A new energy vehicle battery rapid detection method and device

Abstract

The application provides a new energy automobile battery rapid detection method and device, extracts the discrete characteristics of the battery capacity of the battery group to be detected from the electric parameter data, and then determines the charge state difference of each battery monomer in the charging process, and extracts the temperature rise characteristics of each battery monomer from the temperature data; the monomer aging index of the corresponding battery monomer is determined according to the charge state difference and the temperature rise characteristics of each battery monomer, and then the overall aging characteristics of the battery group to be detected are determined; the relative aging gradient of each battery monomer is determined based on the charging and discharging time length of the battery group to be detected and all the monomer aging indexes; the health state detection value of the battery group to be detected is determined through all the relative aging gradients and the overall aging characteristics, and the health state of the battery group to be detected is calibrated based on the health state detection value. By adopting the application, the battery group can be rapidly detected based on the monomer aging and the consistency in the group.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of battery detection, and more particularly to a new energy automobile battery rapid detection method and device. BACKGROUND

[0002] Battery detection is a technical process of comprehensively evaluating various performance indicators of batteries through professional equipment and scientific methods, which covers accurate measurement of key parameters such as battery capacity, voltage, internal resistance, cycle life, and charging and discharging efficiency, and also includes checking whether there are physical defects such as bulging, liquid leakage, and damage on the battery appearance.

[0003] With the popularization of new energy vehicles, the state of health (SOH) of the power battery pack as a core component is directly related to the vehicle's endurance, safety, and service life. The battery pack is composed of a large number of single batteries in series / parallel connection. The aging rates of the single batteries are inconsistent due to manufacturing differences and uneven working environments, forming a "barrel effect", that is, the weakest single battery determines the overall performance. The traditional battery detection method (such as full discharge and full charge cycle test) is time-consuming, complex to operate, and difficult to identify subtle aging differences between single batteries online, and cannot meet the demand for rapid, accurate, and single-level state evaluation of battery packs in maintenance scenarios. Therefore, how to rapidly detect battery packs based on the aging of single batteries in the battery pack and the consistency within the battery pack has become a problem faced by the industry. SUMMARY

[0004] The application provides a new energy automobile battery rapid detection method and device, which can rapidly detect battery packs based on the aging of single batteries in the battery pack and the consistency within the battery pack.

[0005] In a first aspect, the application provides a new energy automobile battery rapid detection method, wherein a new energy automobile battery pack to be detected includes a plurality of battery single bodies, and the method includes the following steps:

[0006] Collecting the electrical parameter data and temperature data of each battery single body in the new energy automobile battery pack to be detected in a single charging stage, and collecting the initial open circuit voltage of each battery single body;

[0007] Extracting the discrete features of the battery capacity of the battery pack to be detected in a preset voltage feature interval from the electrical parameter data, determining the actual capacity values of the battery single bodies based on the discrete features and historical full charge capacity data, determining the state of charge difference of the corresponding battery single bodies in the charging process based on the actual capacity values of the battery single bodies and the initial open circuit voltage of the battery single bodies, and extracting the temperature rise features of the battery single bodies in the charging stage from the temperature data;

[0008] According to the state of charge difference and temperature rise characteristics of each battery monomer in the to-be-tested battery pack, the performance state of the corresponding battery monomer in the charging stage is analyzed to obtain a monomer aging index of each battery monomer, and then the overall aging characteristics of the to-be-tested battery pack are determined.

[0009] Based on the charging and discharging time length of the to-be-tested battery pack and all the monomer aging indexes, the aging difference between each battery monomer is gradient analyzed to obtain a relative aging gradient of each battery monomer in the to-be-tested battery pack.

[0010] The health state detection value of the to-be-tested battery pack is determined through all the relative aging gradients and the overall aging characteristics, and the health state of the to-be-tested battery pack is calibrated based on the health state detection value.

[0011] In some embodiments, the discrete characteristics of the battery capacity of the to-be-tested battery pack in the preset voltage characteristic interval are extracted from the electrical parameter data, specifically including:

[0012] The preset voltage characteristic interval of the to-be-tested battery pack is determined.

[0013] All time periods in which the voltage of each battery monomer falls into the preset voltage characteristic interval in a single charging process are screened from the electrical parameter data.

[0014] The charging capacity of each battery monomer in each time period is calculated by the ampere-hour integral method.

[0015] The discrete characteristics of the battery capacity of the to-be-tested battery pack in the preset voltage characteristic interval are determined according to all the charging capacities.

[0016] In some embodiments, the state of charge difference of the corresponding battery monomer in the charging process is determined according to the actual capacity value of each battery monomer and the initial open circuit voltage of each battery monomer, specifically including:

[0017] The initial state of charge of the corresponding monomer before charging is determined according to the initial open circuit voltage of each battery monomer.

[0018] Real-time current data of the to-be-tested battery pack are obtained.

[0019] The current state of charge of the corresponding battery monomer is calculated according to the real-time current data and the actual capacity value of each battery monomer.

[0020] The state of charge difference of the corresponding battery monomer in the charging process is determined according to the initial state of charge and the current state of charge of each battery monomer.

[0021] In some embodiments, the temperature rise characteristics of each battery monomer in the charging stage are extracted from the temperature data, specifically including:

[0022] From the temperature data, select temperature records that completely cover a single charging process;

[0023] Calculate the temperature difference of each battery cell at the start and end of charging based on the temperature records;

[0024] Acquire temperature monitoring time data for each individual battery cell;

[0025] The temperature rise characteristics of the corresponding battery cell during the charging stage are determined based on the monitoring time data and temperature difference of each battery cell.

[0026] In some embodiments, aging analysis is performed on the performance state of the corresponding battery cells during the charging stage based on the differences in state of charge and temperature rise characteristics of each battery cell in the battery pack under test, to obtain the individual aging index of each battery cell, and then determine the overall aging characteristics of the battery pack under test. Specifically, this includes:

[0027] Obtain the reference impedance curve and reference temperature rise curve of the battery pack under test;

[0028] Select a battery cell as the selected battery cell, and determine the impedance growth rate of the selected battery cell during the charging stage based on the difference in the state of charge of the selected battery cell and the reference impedance curve.

[0029] The temperature rise deviation of the selected battery cell during the charging stage is determined based on the temperature rise characteristics of the selected battery cell and the reference temperature rise curve.

[0030] The cell aging index of the selected battery cell is determined based on the impedance growth rate and the temperature rise deviation.

[0031] Continue to determine the cell aging indicators of the remaining battery cells;

[0032] Statistical analysis was performed on all individual cell aging indicators to obtain the overall aging characteristics of the battery pack under test.

[0033] In some embodiments, gradient analysis is performed on the aging differences between individual battery cells based on the charge / discharge duration and all individual cell aging indicators of the battery pack under test to obtain the relative aging gradient of each battery cell in the battery pack under test. This specifically includes:

[0034] Obtain the charge and discharge times of the battery pack under test;

[0035] Based on the charge and discharge duration of the battery pack under test and all individual cell aging indicators, an aging feature vector for the corresponding individual battery cell is constructed.

[0036] Calculate the similarity between any two aging feature vectors to obtain the aging similarity matrix;

[0037] Based on the aging similarity matrix, all battery cells are divided into different aging clusters;

[0038] For each battery cell, the average aging difference between the battery cell and other battery cells in the same aging cluster is calculated, and then the average aging difference corresponding to each battery cell is obtained.

[0039] The relative aging gradient of each cell in the battery pack under test is determined based on all average aging differences.

[0040] In some embodiments, determining the health status detection value of the battery pack under test by using all relative aging gradients and the overall aging characteristics specifically includes:

[0041] All relative aging gradients and the overall aging characteristics are combined into a multidimensional evaluation vector;

[0042] The initial health status value of the battery pack under test is determined based on the multidimensional evaluation vector.

[0043] Obtain the preset health status detection range of the battery pack under test;

[0044] The health status detection value of the battery pack under test is determined based on the initial health status value and the health status detection interval.

[0045] In some embodiments, calibrating the health status of the battery pack under test based on the health status detection value specifically includes:

[0046] Establish a three-level health status calibration system for the battery pack under test;

[0047] The health status detection value is judged by the three-level health status calibration system to obtain the detection result of the battery pack under test, and the detection result is used as the health status of the battery pack under test.

[0048] In some embodiments, voltage and current sensors are used to collect electrical parameter data of each battery cell in the battery pack under test of a new energy vehicle during a single charging phase.

[0049] Secondly, this application provides a rapid testing device for new energy vehicle batteries, comprising:

[0050] The data acquisition module is used to collect electrical parameter data, temperature data, and initial open-circuit voltage of each battery cell in the battery pack under test of the new energy vehicle during a single charging phase.

[0051] The processing module is used to extract the discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range from the electrical parameter data, determine the actual capacity value of each battery cell by combining the discrete characteristics with historical full-charge capacity data, determine the difference in state of charge of the corresponding battery cell during the charging process based on the actual capacity value of each battery cell and the initial open-circuit voltage of each battery cell, and extract the temperature rise characteristics of each battery cell during the charging stage from the temperature data.

[0052] The processing module is also used to perform aging analysis on the performance state of the corresponding battery cells during the charging stage based on the differences in state of charge and temperature rise characteristics of each battery cell in the battery pack under test, to obtain the cell aging index of each battery cell, and then to determine the overall aging characteristics of the battery pack under test.

[0053] The processing module is also used to perform gradient analysis on the aging differences between individual battery cells based on the charge and discharge time of the battery pack under test and all individual cell aging indicators, so as to obtain the relative aging gradient of each individual battery cell in the battery pack under test.

[0054] The execution module is used to determine the health status detection value of the battery pack under test through all relative aging gradients and the overall aging characteristics, and to calibrate the health status of the battery pack under test based on the health status detection value.

[0055] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0056] The rapid testing method and apparatus for new energy vehicle batteries provided in this application first collects electrical parameter data, temperature data, and initial open-circuit voltage of each battery cell in the battery pack under test during a single charging phase. From the electrical parameter data, the discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range are extracted. The actual capacity value of each battery cell is determined by combining the discrete characteristics with historical full-charge capacity data. Based on the actual capacity value of each battery cell and its initial open-circuit voltage, the difference in state of charge of the corresponding battery cell during the charging process is determined. Finally, the temperature rise characteristics of each battery cell during the charging phase are extracted from the temperature data. Based on the differences in state of charge and temperature rise characteristics of each individual cell in the battery pack under test, an aging analysis is performed on the performance state of the corresponding individual cells during the charging stage to obtain the cell aging index of each individual cell, thereby determining the overall aging characteristics of the battery pack under test. Based on the charge / discharge duration of the battery pack under test and all individual cell aging indices, a gradient analysis is performed on the aging differences between individual cells to obtain the relative aging gradient of each individual cell in the battery pack under test. The health status detection value of the battery pack under test is determined through all relative aging gradients and the overall aging characteristics, and the health status of the battery pack under test is calibrated based on the health status detection value.

[0057] Therefore, this application demonstrates that in the rapid testing process of new energy vehicle batteries, firstly, it collects electrical parameters and temperature data of each battery cell during a single charging phase. This ensures data accuracy at the cell level while improving testing efficiency through the single charging scenario, laying a comprehensive and efficient foundation of basic data support for subsequent analysis. Secondly, by extracting capacity dispersion characteristics from voltage changes, determining differences in state of charge (SOC) based on current data, and extracting temperature rise characteristics, it accurately captures key indicators of battery capacity consistency, electrochemical performance, and thermal stability, providing multi-dimensional core parameters for aging analysis. Finally, aging is conducted based on differences in SOC and temperature rise characteristics. The analysis quantifies individual cell aging indicators and derives overall aging characteristics, achieving a tiered assessment of aging status from individual cells to the overall battery pack, thus improving the specificity and systematic nature of aging analysis. Next, gradient analysis is performed by combining charge / discharge time and individual cell aging indicators to obtain a relative aging gradient, accurately quantifying the aging differences between cells and effectively reflecting the consistency level within the pack, filling the gap in the insufficient attention paid to individual differences by purely overall analysis. Finally, the relative aging gradient and overall aging characteristics are integrated to determine the health status detection value and complete calibration, achieving a comprehensive assessment of the battery pack's health status, taking into account both individual cell consistency and overall aging degree. Using this approach, rapid testing of the battery pack can be performed based on individual cell aging and overall pack consistency. Attached Figure Description

[0058] Figure 1This is an exemplary flowchart of a rapid testing method for new energy vehicle batteries according to some embodiments of this application;

[0059] Figure 2 This is a structural diagram of a new energy electric vehicle battery pack under test, as shown in some embodiments of this application;

[0060] Figure 3 This is an exemplary flowchart illustrating the determination of temperature rise characteristics according to some embodiments of this application;

[0061] Figure 4 This is a schematic diagram of the structure of a rapid testing device for new energy vehicle batteries according to some embodiments of this application;

[0062] Figure 5 This is a schematic diagram of the structure of a computer device for implementing a rapid testing method for new energy vehicle batteries, according to some embodiments of this application. Detailed Implementation

[0063] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0064] refer to Figure 1 The figure is an exemplary flowchart of a rapid testing method for new energy vehicle batteries according to some embodiments of this application. The rapid testing method for new energy vehicle batteries mainly includes the following steps:

[0065] In step 101, electrical parameter data, temperature data, and initial open-circuit voltage of each battery cell in the battery pack under test of the new energy vehicle are collected during a single charging phase.

[0066] In specific implementation, during a single charge of the battery pack under test in a new energy vehicle, high-precision voltage and current sensors are connected to the positive and negative terminals of each battery cell to monitor and record the voltage and current values ​​of each battery cell in real time during the charging process, thereby obtaining electrical parameter data. The electrical parameter data includes the voltage and current data of each battery cell. Simultaneously, a temperature sensor is attached to the surface of each battery cell to continuously collect the temperature changes of each battery cell during the charging phase, thereby obtaining the corresponding temperature data. The collected data is denoised using a Kalman filter algorithm to eliminate measurement noise and high-frequency fluctuations. Before charging begins, the open-circuit voltage of each battery cell in its static state is obtained as the initial value through the battery management system's cell voltage acquisition module (such as the LTC6804 chip), thus obtaining the initial open-circuit voltage of each battery cell.

[0067] In some embodiments, reference Figure 2As shown, this figure is a structural diagram of the battery pack under test for a new energy electric vehicle in some embodiments of this application, such as... Figure 2 As shown, the battery pack under test for a new energy electric vehicle includes a battery module body, connecting lines, and test interfaces and auxiliary structures. The battery module body: The core area in the diagram consists of multiple series / parallel battery modules arranged in a rectangular array. Each module's stacked structure of individual cells is marked with lines, clearly demonstrating the modular design of the battery pack (this is the common "module-battery pack" architecture for new energy vehicle battery packs). Connecting lines: High-voltage connecting lines are indicated by thick lines between modules, while thin lines mark low-voltage sampling lines (used to monitor parameters such as voltage and temperature). The ends of the lines extend to the "test interface area" on the right, illustrating the signal and power transmission path under test. Test interfaces and auxiliary structures: A rectangular test interface module is located on the right, marked with test points such as "voltage detection end" and "temperature sampling end," simulating the connection scenario during actual testing. The bottom lines outline the battery pack casing, demonstrating its protective and securing functions.

[0068] In step 102, the discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range are extracted from the electrical parameter data. The actual capacity value of each battery cell is determined by combining the discrete characteristics with the historical full-charge capacity data of the battery pack under test. The state of charge difference of the corresponding battery cell during the charging process is determined based on the actual capacity value of each battery cell and the initial open-circuit voltage of each battery cell. The temperature rise characteristics of each battery cell during the charging stage are extracted from the temperature data.

[0069] In some embodiments, extracting the discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range from the electrical parameter data can be achieved by the following steps:

[0070] Determine the preset voltage characteristic range of the battery pack under test;

[0071] From the electrical parameter data, select all time periods during which the voltage of each battery cell falls within a preset voltage characteristic range during a single charge;

[0072] The charging capacity of each battery cell in each time period is calculated using the ampere-hour integration method.

[0073] The discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range are determined based on all the charging capacity.

[0074] In specific implementation, firstly, the preset voltage characteristic range of the battery pack under test is determined as follows: Based on the battery type (e.g., lithium iron phosphate battery, ternary lithium battery), the corresponding standard voltage-state-of-charge curve in the battery database is retrieved. By detecting abrupt changes in the curve slope (e.g., using the second derivative method to identify the starting point of the plateau region, the inflection point of the rapid rise region, and the end point of the saturation region), 3-5 characteristic ranges are divided (e.g., for lithium iron phosphate batteries, these can be set to 2.8-3.0V, 3.0-3.2V, 3.2-3.6V, and 3.6-3.65V). All the divided characteristic ranges are used as the preset voltage characteristic ranges. The preset voltage characteristic ranges represent several voltage ranges with significant electrochemical characteristics pre-divided according to the nonlinear voltage-state-of-charge change characteristics of the battery type. The process involves two steps: First, filtering out all time periods in which the voltage of each battery cell falls within a preset voltage characteristic range during a single charge from the electrical parameter data. Specifically, this is achieved by iterating through the real-time voltage sequence (timestamp + voltage value) of each battery cell in the electrical parameter data, identifying consecutive time periods where the voltage value falls within each preset characteristic range using an interval matching algorithm (such as sliding window comparison), and recording the start and end timestamps of each time period (for example, if the voltage of a battery cell is in the 3.0-3.2V range from 10:02:05 to 10:08:30, then that time period is marked as a valid time period within the corresponding range). This yields all time periods in which the voltage of each battery cell falls within the preset voltage characteristic range during a single charge. Other methods can also be used in other embodiments, which are not limited here.

[0075] In addition, in specific implementation, firstly, the charging capacity of each battery cell in each time period is calculated using the ampere-hour integration method: for each effective time period of each cell, the real-time current data of that time period is extracted (since the current of the series group is consistent, the total current sequence can be directly called), and the trapezoidal integration method in the ampere-hour integration method is used to integrate the current change curve with time (ΔQ=∫I(t)dt, where ΔQ represents the increment of the charging capacity of the battery cell in the effective time period, ∫ represents the definite integral operator, I(t) represents the function of the real-time current of the battery pack changing with time, and dt represents the time differential variable), to obtain the charging capacity in each time period (unit: Ah); secondly, based on all the charging capacity... The discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range are determined as follows: the charging capacity of all individual cells is statistically analyzed by grouping according to the voltage characteristic range, and the mean, standard deviation, and extreme value deviation rate (such as the difference between the maximum and minimum capacity / mean) of the capacity in each group are calculated. The dispersion of the capacity in each range is quantified by the coefficient of variation (standard deviation / mean). These statistics are integrated into the capacity discrete characteristic vector of the voltage characteristic range (for example, the discrete characteristics of the 3.0-3.2V range are [mean 2.1Ah, standard deviation 0.15Ah, coefficient of variation 7.1%]). This capacity discrete characteristic vector is used as the discrete characteristic of the battery capacity of the battery pack under test within the preset voltage characteristic range.

[0076] It should be noted that the discrete features in this application represent the differences and fluctuations in the charge distribution of each cell in the battery pack under test within a preset voltage characteristic range. These features can be used to characterize the degree of dispersion of the charge distribution of individual cells, avoid interference from the nonlinear characteristics of voltage-state of charge, accurately depict the capacity differences of each cell in the battery pack at different electrochemical stages, and provide key basis for subsequent aging analysis and health status assessment.

[0077] In some embodiments, determining the actual capacity value of each battery cell by combining the discrete features with the historical full-charge capacity data of the battery pack under test can be achieved by the following steps:

[0078] Obtain the historical full-charge capacity data of the battery pack under test;

[0079] The initial baseline capacity of each battery cell is determined based on the historical full-charge capacity data.

[0080] Based on the discrete characteristics, the initial reference capacity is corrected to obtain the actual capacity value of each battery cell.

[0081] In specific implementation, firstly, obtaining the historical full-charge capacity data of the battery pack under test is achieved by: retrieving all full-charge records of the battery pack since its manufacture or last calibration through the historical database interface of the battery management system (which must meet the standard of charging to the cutoff voltage and having a state of charge ≥95% after resting); extracting the full-charge capacity value of each battery cell in each record (obtained by combining the ampere-hour integral result of the full-charge process with a temperature compensation algorithm); and thus obtaining the historical full-charge capacity data of the battery pack under test. The historical full-charge capacity data refers to the set of actual capacity values ​​recorded by each battery cell in the battery pack during multiple past "full-charge processes"; secondly, according to the... The specific implementation of determining the initial baseline capacity of each battery cell using historical full-charge capacity data is as follows: Preprocess the historical full-charge capacity data of each cell (removing outliers, such as jumps caused by charging interruptions), calculate the initial baseline capacity using a weighted average method, assigning higher weights to the three most recent full-charge capacities (e.g., 40%, 30%, 30%), and incorporating earlier data into the calculation after being discounted by a time decay factor (e.g., 0.95 per month). The final result is an initial baseline capacity that reflects the recent capacity level of the cell, where the initial baseline capacity represents the baseline value of the cell's recent true capacity level. Other methods can also be used in other embodiments, which are not limited here.

[0082] In addition, in specific implementation, the actual capacity value of each battery cell is obtained by correcting the initial reference capacity according to the discrete characteristics as follows: For each cell, its discrete characteristic parameters in each preset voltage characteristic range (such as the energy deviation rate from the group average) are called, and the capacity-range energy correlation model corresponding to the battery type is combined (such as the actual capacity decay of 0.8% for every 1% decrease in the energy ratio of the plateau area). The correction coefficient of each range is calculated; the correction coefficients of all ranges are weighted and summed (the weight of the plateau area is higher than that of the non-plateau area) to obtain the total correction amount. The actual capacity value of the cell is obtained by subtracting the total correction amount from the initial reference capacity. Other methods can also be used for correction in other embodiments, which are not limited here.

[0083] It should be noted that the actual capacity value in this application represents the maximum amount of charge that a single battery cell can store in the current state, while ensuring that the corrected capacity value does not exceed the normal capacity decay range of this type of battery (such as 60%-100% of the rated capacity).

[0084] In some embodiments, determining the difference in state of charge of corresponding battery cells during the charging process based on the actual capacity value of each battery cell and the initial open-circuit voltage of each battery cell can be achieved by the following steps:

[0085] The initial state of charge of each battery cell before charging is determined based on its initial open-circuit voltage.

[0086] Obtain the real-time current data of the battery pack under test;

[0087] The current state of charge of the corresponding battery cell is calculated based on the real-time current data and the actual capacity value of each battery cell.

[0088] The difference in state of charge of each battery cell during the charging process is determined based on the initial state of charge and the current state of charge of each battery cell.

[0089] In specific implementation, firstly, the initial state of charge (SOC) of each battery cell before charging is determined based on its initial open-circuit voltage. This is achieved by calling the open-circuit voltage-SOC mapping table matching the battery type of the battery pack under test, substituting the initial open-circuit voltage value of each cell into the mapping table, and correcting the mapping relationship based on the current battery temperature (e.g., the open-circuit voltage-SOC curve shifts downwards at 0°C). The initial SOC of each cell before charging is obtained through interpolation calculation, where the initial SOC represents the state of charge of the battery cell before the start of the charging process. Secondly, the real-time current data of the battery pack under test is obtained by acquiring the real-time current signal (sampling frequency ≥ 1kHz) during the charging process through a Hall current sensor connected in series in the main circuit of the battery pack under test. After removing high-frequency noise through a filtering circuit, the battery management controller converts the analog signal into a digital quantity (unit: A) and stores it synchronously as a current sequence data (containing the current value and corresponding time point at each moment) according to the timestamp to obtain the real-time current data of the battery pack under test. Other methods can also be used in other embodiments, which are not limited here.

[0090] In specific implementation, the calculation of the current state of charge (SOC) of the corresponding battery cell based on the real-time current data and the actual capacity value of each battery cell is as follows: The real-time current data is integrated over a time interval to obtain the total charging capacity (Q, in Ah) from the start of charging to the current moment; for each cell, the current SOC is calculated using the formula "Current SOC = Initial SOC + (Total charging capacity from the start of charging to the current moment ÷ Actual capacity value) × 100%"; the determination of the SOC difference of the corresponding battery cell during the charging process based on the initial SOC and current SOC of each battery cell is as follows: The average current SOC of all cells in the group is calculated; for each cell, the change in SOC of the cell is obtained by subtracting the initial SOC from its current SOC; the difference is then subtracted from the average current SOC of the group, and the difference is the SOC difference of the cell during the charging process; other methods may be used in other embodiments, which are not limited here.

[0091] It should be noted that the state of charge difference in this application represents the degree of difference between the charge changes of individual battery cells during the charging process, which can characterize the intensity of the internal reaction of the battery under different operating conditions.

[0092] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart for determining temperature rise characteristics in some embodiments of this application. In this embodiment, extracting the temperature rise characteristics of each battery cell during the charging stage from the temperature data can be achieved by the following steps:

[0093] First, in step 1021, temperature records that completely cover a single charging process are selected from the temperature data;

[0094] Secondly, in step 1022, the temperature difference between the start and end times of charging for each battery cell is calculated based on the temperature record.

[0095] Then, in step 1023, the monitoring time data of temperature for each battery cell is obtained;

[0096] Finally, in step 1024, the temperature rise characteristics of the corresponding battery cell during the charging stage are determined based on the monitoring time data and temperature difference value corresponding to each battery cell.

[0097] In specific implementation, the continuous temperature records that completely cover a single charging process can be selected from the temperature data in the following way: the time intervals corresponding to the charging start signal and the end signal are matched by timestamps, and records that do not completely cover the interval (such as data interruption during charging, start time later than charging start time, or end time earlier than charging completion time) are removed; the data continuity of the remaining records is detected by the sliding window method. If there is a blank for more than a preset time (such as 10 seconds) or the data jump amplitude exceeds the normal fluctuation range (determined based on historical data of the same model of battery), it is determined to be a non-continuous record and removed. The final retained records are the temperature records that completely cover a single charging process and are continuous and stable. Other methods can also be used for selection in other embodiments, which are not limited here.

[0098] In specific implementation, the temperature difference between the start and end times of charging for each battery cell can be calculated based on the temperature records as follows: The temperature value corresponding to the moment the charging start signal is triggered is extracted from the temperature records as the starting temperature, and the temperature value corresponding to the moment the charging end signal is triggered is extracted as the ending temperature. If there is a slight deviation between the recorded time point and the charging start / end signals (e.g., a signal delay of less than 0.5 seconds), linear interpolation is used to estimate the temperature at the precise moment based on the temperature values ​​before and after the deviation. Then, the starting temperature is subtracted from the ending temperature to obtain the temperature difference for each battery cell. This temperature difference represents the temperature difference between the battery cell at the start and end times of charging, thus reflecting the overall temperature rise during the entire charging process. Other calculation methods can also be used in other embodiments, which are not limited here.

[0099] The monitoring time data of temperature for each battery cell can be obtained in the following way: extract the complete monitoring time of the charging phase (i.e., the time difference between the end time and the start time) from the timestamp of the temperature record, and mark the key time nodes of temperature change, such as the moment when the temperature first exceeds the ambient temperature by 5°C, the moment when the temperature rise rate begins to accelerate significantly (determined by comparing the temperature change rate of adjacent time points and combining the threshold determination method in the existing technology), and the moment when the temperature reaches the peak. Integrate the total monitoring time with the time information of these key nodes to form the monitoring time data of each battery cell.

[0100] In specific implementation, the temperature rise characteristics of the corresponding battery cell during the charging stage are determined based on the monitoring time data and temperature difference for each battery cell. Specifically, for each battery cell, the average temperature rise rate is obtained using existing rate calculation methods, reflecting the overall temperature rise speed, by combining the temperature difference and the total monitoring time. Based on key nodes in the monitoring time data, the instantaneous temperature rise rate at different stages (such as the rate of rapid temperature rise from the start to the peak stage) is calculated, and the maximum value is selected as the maximum temperature rise rate, reflecting the most intense temperature rise. At the same time, the distribution ratio of the temperature difference in each time period is analyzed, such as the temperature rise ratio between the first half and the second half of charging, to determine whether the temperature rise is concentrated in a specific stage. Finally, the total temperature rise amplitude, average temperature rise rate, maximum temperature rise rate, and temperature rise stage distribution characteristics are integrated to form the temperature rise characteristics of the battery cell, thereby obtaining the temperature rise characteristics of each battery cell. In other embodiments, other methods can also be used to determine the temperature rise characteristics, which are not limited here.

[0101] It should be noted that the temperature rise characteristics in this application represent the characteristics of the temperature change pattern of a single battery cell during a single charge. They can reflect the heat generation, heat dissipation efficiency, and intensity of internal chemical reactions of the battery cell during charging. They are an important basis for judging whether the battery has an overheating risk, aging degree, and performance stability.

[0102] In step 103, based on the differences in state of charge and temperature rise characteristics of each battery cell in the battery pack under test, an aging analysis is performed on the performance state of the corresponding battery cell during the charging stage to obtain the individual aging index of each battery cell, thereby determining the overall aging characteristics of the battery pack under test.

[0103] In some embodiments, the performance state of the corresponding battery cells during the charging stage is analyzed based on the differences in state of charge and temperature rise characteristics of each battery cell in the battery pack under test, and the aging index of each battery cell is obtained. The overall aging characteristics of the battery pack under test can then be determined by the following steps:

[0104] Obtain the reference impedance curve and reference temperature rise curve of the battery pack under test;

[0105] Select a battery cell as the selected battery cell, and determine the impedance growth rate of the selected battery cell during the charging stage based on the difference in the state of charge of the selected battery cell and the reference impedance curve.

[0106] The temperature rise deviation of the selected battery cell during the charging stage is determined based on the temperature rise characteristics of the selected battery cell and the reference temperature rise curve.

[0107] The cell aging index of the selected battery cell is determined based on the impedance growth rate and the temperature rise deviation.

[0108] Continue to determine the cell aging indicators of the remaining battery cells;

[0109] Statistical analysis was performed on all individual cell aging indicators to obtain the overall aging characteristics of the battery pack under test.

[0110] It should be noted that the difference in state of charge (SOC) of a single battery cell directly reflects the aging state of its internal electrochemical system (e.g., electrode material degradation leads to a greater difference in SOC, i.e., increased impedance; hindered ion migration exacerbates the change in SOC, i.e., increased impedance fluctuations). Temperature rise characteristics, on the other hand, reflect the imbalance between heat generation and dissipation caused by aging (e.g., increased Joule heat due to increased internal resistance in aged cells, or accelerated temperature rise due to blocked heat dissipation channels). By comparing both with the baseline curves of a new battery of the same model (impedance baseline reflects the impedance change pattern under healthy conditions, and temperature rise baseline reflects the normal heat generation rhythm), the impedance growth rate (the magnitude of deviation from the baseline) and temperature rise deviation (the temperature difference from the baseline) are quantified. These two indicators are then weighted and integrated to obtain the single-cell aging index (comprehensively reflecting the intrinsic aging degree of the single cell). Finally, statistical analysis of all single-cell indicators (e.g., calculating the mean, standard deviation, and cluster distribution) yields the overall aging characteristics (e.g., overall aging degree, single-cell consistency, high-risk areas, etc.).

[0111] The process involves retrieving historical test data of the same model of new battery pack under standard environmental conditions (such as constant temperature at 25°C and standard charging rate) and selecting more than 30 sets of complete single-charge process data. The reference impedance curve is composed of the average trend line of the impedance change of each cell of the new battery during the charging stage (the average value is taken after removing outliers), and the reference temperature rise curve is the average curve of the temperature change of each cell of the new battery during the charging stage (including features such as starting temperature, ending temperature, and average temperature rise rate). The reference impedance curve is the average trend curve of the impedance change of the same model of new battery during a single complete charge under standard environmental conditions (such as constant temperature, standard charging rate, and no external interference), and the reference temperature rise curve is the average trend curve of the temperature change of the same model of new battery during a single complete charge under the same standard environment.

[0112] In specific implementation, the impedance growth rate of the selected battery cell during the charging stage can be determined based on the difference in state of charge of the selected battery cell and the reference impedance curve in the following way: align the curve of the difference in state of charge of the selected battery cell over time with the reference impedance curve along the charging time axis, compare the differences between the two segment by segment (e.g., every 5 minutes as a segment), calculate the growth ratio of each segment using the trend comparison algorithm in the prior art, and then take the average growth ratio of all segments as the impedance growth rate of the cell (if the impedance value of a certain segment is lower than the reference, the growth ratio is recorded as 0 to avoid negative growth interference). The impedance growth rate represents the increase in the impedance of the cell relative to the ideal state. Other methods can also be used in other embodiments, which are not limited here.

[0113] In specific implementation, the temperature rise deviation of the selected battery cell during the charging stage can be determined by the following method based on the temperature rise characteristics of the selected battery cell and the reference temperature rise curve: the temperature rise characteristics (total temperature difference, average temperature rise rate, and maximum temperature rise rate) of the selected cell are compared item by item with the corresponding characteristics of the reference temperature rise curve. For example, the total temperature difference deviation is the difference between the measured total temperature difference and the reference total temperature difference; the average temperature rise rate deviation is the ratio of the measured rate to the reference rate; and the maximum temperature rise rate deviation is determined by locating the time point and numerical difference of the peak rates of the two. Finally, these deviation values ​​are weighted and integrated (the total temperature difference deviation has the highest weight because it directly reflects the overall heat generation) to obtain the temperature rise deviation of the cell. The temperature rise deviation represents the degree of difference between the temperature rise characteristics of the battery cell and the reference temperature rise curve. Other methods can also be used in other embodiments, which are not limited here.

[0114] In specific implementation, the cell aging index of the selected battery cell can be determined based on the impedance growth rate and the temperature rise deviation in the following way: The weights are set using the analytic hierarchy process (AHP) (the impedance growth rate reflects internal structural aging and has a slightly higher weight than the temperature rise deviation), and the values ​​of both are mapped to a scoring range of 0-10 (e.g., impedance growth rate 0-5% corresponds to 1 point, 5%-10% corresponds to 3 points, and so on; temperature rise deviation 0-2℃ corresponds to 1 point, 2-5℃ corresponds to 3 points, etc.). The two scores are then weighted and summed using an existing index fusion model (e.g., fuzzy comprehensive evaluation method) to obtain a cell aging index of 0-10 points (0 points for no aging, 10 points for severe aging). Stages with scores exceeding 7 points are marked as aging risk points. Other methods can also be used in other embodiments, which are not limited here.

[0115] It should be noted that the cell aging index in this application represents the degree of aging of a single cell during the charging stage. The higher the score of the cell aging index, the more prominent the problems of impedance growth and abnormal temperature rise of the cell are, and the more serious the overall performance degradation is. It can intuitively distinguish the aging level of the cell (such as mild, moderate and severe aging) and provide a basis for locating severely aged cells.

[0116] In practice, statistical analysis is performed on all individual cell aging indicators to obtain the overall aging characteristics of the battery pack under test: First, the average value (reflecting the overall aging degree, e.g., an average value of 3.5 points corresponds to "mild overall aging") and standard deviation (reflecting the consistency of cell aging, with a standard deviation > 2 points indicating poor consistency) of all individual cell aging indicators are calculated; then, the cells are divided into three categories, "low aging," "medium aging," and "high aging," according to aging indicators through cluster analysis, and the proportion and distribution of cells in each category are statistically analyzed (e.g., high-aging cells concentrated in the edge area may indicate heat dissipation problems); finally, the overall aging characteristics are comprehensively output by combining the maximum aging indicator (reflecting the state of the most severely affected cells) and the time distribution of aging indicators (e.g., the aging indicators of most cells rise sharply in the later stages of charging). The overall aging characteristics include the overall aging level (e.g., "medium aging, poor consistency"), the key risk cell number, and information on the concentrated aging stage; other methods can also be used to determine this in other embodiments, which are not limited here.

[0117] It should be noted that the overall aging characteristics in this application reflect the aging state of the entire battery pack under test during the charging stage. For example, a high average value indicates severe overall aging, a large standard deviation indicates uneven aging of individual cells, and a concentration of highly aged cells in a certain area may suggest that there are problems with heat dissipation or circuitry in that area. This provides a comprehensive basis for evaluating the overall performance, lifespan, and safety risks of the battery pack.

[0118] In step 104, gradient analysis is performed on the aging differences between individual battery cells based on the charge and discharge time of the battery pack under test and all individual cell aging indicators to obtain the relative aging gradient of each individual battery cell in the battery pack under test.

[0119] In some embodiments, the relative aging gradient of each battery cell in the battery pack under test can be obtained by performing gradient analysis on the aging differences between the individual cells based on the charge / discharge time and all cell aging indicators of the battery pack under test, using the following steps:

[0120] Obtain the charge and discharge times of the battery pack under test;

[0121] Based on the charge and discharge duration of the battery pack under test and all individual cell aging indicators, an aging feature vector for the corresponding individual battery cell is constructed.

[0122] Calculate the similarity between any two aging feature vectors to obtain the aging similarity matrix;

[0123] Based on the aging similarity matrix, all battery cells are divided into different aging clusters;

[0124] For each battery cell, the average aging difference between the battery cell and other battery cells in the same aging cluster is calculated, and then the average aging difference corresponding to each battery cell is obtained.

[0125] The relative aging gradient of each cell in the battery pack under test is determined based on all average aging differences.

[0126] It should be noted that charge and discharge time directly reflects the actual performance degradation of a single battery cell (e.g., aging cells charge and discharge faster, with shorter durations). Cell aging indicators comprehensively quantify the severity of cell aging. Both indicators constitute the characteristics of a cell's aging state from two dimensions: "performance" and "aging nature." By integrating these two indicators into an aging feature vector, the aging state of each cell can be comprehensively characterized. Furthermore, by calculating the similarity between vectors to divide aging clusters, cells with similar characteristics can be grouped into one category. Then, by combining the average difference between a cell and other cells in the same cluster with the overall aging level of the cluster, the degree of aging prominence of a cell relative to other cells in the group can be quantified, i.e., the relative aging gradient, thus achieving a hierarchical description of the aging differences between cells.

[0127] The process of obtaining the charge and discharge duration of the battery pack under test involves: selecting complete charge and discharge cycle records of the battery pack under test from the historical charge and discharge logs stored in the battery management system; calculating the single charge and discharge duration of the battery pack under test for each charge and discharge process by extracting the difference between the start and end timestamps; removing interrupted or abnormally terminated records; retaining continuous and complete duration data; and finally taking the average duration of the battery pack under test for the most recent 50 charge and discharge cycles as the charge and discharge duration of the battery pack under test. The charge and discharge duration represents the time required for the battery pack to complete one complete charge and discharge process, and the charge and discharge duration corresponding to each individual battery cell is consistent with the overall charge and discharge duration of the battery pack under test.

[0128] In specific implementation, an aging feature vector for each battery cell is constructed based on the charge / discharge duration of the battery pack under test and all individual cell aging indicators. Specifically, for each battery cell, its charge / discharge duration and individual cell aging indicator are used as two core parameters. A min-max normalization method (mapping the data to the 0-1 range) is employed to eliminate dimensional differences. For example, the longer the charge / discharge duration (significantly longer than a new battery), the closer the normalized value is to 1; the higher the individual cell aging indicator (more severe aging), the closer the normalized value is to 1. The two normalized parameters are arranged in the order of "charge / discharge duration - individual cell aging indicator" to form a two-dimensional aging feature vector for that cell. Each element in the vector corresponds to an aging feature in one dimension. The aging feature vector represents a multi-dimensional data combination (such as a two-dimensional vector) formed by combining the charge / discharge duration of a single battery cell with its individual cell aging indicator (a quantized value obtained by combining impedance and temperature rise). Other methods can be used in other embodiments, which are not limited here.

[0129] In addition, in specific implementation, the similarity between any two aging feature vectors is calculated to obtain an aging similarity matrix: that is, the cosine similarity algorithm is used to calculate the similarity between all pairs of aging feature vectors of all battery cells, traversing all cell combinations (such as cell A and cell B, cell A and cell C, etc.). In each calculation, the cosine value of the angle between the vectors is used to measure the similarity between the two in terms of aging features (the closer the value is to 1, the more similar they are). All similarity results are arranged in order of cell number to form a square matrix (the rows and columns are cell numbers). The elements in the matrix are the aging similarity between the corresponding two cells, forming the aging similarity matrix. The aging similarity matrix represents a square matrix with all cells in the battery pack as rows and columns. Each element in the matrix represents the similarity between the aging feature vectors of two cells (such as calculated by cosine similarity). In other embodiments, other methods can also be used for calculation, which are not limited here.

[0130] In addition, in specific implementation, all battery cells are divided into different aging clusters based on the aging similarity matrix: that is, a hierarchical clustering algorithm is used to analyze the aging similarity matrix, and based on similarity, cells with similarity higher than a set threshold (such as 0.8) are grouped into the same cluster. By gradually merging the clusters with the highest similarity, several aging clusters with similar aging characteristics are finally formed (such as "mild aging cluster", "medium aging cluster", "severe aging cluster"). The cells in each cluster show similar characteristics in charge-discharge time and aging index. The aging cluster represents a group of battery cells with similar characteristics that are grouped together. Other methods can be used for division in other embodiments, which are not limited here.

[0131] In addition, in specific implementation, for each battery cell, the average aging difference between the battery cell and other battery cells in the same aging cluster is calculated, thereby obtaining the average aging difference corresponding to each battery cell: that is, for each battery cell, the aging cluster to which it belongs is first determined, and then the aging difference between the cell and all other cells in the cluster is calculated (by subtracting the similarity between the two from 1, the higher the similarity, the smaller the difference). The arithmetic mean of these difference values ​​is taken to obtain the average aging difference of the cell in its cluster. The larger this value is, the more obviously the cell deviates from the group characteristics in a cluster of the same aging degree.

[0132] In addition, in specific implementation, the relative aging gradient of each battery cell in the battery pack under test is determined based on all the average aging differences: that is, the average aging differences of all battery cells are normalized (mapped to the gradient range of 0-10), and combined with the overall aging level of its cluster (e.g., the basic gradient value of a heavily aged cluster is higher than that of a lightly aged cluster), the relative aging gradient of each cell is obtained by weighted calculation (cluster level weight accounts for 60%, average aging difference weight accounts for 40%). Other methods can also be used to determine this in other embodiments, which are not limited here.

[0133] It should be noted that the relative aging gradient in this application reflects the severity of aging of a single cell relative to the entire battery pack (especially cells of the same aging degree). The higher the gradient value, the more prominent the aging state of the cell within the pack and the more significant the aging difference with other cells. It can be used to locate the cell with the most severe or abnormal aging within the pack.

[0134] In step 105, the health status detection value of the battery pack under test is determined by all relative aging gradients and the overall aging characteristics, and the health status of the battery pack under test is calibrated based on the health status detection value.

[0135] In some embodiments, determining the health status detection value of the battery pack under test by using all relative aging gradients and the overall aging characteristics can be achieved by the following steps:

[0136] All relative aging gradients and the overall aging characteristics are combined into a multidimensional evaluation vector;

[0137] The initial health status value of the battery pack under test is determined based on the multidimensional evaluation vector.

[0138] Obtain the preset health status detection range of the battery pack under test;

[0139] The health status detection value of the battery pack under test is determined based on the initial health status value and the health status detection interval.

[0140] It should be noted that the health status of a battery pack is determined by both the "overall aging level" and the "consistency of cell aging". The relative aging gradient quantifies the aging differences between individual cells, reflecting the balance within the battery pack. If the aging differences between individual cells are large (high gradient value), even if the overall aging is not severe, local lag may lead to a decrease in charging and discharging efficiency and an increase in safety risks. On the other hand, the overall aging characteristics (such as the overall capacity decay rate, the remaining cycle life, etc.) directly reflect the overall performance level of the battery pack, because the actual health performance of the battery pack cannot be reflected by a single dimension: if only the overall aging characteristics are considered, local failures caused by differences between individual cells will be ignored (such as excessive aging of a certain cell leading to premature failure of the entire pack); if only the relative aging gradient is considered, it will deviate from the basic judgment of overall performance (such as when the overall aging is severe, even if the differences between individual cells are small, the health status is already poor).

[0141] In specific implementation, all relative aging gradients and the overall aging characteristics are combined into a multi-dimensional evaluation vector: First, the relative aging gradient of each battery cell is standardized, and its value is uniformly transformed to the 0-1 range through linear mapping (the higher the gradient, the closer the corresponding value is to 1). Then, key parameters in the overall aging characteristics (such as overall aging degree score, standard deviation of cell aging consistency, and proportion of high-aging cells) are extracted and standardized to the 0-1 range. In the order of "relative aging gradient (arranged in order of cell number) + overall aging characteristic parameters", these standardized values ​​are combined into a multi-dimensional array, which is used as a multi-dimensional evaluation vector. For example, a 15-dimensional evaluation vector containing 12 cell gradient values ​​and 3 overall characteristic values, where each element corresponds to a specific aging-related indicator. Other methods can be used in other embodiments, which are not limited here.

[0142] In addition, in specific implementation, the initial health status value of the battery pack under test is determined according to the multidimensional evaluation vector: Principal component analysis is used to reduce the dimensionality of the multidimensional evaluation vector, and the top three principal components with the highest contribution (the cumulative contribution must exceed 85%) are extracted. Each principal component represents a set of weighted integrated core aging features (e.g., the first principal component focuses on the overall aging degree, the second principal component reflects the consistency of individual cells, and the third principal component is associated with the distribution of high-risk individual cells). By comparing the benchmark principal component scores of healthy battery packs of the same model, the deviation of the principal component scores of the test group from the benchmark value is calculated. Then, the deviation is converted into an initial health status value of 0-100 points using the existing scoring conversion model (e.g., mapping rules based on expert experience). The initial health status value reflects the preliminary value of the current health level of the battery pack. The higher the value, the better the health status. However, this value has not been standardized and is only used as the basis for subsequent calculations. Other methods can also be used in other embodiments, which are not limited here.

[0143] The process involves obtaining the preset health status detection range for the battery pack under test. This range is retrieved from the built-in parameter library of the battery management system, and is typically divided according to industry standards or manufacturer specifications. It usually includes multiple levels, such as 90-100 points for "Excellent" (no obvious aging, good performance), 70-89 points for "Good" (slight aging, does not affect use), 50-69 points for "Medium" (moderate aging, requires attention), 30-49 points for "Poor" (severe aging, significant performance degradation), and 0-29 points for "Critical" (on the verge of failure, safety risk exists). Each range corresponds to a clear performance description and maintenance recommendations. The health status detection range represents the standardized reference range for judging the health status of the battery under test. Other methods can also be used in other embodiments, which are not limited here.

[0144] In addition, in specific implementation, the health status detection value of the battery pack under test is determined according to the initial health status value and the health status detection interval: the initial health status value is compared with the critical value of the preset interval. If the initial value falls within a certain interval (e.g., 75 points falls within the "Good" interval), the standard score corresponding to that interval is directly used as the health status detection value (e.g., the median value of 80 points is taken from 70-89 points, or the original score is retained and the interval level is marked). If the initial value is close to the interval critical value (e.g., 69 points is close to the critical line between "Medium" and "Good"), it is fine-tuned in combination with the trend parameter in the overall aging characteristics (e.g., the slope of the health value change in the last 3 tests). For example, if there is a downward trend, it is classified into a lower interval. Finally, the detection result containing the specific score, interval level and trend label (e.g., "Good, health value slightly decreased") is output. Other methods can also be used in other embodiments, which are not limited here.

[0145] It should be noted that the health status detection value in this application represents the detection parameter value of the corresponding health status of the battery pack under test, which can be directly used to determine whether the battery needs maintenance, replacement or continued use.

[0146] In some embodiments, the health status of the battery pack under test can be calibrated based on the health status detection value by the following steps;

[0147] Establish a three-level health status calibration system for the battery pack under test;

[0148] The health status detection value is judged by the three-level health status calibration system to obtain the detection result of the battery pack under test, and the detection result is used as the health status of the battery pack under test.

[0149] In specific implementation, a three-level health status calibration system for the battery pack under test is determined: a three-level system is constructed, including a basic health layer, a dynamic risk layer, and a trend warning layer. The basic health layer is based on the absolute value of the health status detection value and is divided into five fixed levels: healthy (80-100 points), mild aging (60-79 points), moderate aging (40-59 points), severe aging (20-39 points), and failure (0-19 points). Each level corresponds to a specific performance threshold (e.g., the health level requires a capacity retention rate ≥85% and no abnormal aging gradient of individual cells). The dynamic risk layer combines the voltage fluctuation amplitude and temperature peak of the battery pack at the end of charging to dynamically correct the level of the basic health layer (e.g., if the detection value is in the healthy level but the voltage of a certain cell suddenly rises above the threshold, it is downgraded to "healthy - risk concern"). The trend warning layer introduces the change rate of the last three detection values. If the decrease exceeds 5% for two consecutive times, an "accelerated aging warning" label is added after the level, forming a multi-dimensional calibration framework.

[0150] In addition, in specific implementation, the health status detection value is judged by the three-level health status calibration system to obtain the detection result of the battery pack under test, and the detection result is used as the health status of the battery pack under test: The health status detection value is input into the three-level calibration system. First, the basic health layer is matched to determine the initial level. Then, the rule base of the dynamic risk layer is called to check whether there are risk factors such as abnormal voltage fluctuations and excessive local temperature rise. If they exist, the initial level is adjusted (e.g., a "local overheating risk" label is superimposed on the mild aging level). Then, the trend warning layer analyzes the changing trend of historical detection data and calculates the decay rate of health status. If the rate exceeds the preset safety range, a warning label is added after the level (e.g., "moderate aging - accelerated warning"). Finally, the three-level judgment results are integrated to form a detection result of the battery pack under test that includes the absolute level, risk factors, and trend warning, such as "mild aging (local single cell gradient abnormality) - stable trend". This result is defined as the final health status of the battery pack under test, and corresponding maintenance suggestions are output (e.g., equalization charging, replacement of high-risk cells, etc.).

[0151] In another aspect, in some embodiments, this application provides a rapid testing device for new energy vehicle batteries, referring to... Figure 4 The figure is a schematic diagram of the structure of a rapid testing device for new energy vehicle batteries according to some embodiments of this application. The rapid testing device 400 for new energy vehicle batteries includes: a data acquisition module 401, a processing module 402, and an execution module 403, which are described below:

[0152] The acquisition module 401 in this application is mainly used to acquire electrical parameter data, temperature data and initial open circuit voltage of each battery cell in the battery pack under test of the new energy vehicle during a single charging stage.

[0153] Processing module 402 in this application is used to extract the discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range from the electrical parameter data, determine the actual capacity value of each battery cell by combining the discrete characteristics with historical full-charge capacity data, determine the difference in state of charge of the corresponding battery cell during the charging process based on the actual capacity value of each battery cell and the initial open-circuit voltage of each battery cell, and extract the temperature rise characteristics of each battery cell during the charging stage from the temperature data.

[0154] It should be noted that the processing module 402 in this application is also used to perform aging analysis on the performance state of the corresponding battery cell during the charging stage based on the differences in the state of charge and temperature rise characteristics of each battery cell in the battery pack under test, to obtain the cell aging index of each battery cell, and then to determine the overall aging characteristics of the battery pack under test.

[0155] In addition, it should be noted that the processing module 402 in this application is also used to perform gradient analysis on the aging differences between individual battery cells based on the charge and discharge time of the battery pack under test and all individual cell aging indicators, so as to obtain the relative aging gradient of each individual battery cell in the battery pack under test.

[0156] The execution module 403 in this application is mainly used to determine the health status detection value of the battery pack under test through all the relative aging gradients and the overall aging characteristics, and to calibrate the health status of the battery pack under test based on the health status detection value.

[0157] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described rapid detection method for new energy vehicle batteries.

[0158] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing a rapid testing method for new energy vehicle batteries according to some embodiments of this application. The rapid testing method for new energy vehicle batteries in the above embodiments can... Figure 5 The computer device shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0159] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0160] The communication bus 502 can be used to transmit information between the aforementioned components.

[0161] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0162] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0163] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0164] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0165] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0166] In addition, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described rapid detection method for new energy vehicle batteries.

[0167] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0168] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A rapid testing method for new energy vehicle batteries, wherein, The battery pack to be tested in a new energy vehicle includes multiple battery cells, and the method is characterized by the following steps: Collect electrical parameter data, temperature data, and initial open-circuit voltage of each battery cell in the battery pack under test of the new energy vehicle during a single charging phase. The discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range are extracted from the electrical parameter data. The actual capacity value of each battery cell is determined by combining the discrete characteristics with the historical full-charge capacity data of the battery pack under test. The difference in state of charge of the corresponding battery cell during the charging process is determined based on the actual capacity value of each battery cell and the initial open-circuit voltage of each battery cell. The temperature rise characteristics of each battery cell during the charging stage are extracted from the temperature data. Based on the differences in state of charge and temperature rise characteristics of each individual cell in the battery pack under test, an aging analysis is performed on the performance state of the corresponding individual cells during the charging stage to obtain the cell aging index of each individual cell, thereby determining the overall aging characteristics of the battery pack under test. Based on the charge and discharge time of the battery pack under test and all the aging indicators of each individual cell, a gradient analysis of the aging differences between individual cells is performed to obtain the relative aging gradient of each individual cell in the battery pack under test. The health status detection value of the battery pack under test is determined by all relative aging gradients and the overall aging characteristics, and the health status of the battery pack under test is calibrated based on the health status detection value. Specifically, based on the differences in state of charge and temperature rise characteristics of each individual battery cell within the battery pack under test, an aging analysis is performed on the performance state of the corresponding battery cell during the charging phase to obtain the individual aging index of each battery cell, thereby determining the overall aging characteristics of the battery pack under test. Obtain the reference impedance curve and reference temperature rise curve of the battery pack under test; Select a battery cell as the selected battery cell, and determine the impedance growth rate of the selected battery cell during the charging stage based on the difference in the state of charge of the selected battery cell and the reference impedance curve. The temperature rise deviation of the selected battery cell during the charging stage is determined based on the temperature rise characteristics of the selected battery cell and the reference temperature rise curve. The cell aging index of the selected battery cell is determined based on the impedance growth rate and the temperature rise deviation. Continue to determine the cell aging indicators of the remaining battery cells; Statistical analysis was performed on all individual cell aging indicators to obtain the overall aging characteristics of the battery pack under test. Specifically, based on the charge / discharge time of the battery pack under test and all individual cell aging indicators, a gradient analysis is performed on the aging differences between individual battery cells to obtain the relative aging gradient of each individual battery cell in the battery pack under test. Obtain the charge and discharge times of the battery pack under test; Based on the charge and discharge duration of the battery pack under test and all individual cell aging indicators, an aging feature vector for the corresponding individual battery cell is constructed. Calculate the similarity between any two aging feature vectors to obtain the aging similarity matrix; Based on the aging similarity matrix, all battery cells are divided into different aging clusters; For each battery cell, the average aging difference between the battery cell and other battery cells in the same aging cluster is calculated, and then the average aging difference corresponding to each battery cell is obtained. The relative aging gradient of each cell in the battery pack under test is determined based on all average aging differences.

2. The rapid testing method for new energy vehicle batteries as described in claim 1, characterized in that, Extracting the discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range from the electrical parameter data specifically includes: Determine the preset voltage characteristic range of the battery pack under test; From the electrical parameter data, select all time periods during which the voltage of each battery cell falls within a preset voltage characteristic range during a single charge; The charging capacity of each battery cell in each time period is calculated using the ampere-hour integration method. The discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range are determined based on all the charging capacity.

3. The rapid testing method for new energy vehicle batteries as described in claim 1, characterized in that, The differences in the state of charge of each battery cell during the charging process are determined based on the actual capacity value and the initial open-circuit voltage of each battery cell. Specifically, this includes: The initial state of charge of each battery cell before charging is determined based on its initial open-circuit voltage. Obtain the real-time current data of the battery pack under test; The current state of charge of the corresponding battery cell is calculated based on the real-time current data and the actual capacity value of each battery cell. The difference in state of charge of each battery cell during the charging process is determined based on the initial state of charge and the current state of charge of each battery cell.

4. The rapid testing method for new energy vehicle batteries as described in claim 1, characterized in that, Extracting the temperature rise characteristics of each battery cell during the charging phase from the temperature data specifically includes: From the temperature data, select temperature records that completely cover a single charging process; Calculate the temperature difference of each battery cell at the start and end of charging based on the temperature records; Acquire temperature monitoring time data for each individual battery cell; The temperature rise characteristics of the corresponding battery cell during the charging stage are determined based on the monitoring time data and temperature difference of each battery cell.

5. The rapid testing method for new energy vehicle batteries as described in claim 1, characterized in that, Determining the health status detection value of the battery pack under test by using all relative aging gradients and the overall aging characteristics specifically includes: All relative aging gradients and the overall aging characteristics are combined into a multidimensional evaluation vector; The initial health status value of the battery pack under test is determined based on the multidimensional evaluation vector. Obtain the preset health status detection range of the battery pack under test; The health status detection value of the battery pack under test is determined based on the initial health status value and the health status detection interval.

6. The rapid testing method for new energy vehicle batteries as described in claim 1, characterized in that, The calibration of the health status of the battery pack under test based on the health status detection value specifically includes: Establish a three-level health status calibration system for the battery pack under test; The health status detection value is judged by the three-level health status calibration system to obtain the detection result of the battery pack under test, and the detection result is used as the health status of the battery pack under test.

7. The rapid testing method for new energy vehicle batteries as described in claim 1, characterized in that, Electrical parameter data of each cell in the battery pack of a new energy vehicle under test are collected during a single charging phase using voltage and current sensors.

8. A rapid testing device for new energy vehicle batteries, wherein the rapid testing method for new energy vehicle batteries according to any one of claims 1 to 7 is used for testing, characterized in that, The device includes: The data acquisition module is used to collect electrical parameter data, temperature data, and initial open-circuit voltage of each battery cell in the battery pack under test of the new energy vehicle during a single charging phase. The processing module is used to extract the discrete characteristics of the battery capacity of the battery pack under test within a preset voltage characteristic range from the electrical parameter data, determine the actual capacity value of each battery cell by combining the discrete characteristics with historical full-charge capacity data, determine the state of charge difference of the corresponding battery cell during the charging process based on the actual capacity value of each battery cell and the initial open-circuit voltage of each battery cell, and extract the temperature rise characteristics of each battery cell during the charging stage from the temperature data. The processing module is also used to perform aging analysis on the performance state of the corresponding battery cells during the charging stage based on the differences in state of charge and temperature rise characteristics of each battery cell in the battery pack under test, to obtain the cell aging index of each battery cell, and then to determine the overall aging characteristics of the battery pack under test. The processing module is also used to perform gradient analysis on the aging differences between individual battery cells based on the charge and discharge time of the battery pack under test and all individual cell aging indicators, so as to obtain the relative aging gradient of each individual battery cell in the battery pack under test. The execution module is used to determine the health status detection value of the battery pack under test through all relative aging gradients and the overall aging characteristics, and to calibrate the health status of the battery pack under test based on the health status detection value.

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