery based on the pulse charging strategy and acquire the battery pulse charging data of each pulse charging stage and the battery quasi-static data of the quasi-static stage adjacent to each pulse charging stage, as well as the charging end time data of the power battery at the charging end time.

[0039] The data cleaning module is used to clean all the battery pulse charging data, all the battery quasi-static data and the charging end time data after charging is completed;

[0040] The determination and calculation module determines the battery cell with the highest voltage in each of the quasi-static stages based on the battery quasi-static data after data cleaning, and calculates the frequency of each battery cell appearing in all of the quasi-static stages.

[0041] The identification module is used to identify whether there are outlier battery cells among all the battery cells based on the frequency using a k-means clustering algorithm.

[0042] The analysis module is used to analyze the safety status of the power battery based on the identification results, all the battery pulse charging data, all the battery quasi-static data, and the charging end time data.

[0043] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0044] At least one processor; and,

[0045] A memory communicatively connected to the at least one processor; wherein,

[0046] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the power battery safety status detection method according to any of the first aspects.

[0047] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, the non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the power battery safety state detection method described in any of the first aspects.

[0048] The beneficial effects of this invention are as follows: This invention charges the power battery using a pulse charging strategy. The charging phase includes a pulse charging phase and a normal charging phase, i.e., charging is performed in a cyclical manner of quasi-static-pulse charging-quasi-static-normal charging-quasi-static until the charging cutoff condition is triggered, and the charging ends. The pulse charging data of the power battery during the pulse charging phase and the quasi-static data of the adjacent quasi-static phase after the pulse charging phase, as well as the charging end time data of the power battery at the end of the charging, are extracted. By analyzing the pulse charging data and the corresponding quasi-static data, the change pattern of the battery cell with the highest voltage is analyzed. Based on the change pattern, it is determined whether there are outlier battery cells. If there is one outlier battery cell, the power battery is detected for lithium plating safety risks; if there are multiple outlier battery cells, the power battery is detected for high charge and low discharge safety risks. This enables battery safety status detection without cloud data.

[0049] The system of the present invention has other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0050] The above and other objects, features and advantages of the present invention will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0051] Figure 1 A flowchart illustrating the steps of a power battery safety status detection method according to the present invention is shown.

[0052] Figure 2 A flowchart illustrating the steps of a power battery safety status detection method according to Embodiment 1 of the present invention is shown.

[0053] Figure 3 A schematic diagram of the relaxation voltage curve according to Embodiment 1 of the present invention is shown.

[0054] Figure 4 A schematic diagram of the voltage ranking curves of each battery cell according to Embodiment 1 of the present invention is shown.

[0055] Figure 5 A step diagram of the pulse charging strategy according to Embodiment 2 of the present invention is shown.

[0056] Figure 6 A schematic diagram of the clustering results of the K-means clustering algorithm according to Embodiment 2 of the present invention is shown.

[0057] Figure 7 A schematic diagram of the relaxation voltage curve according to Embodiment 2 of the present invention is shown.

[0058] Figure 8 A schematic diagram of the clustering results of the K-means clustering algorithm according to Embodiment 3 of the present invention is shown. Detailed Implementation

[0059] The invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0060] like Figure 1 As shown, a method for detecting the safety status of a power battery according to the present invention includes:

[0061] The power battery is charged based on a pulse charging strategy, and the battery pulse charging data of each pulse charging stage and the battery quasi-static data of each adjacent quasi-static stage after each pulse charging stage are collected, as well as the charging end time data of the power battery at the charging end time.

[0062] After charging is complete, perform data cleaning on all battery pulse charging data, all battery quasi-static data, and charging end time data.

[0063] Based on the cleaned quasi-static battery data, the battery cell with the highest voltage in each quasi-static stage is identified, and the frequency of each battery cell appearing in all quasi-static stages is calculated.

[0064] Based on frequency, the k-means clustering algorithm is used to identify whether there are outlier battery cells among all battery cells.

[0065] The safety status of the power battery is analyzed based on the identification results, all battery pulse charging data, all battery quasi-static data, and charging end time data.

[0066] Specifically, this invention employs a pulse charging strategy to charge the power battery. The entire charging process is divided into a charging phase and a resting phase. Due to factors related to the BMS (Battery Management System), the current cannot reach zero during the resting phase; therefore, a small current is used for charging during this phase, which is called the quasi-resting phase. The charging phase and the quasi-resting phase alternate until charging is complete. The charging phase includes a pulse charging phase and a normal charging phase. A charging phase with a charging duration less than a set charging duration threshold is considered a pulse charging phase. This pulse charging strategy allows for the acquisition of power battery data during the pulse charging phase and the adjacent quasi-resting phase, as well as charging end time data, without cloud data. After extracting the battery pulse charging data for each pulse charging phase and the battery quasi-static data and charging end time data for each adjacent quasi-resting phase, data cleaning is performed to remove duplicate and abnormal data. Duplicate data leads to data redundancy, affecting data processing efficiency, while abnormal data interferes with subsequent analysis results, affecting accuracy. For example, if data with identical time points and content exists, only one is retained, and the others are deleted. The quasi-static battery data includes charging process data, such as time, charging current, maximum voltage of a single cell, cell maximum voltage number, SOC value, total voltage, maximum temperature value, maximum temperature probe number, minimum temperature value, and minimum temperature probe number. Data at the end of charging includes time, maximum voltage of a single cell, minimum voltage of a single cell, maximum temperature value, minimum temperature value, and SOC value. Then, the corresponding battery cell is identified through the maximum voltage number in the quasi-static data. Each battery cell has its own unique number and corresponding temperature probe number. The frequency of the corresponding battery cell is determined based on the frequency of the maximum voltage number in the adjacent quasi-static phase after all pulse charging phases. Based on the frequency of the battery cell occurrences, a k-means clustering algorithm is used to identify any outlier cells among the battery cells with the highest voltage, and the data for these outlier cells is collected. If one outlier cell is found, it is necessary to check whether the power battery exhibits lithium plating; if so, it indicates a safety risk. If multiple outlier cells are found, it is necessary to check whether the power battery exhibits high-charge-low-discharge behavior; if so, it indicates a safety risk.This invention utilizes a pulse charging strategy to charge power batteries, obtaining pulse charging data and quasi-static data. This data is then used to analyze the variation patterns of the highest-voltage battery cells and determine the presence of outlier cells. If an outlier cell exists, the voltage drop and internal resistance of the outlier cell are calculated at each quasi-static moment in each pulse charging phase and the subsequent adjacent quasi-static phase, along with the average voltage drop and average internal resistance. These differences are analyzed to detect lithium plating safety risks. If multiple highest-voltage battery cells exhibit significant outlier behavior, the highest-voltage cell numbers are extracted from the data at the beginning and end of the pulse charging phase and the quasi-static phase. The relationship between these numbers and the corresponding battery cells at different times during the pulse charging and quasi-static phases is analyzed. The voltage difference between the highest and lowest-voltage cells at the end of the charging phase is calculated based on the charging end data, thereby identifying any abnormal high-charge / low-discharge phenomena and enabling battery safety status detection even without cloud data.

[0067] In one example, the pulse charging strategy includes:

[0068] The charging phase and the semi-static phase alternately charge the power battery until charging is complete.

[0069] The charging phase includes a pulse charging phase and a normal charging phase, and the power battery is charged alternately by the pulse charging phase and the normal charging phase.

[0070] During the quasi-static stage, the charging rate of the power battery is 0.05C;

[0071] During the charging phase, the charging rate of the power battery is 1C.

[0072] Specifically, the pulse charging strategy of the present invention includes a charging phase and a quasi-static phase, during which the power battery is charged alternately until charging is complete. The charging phase includes a pulse charging phase and a normal charging phase, during which the power battery is charged alternately. A charging phase with a charging duration less than a set charging duration threshold is a pulse charging phase. The entire pulse charging strategy cycles in the order of quasi-static - pulse charging - quasi-static - normal charging - quasi-static until the charging cutoff condition is triggered and charging is complete. This strategy enables the collection of battery pulse charging data and corresponding quasi-static data under different SOCs, improving the reliability of the data and achieving the purpose of charging the power battery.

[0073] In one example, the SOC of the power battery was less than 40% before charging.

[0074] In one example, data cleaning includes:

[0075] Delete duplicate and abnormal data from battery pulse charging data, battery quasi-static data, and charging end time data.

[0076] Specifically, data cleaning is the process of re-examining and verifying data, with the aim of removing duplicate information, correcting existing errors, and providing data consistency. There are many methods of data cleaning, and this invention is not limited to any data cleaning method that can achieve the data cleaning objectives of this invention.

[0077] In one example, analyzing the safety status of a power battery based on the identification results includes:

[0078] If there is an outlier battery cell, the power battery is analyzed for lithium plating safety risks based on battery pulse charging data and quasi-static battery data.

[0079] When multiple outlier battery cells exist, the power battery is analyzed for potential safety risks of high charging and low discharging based on battery pulse charging data, quasi-static battery data, and charging end time data.

[0080] In one example, analyzing whether a power battery poses a lithium plating safety risk includes:

[0081] Based on battery pulse charging data and battery quasi-static data, the voltage drop and corresponding internal resistance of outlier battery cells at the end of each pulse charging phase and at each quasi-static moment of the adjacent quasi-static phase following each pulse charging phase are calculated.

[0082] Based on the total voltage of the power battery at the end of each pulse charging phase and the number of series connections of the power battery, the average voltage of the battery cell at the end of each pulse charging phase is calculated. Then, the average voltage drop and the corresponding average internal resistance value at each quasi-static moment of the quasi-static phase adjacent to each pulse charging phase are calculated.

[0083] Based on the average internal resistance value and the internal resistance value, calculate the ratio of the average internal resistance value to the internal resistance value at each quasi-static moment in the intermediate stage of each quasi-static stage, and obtain the maximum value of the ratio of the average internal resistance value to the internal resistance value in each quasi-static stage.

[0084] When there is a quasi-static stage where the average internal resistance is greater than the internal resistance value and the maximum ratio of the average internal resistance value to the internal resistance value is greater than the set safety threshold, the power battery is at risk of lithium plating.

[0085] Specifically, the voltage value of the outlier battery cell at the end of each pulse charging phase is obtained from battery pulse charging data. This voltage value is the highest voltage value of the outlier battery cell in each pulse charging phase. The voltage value of the outlier battery cell at each quasi-static moment in the adjacent quasi-static phase following each pulse charging phase is obtained from battery quasi-static data. Then, the voltage drop of the outlier battery cell at the end of each pulse charging phase and at each quasi-static moment in the adjacent quasi-static phase is calculated. That is, the voltage value of the outlier battery cell at the end of each pulse charging phase and the voltage value at each quasi-static moment in the adjacent quasi-static phase are subtracted one by one. Each pulse charging phase corresponds to multiple voltage drops. Then, based on the current difference between the voltage value of the outlier battery cell at the end of each pulse charging phase and the current difference at each quasi-static moment in the adjacent quasi-static phase, the voltage drop of the outlier battery cell at the end of each pulse charging phase and at each quasi-static moment in the adjacent quasi-static phase is calculated. The internal resistance value at the quasi-static moment is calculated. Then, the total voltage of the power battery at the end of each pulse charging stage and the number of series connections of the power battery are obtained from the battery pulse charging data to calculate the average voltage of the battery cells at the end of each pulse charging stage. Then, the average voltage drop and corresponding average internal resistance value are calculated for each quasi-static moment of the adjacent quasi-static stage after each pulse charging stage. Based on the average internal resistance value and corresponding internal resistance value of each quasi-static moment of each pulse charging stage and the adjacent quasi-static stage after that pulse charging stage, the ratio of the average internal resistance value to the internal resistance value of each quasi-static moment in the middle stage of each quasi-static stage is calculated. The maximum value of the ratio of the average internal resistance value to the internal resistance value of each quasi-static stage is obtained. The current and voltage at the quasi-static moment in the middle stage of each quasi-static stage are more stable than those at the front and rear stages, and the calculation results are more reliable. When there is a quasi-static stage where the average internal resistance value is greater than the internal resistance value and the maximum value of the ratio of the average internal resistance value to the internal resistance value is greater than the set safety threshold, the power battery has a lithium plating safety risk.

[0086] In one example, analyzing whether a power battery poses a safety risk of overcharging and undercharging includes:

[0087] Based on the battery pulse charging data, the number of the battery cell with the highest voltage at the beginning and end of each pulse charging phase is determined and denoted as the first number and the second number, respectively.

[0088] Based on the quasi-static data of the battery, the numbers of the battery cells with the highest voltage at the beginning and end of each quasi-static stage are determined and denoted as the third number and the fourth number, respectively.

[0089] Based on the data at the end of charging, the SOC values ​​of the first battery cell with the highest voltage, the second battery cell with the lowest voltage, and the power battery at the end of charging are determined respectively. The first voltage value corresponding to the first battery cell and the second voltage value of the second battery cell are obtained. The voltage difference at the end of charging is calculated based on the first voltage value and the second voltage value. The voltage difference threshold of the power battery is determined based on the SOC value and the material system of the power battery.

[0090] When, in a single charging-resting stage consisting of a pulse charging stage and an adjacent quasi-resting stage, the second and third numbers are the same, and the second and third numbers are different from the first and fourth numbers, and the voltage difference is greater than the voltage difference threshold corresponding to the power battery, then the power battery has a high-charge-low-discharge safety risk.

[0091] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the invention. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.

[0092] Example 1

[0093] like Figure 2 As shown, this embodiment provides a method for detecting the safety status of a power battery, including:

[0094] Step 1: Charge the vehicle using offline testing equipment, requiring the initial SOC to be less than 40%. Charge according to the set pulse charging strategy, including a battery charging phase and a battery quasi-resting phase. The battery charging phase includes a pulse charging phase and a normal charging phase. Collect charging data for the pulse charging phase and the adjacent quasi-resting phase. During the battery charging phase, the charging rate of the power battery is 1C, and during the quasi-resting phase, the charging rate of the power battery is 0.05C. 0.05C is 0.05 times the maximum allowable charging current required by the BMS (Battery Management System) under the current SOC and temperature conditions; 1C is the maximum allowable charging current required by the BMS under the current SOC and temperature conditions. The duration of each phase can be adjusted according to the total charging time requirement. The pulse charging strategy in this embodiment is shown in Table 1.

[0095] Table 1. Pulse Charging Strategy Steps

[0096]

[0097]

[0098] Step 2: Perform data cleaning on the charging data for each stage. After completion, extract the pulse charging data for the pulse charging stage and the quasi-static data of the battery in the adjacent quasi-static stage. Both the pulse charging data and the quasi-static data include charging process data, which includes time, charging current, maximum voltage of a single cell, maximum voltage number of a single cell, SOC value, total voltage, maximum temperature value, maximum temperature probe number, minimum temperature value, minimum temperature probe number, etc. Also, extract the charging end time data, which includes time, maximum voltage of a single cell, minimum voltage of a single cell, maximum temperature value, minimum temperature value, SOC value, etc.

[0099] Step 3: Extract the highest voltage number field of each cell from the quasi-static data. Each number corresponds to a cell. Calculate the frequency of the highest voltage cell in each quasi-static stage in all quasi-static stages. Automatically identify outlier cells from these cells using the K-means clustering algorithm. If the number is unique, the cell corresponding to that number is recorded as Nx, and proceed to Step 4; if multiple outlier cells are identified, proceed to Step 5.

[0100] Step 4: Extract the data at the end of each pulse charging phase and the quasi-static data of the adjacent quasi-static phase following the pulse charging phase for cell Nx. Each pulse charging phase and its adjacent quasi-static phase constitute a pulse charging static phase. Calculate the voltage (V) of cell Nx at the end of each pulse charging static phase. pi ) and the voltage (V) at each quasi-static moment in the quasi-static phase. st(ij) Voltage drop (ΔV) Nx The corresponding internal resistance value is denoted as R. Nx ; where ΔV Nx(ij) =V pi -V st(ij)， R Nx(ij) =ΔV Nx(ij) / ΔI, where i represents the i-th pulse charging resting stage, j represents the j-th quasi-static moment in the quasi-resting stage within the i-th pulse charging resting stage, and ΔI is the current difference between the pulse charging end time of each pulse charging resting stage and each quasi-static moment in the quasi-resting stage; then, the total battery voltage (V) at the pulse charging end time of each pulse charging resting stage is extracted. (i) Given the number of batteries connected in series (N), calculate the end time (V) of each pulse charging resting phase. avgpi ) and each quasi-static moment (V) in the quasi-static phase st(ij) The average voltage drop (ΔV) avg(ij) The corresponding average internal resistance is denoted as R. avg(ij) Among them, V avgpi =V (i) / N, ΔV avg(ij) =V avgpi -V st(ij) ;

[0101] R avg(ij) =ΔV avg(ij) / ΔI, and calculate R at each time point in each quasi-static phase. avg(ij) / R Nx(ij) The value is denoted as α. Taking the intermediate data from the quasi-static stage within each pulse charging static stage, the maximum value of α is calculated and denoted as αmax. When R... avg(ij) >R Nx(ij) When αmax > P, P = 1.5 is preferred, where P is a set safety threshold; otherwise, the battery is considered to have a lithium plating safety risk. Figure 3 As shown, if lithium plating occurs during the charging process, lithium re-intercalation will occur when the battery is left to stand after charging, leading to an increase in the battery relaxation voltage and a decrease in the battery internal resistance.

[0102] Step 5: Extract the highest voltage number field of each cell from the data at the beginning and end of each pulse charging stage and the data at the beginning and end of the adjacent quasi-static stage, respectively, denoted as Npulse_s, Npulse_e, Nstatic_s, and Nstatic_e. Extract the highest and lowest voltage of each cell at the end of charging, calculate the voltage difference between the highest and lowest voltages at the end of charging, denoted as ΔV. Extract the voltage difference threshold corresponding to the power battery in Table 2 based on the battery material and the SOC value of the power battery at the end of charging, denoted as ΔVt. When the data of multiple pulse charging stages and the adjacent quasi-static stage all meet the following conditions, Npulse_e = Nstatic_s ≠ Npulse_s; Npulse_e =

[0103] Nstatic_s≠Nstatic_e; ΔV>ΔVt; This indicates a high-charge-low-discharge phenomenon in the power battery, posing a safety risk. Because offline data collection equipment cannot obtain the voltage values ​​of individual battery cells during charging, but only the voltage value at the end of charging, to better illustrate the basis for the safety assessment judgment, such as... Figure 4 As shown, the isolated battery cell is not the highest voltage cell in the initial stage, but becomes the highest voltage cell in the final stage, indicating that the battery is severely polarized. The instantaneous large current causes the voltage to rise rapidly, resulting in the phenomenon of high charge and low discharge.

[0104] Table 2 Pressure Thresholds for Different Material Systems

[0105]

[0106] Example 2

[0107] This embodiment provides a method for detecting the safety status of a power battery, including:

[0108] A vehicle equipped with a ternary lithium battery system and having traveled approximately 200,000 kilometers was selected. The vehicle was charged using offline testing equipment according to the pulse charging strategy shown in Table 3. The initial SOC (State of Charge) was required to be less than 40%. During the initial charging phase, the battery charging rate was 1C, and during the quasi-resting phase, the charging rate was 0.05C. 0.05C represents 0.05 times the maximum allowable charging current required by the BMS (Battery Management System) at the current SOC and temperature. 1C represents the maximum allowable charging current required by the BMS at the current SOC and temperature. The duration of each phase is shown in Table 3. Phases with a charging duration of 120 seconds or less are considered pulse charging phases, while phases longer than 120 seconds are considered normal charging phases. The pulse charging strategy flowchart for this embodiment is shown below. Figure 5 As shown.

[0109] Table 3. Pulse Charging Strategy Steps

[0110]

[0111]

[0112] Data cleaning was performed on the charging data for each stage. After cleaning, pulse charging data from the pulse charging stage and quasi-static data from the adjacent quasi-resting stage were extracted. Data at the end of charging was also extracted. The highest voltage number field for each individual cell was extracted from the quasi-static data, with each number corresponding to a single cell. The frequency of the highest voltage cell in each quasi-resting stage across all quasi-resting stages was calculated. Outlier cells were automatically identified from these cells using the K-means clustering algorithm. The clustering results in this embodiment are as follows: Figure 6 As shown, there exists an outlier battery cell with a unique number, denoted as Nx. Data at the end of pulse charging and quasi-static data after pulse charging are extracted from battery cell Nx. Each pulse charging stage and its adjacent quasi-static stage constitute a pulse charging static stage. The pulse charging end time (V) of battery cell Nx in each pulse charging static stage is calculated. pi ) and each quasi-static moment (V) in the quasi-static phase st(ij) Voltage drop (ΔV) Nx The corresponding internal resistance value is denoted as R. Nx ; where ΔV Nx(ij) =V pi -V st(ij)， R Nx(ij) =ΔV Nx(ij) / ΔI, where i represents the i-th pulse charging resting stage, and j represents the j-th quasi-static moment in the quasi-resting stage within the i-th pulse charging resting stage; then, the total battery voltage (V) at the end of each pulse charging resting stage is extracted.(i) Given the number of batteries connected in series (N), calculate the end time (V) of each pulse charging resting phase. avgpi ) and each quasi-static moment (V) in the quasi-static phase st(ij) The average voltage drop (ΔV) avg(ij) The corresponding average internal resistance is denoted as R. avg(ij) Among them, V avgpi =V (i) / N, ΔV avg(ij) =V avgpi -V st(ij) ;R avg(ij) =ΔV avg(ij) / ΔI, and calculate R at each time point in each quasi-static phase. avg(ij) / R Nx(ij) The value is denoted as α. Taking the intermediate data from the quasi-static stage within each pulse charging static stage, the maximum value of α is calculated and denoted as αmax. For example... Figure 7 As shown, αmax = 1.88 and R avg(ij) >R Nx(ij) When αmax > P, P = 1.5, where P is the set safety threshold, then the power battery exhibits lithium plating, posing a safety risk.

[0113] Example 3

[0114] This embodiment provides a method for detecting the safety status of a power battery, including:

[0115] A vehicle equipped with a ternary lithium battery system and having traveled approximately 100,000 kilometers was selected. The vehicle was charged using the pulse charging strategy described in Example 2. Data cleaning was performed on the charging data for each stage. After completion, pulse charging data for each pulse charging stage, quasi-static battery data for the quasi-resting stage, and charging end time data were extracted. The highest voltage number field for each individual battery cell was extracted from the quasi-static data, with each number corresponding to a single battery cell. The frequency of the highest voltage battery cell appearing across all quasi-resting stages was calculated. Outlier battery cells were automatically identified from these cells using the K-means clustering algorithm. The clustering results of this example are as follows: Figure 8As shown, multiple outlier battery cells were identified. The highest voltage number field of each cell was extracted from the data at the beginning and end of the pulse charging phase and the beginning and end of the quasi-static phase, respectively denoted as Npulse_s, Npulse_e, Nstatic_s, and Nstatic_e. The highest and lowest voltages of each cell at the end of charging were obtained based on the data at the end of charging. The voltage difference between the highest and lowest voltages at the end of charging was calculated and denoted as ΔV. The voltage difference threshold corresponding to the power battery in Table 2 was extracted based on the battery material and the SOC value of the power battery at the end of charging and denoted as ΔVt. The calculation results are shown in Table 4. At the end of charging, the SOC value of the power battery was 100%, and the voltage difference was 370mV, which is greater than the corresponding voltage difference threshold of 350mV. Furthermore, Npulse_e = Nstatic_s ≠ Npulse_s. If Npulse_e = Nstatic_s ≠ Nstatic_e, then the battery is considered to have a high-charge-low-discharge phenomenon, and the power battery has a safety risk.

[0116] Table 4. Statistics of the highest single-unit number results

[0117]

[0118]

[0119] Example 4

[0120] This embodiment provides a method for detecting the safety status of a power battery, including:

[0121] The charging and data acquisition module is used to charge the power battery based on the pulse charging strategy and acquire the battery pulse charging data of each pulse charging stage, the battery quasi-static data of the adjacent quasi-static stage after each pulse charging stage, and the charging end time data of the power battery at the end of charging.

[0122] The data cleaning module is used to clean all battery pulse charging data, all battery quasi-static data, and charging end time data after charging is completed.

[0123] The determination and calculation module determines the battery cell with the highest voltage in each quasi-static stage based on the cleaned battery quasi-static data, and calculates the frequency of each battery cell in all quasi-static stages.

[0124] The identification module is used to identify whether there are outlier battery cells among all battery cells based on frequency using the k-means clustering algorithm.

[0125] The analysis module is used to analyze the safety status of the power battery based on the identification results, all battery pulse charging data, all battery quasi-static data, and charging end time data.

[0126] Example 5

[0127] This embodiment provides an electronic device, which includes:

[0128] At least one processor; and,

[0129] A memory communicatively connected to the at least one processor; wherein,

[0130] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the power battery safety status detection method in Embodiment 1.

[0131] An electronic device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0132] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0133] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0134] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0135] Example 6

[0136] This embodiment provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to execute the power battery safety status detection method in Embodiment 1.

[0137] The computer-readable storage medium according to this embodiment stores non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods of the foregoing embodiments are performed.

[0138] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0139] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for detecting the safety status of a power battery, characterized in that, include: The power battery is charged based on a pulse charging strategy, and battery pulse charging data for each pulse charging stage and battery quasi-static data for the adjacent quasi-static stage after each pulse charging stage are collected, as well as the charging end time data of the power battery at the charging end time. After charging is completed, all battery pulse charging data, all battery quasi-static data, and charging end time data are cleaned. Based on the cleaned battery quasi-static data, the battery cell with the highest voltage in each quasi-static stage is determined, and the frequency of each battery cell appearing in all quasi-static stages is calculated. Based on the frequency, k-means clustering algorithm is used to identify whether there are outlier battery cells among all the battery cells. The safety status of the power battery is analyzed based on the identification results, all battery pulse charging data, all battery quasi-static data, and the charging end time data. The analysis of the safety status of the power battery based on the identification results includes: If a single outlier battery cell exists, the power battery is analyzed for lithium plating safety risks based on the battery pulse charging data and the battery quasi-static data. If there are multiple outlier battery cells, the power battery is analyzed for high charge and low discharge safety risks based on the battery pulse charging data, the battery quasi-static data, and the charging end time data. The analysis of whether the power battery poses a lithium plating safety risk includes: Based on the battery pulse charging data and the battery quasi-static data, calculate the voltage drop and corresponding internal resistance value of the outlier battery cell at the end of each pulse charging phase and at each quasi-static moment of the quasi-static phase adjacent to each pulse charging phase. Based on the total voltage of the power battery at the end of each pulse charging phase and the number of series connections of the power battery, the average voltage of the battery cell at the end of each pulse charging phase is calculated, and then the average voltage drop and the corresponding average internal resistance value are calculated at each quasi-static moment of the quasi-static phase adjacent to each pulse charging phase at the end of each pulse charging phase. Based on the average internal resistance value and the internal resistance value, calculate the ratio of the average internal resistance value to the internal resistance value at each quasi-static moment in the intermediate stage of each quasi-static stage, and obtain the maximum value of the ratio of the average internal resistance value to the internal resistance value in each quasi-static stage. When the average internal resistance value is greater than the internal resistance value, and the maximum value of the ratio of the average internal resistance value to the internal resistance value is greater than the set safety threshold during the quasi-static stage, the power battery is subject to the lithium plating safety risk.

2. The method for detecting the safety status of a power battery according to claim 1, characterized in that, The pulse charging strategy includes: The power battery is charged alternately during the charging phase and the quasi-static phase until the charging cutoff condition is triggered to complete the charging process. The charging phase includes a pulse charging phase and a normal charging phase. The pulse charging phase and the normal charging phase alternately charge the power battery to obtain battery pulse charging data under different SOC states and charge the power battery. During the quasi-static stage, the charging rate of the power battery is 0.05C; During the charging phase, the charging rate of the power battery is 1C; The 0.05C is 0.05 times the maximum allowable charging current required by the BMS for the power battery under the current SOC and temperature conditions; The 1C refers to the maximum allowable charging current required by the BMS for the power battery under the current SOC and temperature conditions.

3. The method for detecting the safety status of a power battery according to claim 1, characterized in that, Before charging, the SOC of the power battery is less than 40%.

4. The method for detecting the safety status of a power battery according to claim 1, characterized in that, The data cleaning includes: Delete duplicate and abnormal data from the battery pulse charging data, the battery quasi-static data, and the charging end time data.

5. The method for detecting the safety status of a power battery according to claim 1, characterized in that, The analysis of whether the power battery poses a safety risk of high charging and low discharging includes: Based on the battery pulse charging data, the numbers of the battery cells with the highest voltage at the start and end of each pulse charging phase are determined and denoted as the first number and the second number, respectively. Based on the battery quasi-static data, the numbers of the battery cells with the highest voltage at the beginning and end of each quasi-static stage are determined and denoted as the third number and the fourth number, respectively. Based on the charging end time data, the SOC values ​​of the first battery cell with the highest voltage, the second battery cell with the lowest voltage, and the power battery at the charging end time are determined respectively. The first voltage value of the first battery cell and the second voltage value of the second battery cell are obtained. The voltage difference at the charging end time is calculated based on the first voltage value and the second voltage value. The voltage difference threshold of the power battery is determined based on the SOC value and the material system of the power battery. When, in a single charging-resting stage consisting of a pulse charging stage and an adjacent quasi-resting stage, the second number is the same as the third number and the second number and the third number are different from both the first number and the fourth number, and the voltage difference is greater than the voltage difference threshold corresponding to the power battery, then the power battery has a high-charge-low-discharge safety risk.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the power battery safety status detection method according to any one of claims 1-5.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to execute the power battery safety status detection method according to any one of claims 1-5.

8. A power battery safety status detection device, characterized in that, include: The charging and data acquisition module is used to charge the power battery based on the pulse charging strategy and acquire the battery pulse charging data of each pulse charging stage and the battery quasi-static data of the quasi-static stage adjacent to each pulse charging stage, as well as the charging end time data of the power battery at the charging end time. The data cleaning module is used to clean all the battery pulse charging data, all the battery quasi-static data and the charging end time data after charging is completed; The determination and calculation module determines the battery cell with the highest voltage in each of the quasi-static stages based on the battery quasi-static data after data cleaning, and calculates the frequency of each battery cell appearing in all of the quasi-static stages. The identification module is used to identify whether there are outlier battery cells among all the battery cells based on the frequency using a k-means clustering algorithm. The analysis module is used to analyze the safety status of the power battery based on the identification results, all the battery pulse charging data, all the battery quasi-static data, and the charging end time data; The analysis of the safety status of the power battery based on the identification results includes: If a single outlier battery cell exists, the power battery is analyzed for lithium plating safety risks based on the battery pulse charging data and the battery quasi-static data. If there are multiple outlier battery cells, the power battery is analyzed for high charge and low discharge safety risks based on the battery pulse charging data, the battery quasi-static data, and the charging end time data. The analysis of whether the power battery poses a lithium plating safety risk includes: Based on the battery pulse charging data and the battery quasi-static data, calculate the voltage drop and corresponding internal resistance value of the outlier battery cell at the end of each pulse charging phase and at each quasi-static moment of the quasi-static phase adjacent to each pulse charging phase. Based on the total voltage of the power battery at the end of each pulse charging phase and the number of series connections of the power battery, the average voltage of the battery cell at the end of each pulse charging phase is calculated, and then the average voltage drop and the corresponding average internal resistance value are calculated at each quasi-static moment of the quasi-static phase adjacent to each pulse charging phase at the end of each pulse charging phase. Based on the average internal resistance value and the internal resistance value, calculate the ratio of the average internal resistance value to the internal resistance value at each quasi-static moment in the intermediate stage of each quasi-static stage, and obtain the maximum value of the ratio of the average internal resistance value to the internal resistance value in each quasi-static stage. When the average internal resistance value is greater than the internal resistance value, and the maximum value of the ratio of the average internal resistance value to the internal resistance value is greater than the set safety threshold during the quasi-static stage, the power battery is subject to the lithium plating safety risk.

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