A battery testing method and system

Abstract

The present application relates to the technical field of electrical testing, and in particular to a battery testing method and system. The method comprises: obtaining voltage data sequences and historical log data of each battery cell in a battery pack to be tested; determining a dynamic instability score of each battery cell according to a parameter fluctuation rate index of each battery cell and a spatial correlation weight between each battery cell and an adjacent battery cell; determining a historical damage degree of each battery cell based on a repeated cumulative effect of all historical damage events corresponding to each battery cell analyzed based on the historical log data; deeply fusing the dynamic instability score and the historical damage degree through a nonlinear function to obtain a comprehensive risk index of each battery cell; determining a failure degree capable of representing the overall health risk of the battery pack based on the extreme value and distribution of the comprehensive risk indexes of all battery cells; and determining a health test result of the battery pack according to the failure degree. The method improves the accuracy and reliability of battery pack testing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical testing. Specifically, it relates to a battery testing method and system. BACKGROUND

[0002] In the testing process of new energy vehicles, accurate and deep state of health assessment of power battery packs is a core link to ensure long-term safety and performance of vehicles.

[0003] Currently, a portable diagnostic device is generally used to communicate with the battery management system (BMS) of a vehicle to perform a rapid test on the battery pack based on a snapshot mode. This test method captures static parameters of the battery pack at a certain moment, such as single-cell voltage, temperature, etc., within a few seconds, and compares them with standard thresholds to determine whether the static parameters of the battery pack are normal, so as to determine the state of health of the battery pack.

[0004] However, in long-term use of vehicles, the battery pack usually has a cumulative potential abnormal state. This potential abnormal state cannot be identified in time by the rapid test based on the snapshot mode. For example, repeated non-standard charging or high-load discharging of the battery pack will continuously induce a small amount of lithium precipitation on the negative electrode surface of the battery cell. The change in battery parameters caused by single precipitation is extremely weak. With the passage of time, the precipitated metal lithium will accumulate and grow into dangerous lithium dendrites, which will gradually pierce the internal separator and form a hidden internal micro-short circuit. For this cumulative damage from quantitative change to qualitative change, the test method based on the snapshot mode cannot identify the gradual abnormal process until the micro-short circuit develops into a serious internal short circuit, and the test method based on the snapshot mode can identify the abnormal state of the battery pack. In summary, the conventional test method based on the snapshot mode has a lag in identifying the cumulative potential abnormality of the battery pack caused by long-term use, resulting in inaccurate and incomplete test results. SUMMARY

[0005] To solve the problem that the potential cumulative abnormality of the battery pack cannot be tested in time by the snapshot test mode in the prior art, resulting in inaccurate and incomplete test results, the present application provides a battery testing method and system.

[0006] In a first aspect, the present application provides a battery testing method, comprising:

[0007] obtaining voltage data sequences of each battery cell in a battery pack to be tested and historical log data of the battery pack;

[0008] calculating a parameter fluctuation rate index of each battery cell according to the voltage data sequence of each battery cell, to represent the fluctuation intensity of the voltage data of each battery cell;

[0009] According to the physical topology of the battery pack, all adjacent battery cells of each battery cell are determined, and the spatial correlation weight between each battery cell and all adjacent battery cells is calculated by using the parameter fluctuation rate index of each battery cell and all adjacent battery cells; the dynamic instability score of each battery cell is calculated by combining the parameter fluctuation rate index and the spatial correlation weight of each battery cell;

[0010] The historical log data is analyzed to extract all historical damage events corresponding to each battery cell, the historical damage degree of each battery cell is determined by analyzing the repeated cumulative effect of all historical damage events, and the comprehensive risk index of each battery cell is generated by fusing the dynamic instability score and the historical damage degree of each battery cell;

[0011] The failure degree of the battery pack is calculated by fusing the comprehensive risk index of all battery cells, and whether the health status of the battery pack is qualified is determined according to the comparison result of the failure degree and the preset failure degree threshold, so as to complete the battery test.

[0012] This technical solution first obtains high-frequency time-series dynamic data and historical log data to build a three-dimensional data foundation containing micro-dynamics and macro-history for diagnosis. Based on this solid foundation, the solution does not stop at simple numerical comparison, but further refines the original data into insights with clear physical meaning. On the one hand, it converts the constantly changing voltage signal into a dynamic instability score that can distinguish between isolated noise and systematic jitter through time-series volatility and spatial correlation analysis, which is equivalent to accurately quantifying the current functional symptoms of the battery. On the other hand, it converts scattered historical logs into a historical damage degree that measures cumulative damage through a decay accumulation model, which is equivalent to quantifying the actual damage degree of the battery. By nonlinear fusion, the current health status is placed in the context of its historical health status for comprehensive evaluation, generating a comprehensive risk index for individual battery cells. This simulates the physical law that damaged components are more prone to failure under disturbance. By elevating all local diagnostic information to the final judgment of the overall safety of the battery pack, any single fatal flaw or widespread universal problem is exposed, ensuring accurate diagnosis of the health status of the battery pack within the limited time of the production line test.

[0013] As a preferred, the voltage data sequence of each battery cell in the battery pack to be tested is obtained, including:

[0014] A test time window and a sampling frequency are preset, and the voltage of each battery cell is continuously collected in the test time window according to the sampling frequency to form the voltage data sequence of each battery cell.

[0015] As a preferred method, the parameter volatility index of each cell is determined as follows: For any cell, the voltage data sequence of the cell is subjected to first-order difference processing to obtain a first-order difference sequence; the absolute value of all data in the first-order difference sequence is first calculated, and then the average value is calculated, and the average value is used as the parameter volatility index of the cell.

[0016] As a preferred embodiment, the spatial association weight between each cell and all adjacent cells is obtained by the following calculation formula:

[0017]

[0018] in, For the first Spatial association weights between each cell and all its adjacent cells For cell numbering, For the first The parameter volatility index of individual battery cells For the first The total number of all adjacent cells of a given cell. For the first The first cell The parameter volatility index of adjacent cells, A preset positive number used to prevent the denominator from being zero. It is the absolute value symbol.

[0019] This technical solution assesses the synchronicity of the parameter fluctuation behavior of a single cell with its physical neighboring cell group. When the parameter fluctuation behavior of a cell is highly consistent with that of its neighboring cells, it indicates that there is more likely to be a regional event around the cell. Conversely, the cell is more likely to be an isolated local anomaly. This can effectively distinguish whether the root cause of the fault is an isolated problem or a systemic risk.

[0020] As a preferred method, the dynamic instability score of each cell is obtained using the following formula:

[0021]

[0022] in, For the first Dynamic instability score of individual battery cells For the first The parameter volatility index of individual battery cells For the first Spatial association weights between each cell and all its adjacent cells It is a sine function.

[0023] This technical solution aims to calculate a dynamic instability score that can intelligently distinguish risk types based on the fluctuation level of the battery cell itself and its linkage with neighboring battery cells. Through a smooth nonlinear function, it achieves a significant amplification of systemic failure risk while tolerating isolated noise fluctuations, so that the final score can more accurately reflect the true danger level of different types of fluctuations.

[0024] Preferably, the historical damage level of each battery cell is determined by analyzing the cumulative effect of repeated historical damage events. This includes: for each battery cell, selecting all historical damage events corresponding to that cell from historical log data; for each historical damage event, extracting the basic severity weight of that historical damage event, the total number of times that historical damage event has occurred among all historical damage events, and the time interval between the most recent occurrence of that historical damage event and the current testing time; using an exponential decay function, determining the time decay coefficient of that historical damage event based on the time interval; multiplying the basic severity weight, the total number of occurrences, and the time decay coefficient to obtain the weighted damage level of that historical damage event; and summing the weighted damage levels of all historical damage events to obtain the historical damage level of the battery cell.

[0025] This technical solution successfully transforms discrete historical damage log data in the BMS into a quantitative historical damage level that accurately reflects the cumulative damage level of the battery cell by constructing a decay accumulation model for a single battery cell that integrates event severity, occurrence frequency, and time decay effects. This provides a clearly physical input for subsequent risk fusion.

[0026] Preferably, the time decay coefficient of historical damage events is determined based on the following method:

[0027] For the The first cell Historical damage events The time decay coefficient is , It is a natural exponential function. The preset time decay constant, It is the first The first cell The time interval between the most recent occurrence of a historical damage event and the time being tested.

[0028] As a preferred approach, the comprehensive risk index for each battery cell is generated by integrating the dynamic instability score and historical damage level of each cell, based on the following formula:

[0029] ;

[0030] in, is a comprehensive risk index of the nth battery cell, is a dynamic instability score of the nth battery cell, is a maximum value of the dynamic instability scores of all battery cells, is a hyperbolic tangent function, is a natural exponential function, is a historical damage degree of the nth battery cell, is an average value of the historical damage degrees of all battery cells, is a parameter for preventing the denominator from being 0.

[0031] This technical solution uses the statistical characteristics of the current data of the battery pack for dynamic self-calibration, thereby improving the accuracy and robustness of the test. This design enables the evaluation system to adaptively distinguish between two completely different situations: one is an abnormal battery cell in a battery pack with good overall health, and the other is a battery cell with similar indicators in a generally aged battery pack. The logic of combining the individual state of a single battery cell with the group background of the battery pack in which it is located for comprehensive judgment can more accurately identify the truly abnormal battery cell that poses a risk.

[0032] As a preferred embodiment, the failure degree of the battery pack is determined based on the following manner: taking the maximum value of the comprehensive risk indexes of all battery cells as an extreme risk index; counting the number of battery cells whose comprehensive risk indexes exceed a preset comprehensive risk index threshold, and calculating the proportion of this number in the total number of battery cells in the battery pack; performing a nonlinear amplification operation on the proportion to obtain a systemic risk index; and multiplying the extreme risk index and the systemic risk index to obtain the failure degree of the battery pack.

[0033] This technical solution uses an aggregation model to upgrade the local risk information of all battery cells to a failure degree representing the overall health status of the battery pack. This model not only focuses on the weakest link determined by the bucket theory (extreme risk index), but also nonlinearly quantifies the systemic collapse risk caused by the spread of risk (the number of battery cells whose comprehensive risk indexes exceed a preset comprehensive risk index threshold), so that the test results can take into account the severity of local problems and the universality of problems, and are more comprehensive and accurate.

[0034] In a second aspect, the present application also provides a battery test system, which comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of any one of the battery test methods.

[0035] The present application has the following effects:

[0036] ​​​The application can identify internal micro-short circuit and other cumulative potential abnormalities caused by long-term complex working conditions and improve the accuracy and comprehensiveness of battery test results by constructing a decay accumulation model to deeply mine historical damage events of each battery cell, nonlinearly fusing quantized historical damage and current dynamic instability analyzed by high-frequency sampling to generate a forward-looking comprehensive risk index. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is a method flowchart of the application;

[0038] Figure 2 is a portable test device diagram of the application;

[0039] Figure 3 is a comprehensive risk index distribution diagram of each battery cell of the battery pack of the application;

[0040] Figure 4 is a health test result diagram of the battery pack of the application;

[0041] Figure 5 is a failure degree distribution diagram of the battery pack sample of the application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the application.

[0043] Referring to Figure 1 A battery test method, specifically comprising the following steps:

[0044] S1: Obtain voltage data sequences and historical log data of each battery cell in a battery pack to be tested.

[0045] Through a short communication with the battery pack to be tested, high-frequency time sequence data for subsequent dynamic instability analysis and deep log data for historical damage event evaluation are synchronously collected, thereby providing an accurate data basis for constructing a comprehensive detection process of the battery pack.

[0046] Since a power battery pack is a complex system composed of hundreds or thousands of independent battery cells, the overall performance and safety state of the power battery pack essentially depend on the consistency and health state of each battery cell. Therefore, the most refined raw data is obtained by deeply accessing the battery cell level, and the process is performed at a battery test station of an automobile assembly line, and the specific implementation is as follows:

[0047] First, a reliable communication link is established at the physical and logical levels, and a communication protocol such as Figure 2The portable test device shown, with a portable test terminal with a standardized battery pack dedicated interface, is connected to the diagnostic CAN (Controller Area Network) bus of the battery pack through a physical interface, and the test terminal initiates a communication handshake on the bus and establishes a diagnostic session with the BMS (Battery Management System) of the entire battery pack to be tested. The BMS is responsible for monitoring and managing each cell in the battery pack. Through communication with the BMS, all underlying cell data can be accessed.

[0048] Next, in the established diagnostic session, two types of data collection for each cell are performed in parallel:

[0049] The first type is the collection of high-frequency dynamic voltage data sequences of each cell. This part of data collection is limited to a preset test time window and a sampling frequency. In this embodiment, the test time window is set to 3 seconds and the sampling frequency is set to 500Hz. The BMS will continuously report the voltage values of all individual cells it monitors through the CAN bus according to the test time window and the sampling frequency to obtain the voltage data sequence of each cell.

[0050] The test time window is set to 3 seconds because it is an engineering choice that balances the production line rhythm and data effectiveness. On the one hand, the operation time of each station on the automobile assembly line is usually strictly controlled within tens of seconds, and a 3-second test duration is sufficient to integrate into the rhythm without forming a production bottleneck. On the other hand, at a sampling rate of 500Hz, 3 seconds are sufficient to collect 1500 data points for each cell. Such sample size is sufficient for subsequent calculation of statistical characteristics such as volatility, ensuring the statistical significance and stability of the analysis results; The sampling frequency is set to 500Hz to identify millisecond-level intermittent faults occurring in individual cells or their connection points. Transient contact failure caused by wire harness virtual connection, terminal micro-vibration, etc. The voltage jump duration exhibited at the cell level is usually within 1-10 milliseconds. In order to accurately capture the shortest 2ms event (equivalent to a signal frequency of 500Hz), a sampling rate of 500Hz ensures that at least one sampling point falls within the fault interval, leaving a trace on the data sequence of the cell for analysis, avoiding missing critical fault information due to insufficient sampling rate.

[0051] The second type is the deep reading of historical log data related to each cell. In the same time window for collecting high-frequency data, the test terminal will send a series of diagnostic commands to the BMS in parallel to read the historical information associated with the specific cell in the internal non-volatile memory, which includes reading all historical fault records, each of which reflects a historical damage event that has occurred to the cell, and is located to the specific cell number through the additional information of the fault code, reading specific data identifiers that point to the records of extreme working conditions experienced in the life cycle of the battery pack, such as: historical maximum / minimum single cell voltage and its corresponding cell number, historical maximum / minimum cell temperature and its occurrence location, etc.

[0052] Through the above operation, in a short production line test operation, a three-dimensional data set containing the current micro-dynamics and past macro-experience is successfully established for each cell in the battery pack, providing a reliable data basis for all subsequent steps of risk quantification for individual cells and ultimately evaluating the overall health status of the battery pack.

[0053] S2: Calculate the dynamic instability score of each cell according to the voltage data sequence of each cell.

[0054] This step aims to generate a score indicator that reflects the connection stability of each cell from high-frequency time series dynamic data through a series of calculations. Specifically, it includes:

[0055] S21: Determine the parameter fluctuation rate indicator of each cell.

[0056] The purpose is to convert a dynamic voltage time series containing thousands of data points into a single numerical indicator that can stably measure the degree of fluctuation. Specifically, for any cell, perform first-order difference processing on the voltage data sequence of the cell to obtain a first-order difference sequence, take the absolute value of all data in the first-order difference sequence, then calculate the average of these absolute values, and take the final average as the parameter fluctuation rate indicator of the cell.

[0057] For example, 5 voltage data points of a cell are collected within a short period of time to form the following voltage data sequence (in volts): Perform first-order difference processing on the voltage data sequence, which is to subtract the previous data from the next data to obtain a first-order difference sequence of Take the absolute value of all data in the first-order difference sequence, then calculate the average to obtain 0.02 as the parameter fluctuation rate indicator.

[0058] The parameter fluctuation rate of each cell quantifies the overall instability of the voltage of each cell during the test. A physically connected stable and chemically healthy cell should have a first-order difference sequence that is continuously close to zero, and the parameter fluctuation rate index is also very low. On the contrary, any transient contact failure caused by connector micro-motion, wire harness virtual connection, etc. will be manifested as a spike pulse on the difference sequence, thereby raising the parameter fluctuation rate index. Therefore, the size of the parameter fluctuation rate index is directly inversely proportional to the stability of the cell connection, and is the cornerstone of subsequent risk assessment.

[0059] S22: Analyze the spatial correlation weight between each cell and all adjacent cells.

[0060] After obtaining the respective fluctuation rate index of each cell, further analysis is performed to determine whether the voltage fluctuation of the cell is a unique problem of the cell or a local regional system problem.

[0061] Before analysis, it is necessary to determine all adjacent cells of each cell, because a large number of systematic faults have the characteristic of local aggregation in the physical layer. For example, loosening of the collection wire or high-voltage connector of a single battery module will directly affect all cells in the module, or a micro-crack in a bus bar will affect the cells connected to it.

[0062] Therefore, by defining the physically adjacent cells according to the physical topology of the battery pack (such as module division, wire layout, series-parallel topology), an analysis framework that matches the potential fault mode can be constructed, so that the algorithm can accurately capture the regional fault characteristics that occur in patches.

[0063] Since all physically adjacent cells of each cell are pre-configured in the test system as prior knowledge, for each type of battery pack to be tested, the internal cell topology needs to be digitized in advance according to the design blueprint (such as CAD files). This is usually stored as a cell adjacency relationship table (which can be a JSON, XML, CSV file or database table), which clearly maps each cell ID and its list of all physically adjacent cell IDs, and thus obtains all adjacent cells of each cell.

[0064] For example, in JSON format, a small part of the cell adjacency relationship table is as follows:

[0065] {"Cell-001":["Cell-002"],"Cell-002":["Cell-001","Cell-003"],"Cell-105":["Cell-104","Cell-106","Cell-115"]};

[0066] In this list, Cell is the prefix for cell number. From this list, we can see that all physically adjacent cells of Cell-001 are numbered Cell-002; all physically adjacent cells of Cell-002 are numbered Cell-001 and Cell-003; and all physically adjacent cells of Cell-105 are numbered Cell-106 and Cell-115.

[0067] Before the test begins, the test software loads an adjacency table that matches the model of the battery pack under test. When calculating the spatial association weight between any cell and all its adjacent cells, the software only needs to query this table to instantly obtain the set of cell numbers of all physically adjacent cells. This process is a one-time configuration task, which ensures the accuracy, efficiency and repeatability of subsequent calculations.

[0068] After determining the set of cell numbers of all physically adjacent cells for each cell, the spatial association weight between each cell and all adjacent cells is calculated using the following formula:

[0069]

[0070] in, For the first Spatial association weights between each cell and all its adjacent cells For cell numbering, For the first The parameter volatility index of individual battery cells For the first The total number of all adjacent cells of a given cell. For the first The first cell The parameter volatility index of adjacent cells, To prevent the denominator from being zero, a preset positive number is set as follows: This is a very small positive number, close to 0, and is usually used for zero-prevention parameters. It is the absolute value symbol.

[0071] The numerator of this formula reflects the first The consistency of electrical characteristics between a cell and its neighboring cells is determined by the numerator. The closer the numerator is to zero, the more consistent the behavior of the cell is with its neighboring cells. Whether the group is stable or deteriorating, the better the consistency of electrical characteristics. Conversely, the larger the numerator is than zero, the more inconsistent the electrical characteristics are with the cell and its neighboring cells.

[0072] The denominator part of the formula represents the overall parameter fluctuation rate index in the local micro-environment composed of the cell and the adjacent cells, which provides a dynamic and adaptive evaluation scale for the numerator, and its core function is to eliminate the interference of the absolute fluctuation size and realize the situational relative evaluation.

[0073] The overall design logic of the formula is centered on the fraction part of the formula, which is a normalized, dimensionless index between 0 and 1. On this basis, the similarity conversion of the fraction part is realized by subtracting 1 from the overall structure of the fraction, and finally the spatial correlation weight is obtained. Through this conversion, an index measuring the difference is reversed into a more intuitive index measuring the degree of correlation. The value of the spatial correlation weight is dynamically adjusted between 0 and 1. The greater the value of the spatial correlation weight, the better the consistency of the electrical characteristics of the individual cell and its adjacent cells, and vice versa.

[0074] When a systematic event occurs, such as the loosening of the connectors in the entire module, the voltage of all cells in the module will simultaneously exhibit high fluctuations. At this time, the parameter fluctuation rate index of any cell is highly consistent with the average parameter fluctuation rate index of its adjacent cells, resulting in the numerator of the fraction approaching 0, and thus the fraction also approaching 0. The greater the spatial correlation weight between the individual cell and all adjacent cells, the smaller the spatial correlation weight when an isolated fault occurs, such as a single cell with a virtual soldering point. At this time, the parameter fluctuation rate index of the cell will be high, while the parameter fluctuation rate index of its adjacent cells will be stable at a low level. The numerator of the fraction is almost equal to the denominator, resulting in the fraction approaching 1. The smaller the spatial correlation weight between the individual cell and all adjacent cells.

[0075] S23: Calculate the dynamic instability score of each cell.

[0076] The purpose of this step is to fuse the information of the parameter fluctuation rate index and the spatial correlation weight of each cell into a final risk score. The risk size depends not only on the severity of the fault, but also on the type of fault. Systematic faults have a greater impact on the battery pack than isolated faults, and should be given a higher dynamic instability score.

[0077] The dynamic instability score of each cell is calculated by the following formula:

[0078]

[0079] wherein, is the dynamic instability score of the i-th cell, is the dynamic instability score of the i-th cell, is the dynamic instability score of the i-th cell, The volatility index of an individual battery cell represents a fundamental risk factor, reflecting how drastic the voltage fluctuations of that cell are. The more drastic the fluctuations, the higher the risk level will be identified, ensuring that the risk of failure of a single battery cell is not overlooked. For the first Spatial association weights between each cell and all its adjacent cells It is a sine function.

[0080] In this formula, This part constitutes a nonlinear risk amplification factor, which dynamically changes according to the failure mode (by...). The penalty weight should be determined based on the reflection of the risk level. exist Within this range, the degree to which risk is amplified is directly controlled. Its function is to of The range of values ​​is precisely mapped to the most variable range of the sine function. Within the domain, the sine function is monotonically increasing, and its slope smoothly transitions from maximum to minimum, which matches the nonlinear characteristics required by this scheme. Within the interval, the output value of the sine function smoothly increases from 0 to 1. When increasing from 0, the sine function curve is at its steepest point. This means that even if a cell has only a slight correlation with all its neighboring cells, the nonlinear risk amplification factor will be disproportionately increased to issue a strong early warning signal for systemic failure risks that are nascent and may develop into major problems. When the value approaches 1, for example, from 0.8 to 1, the curve of the sine function becomes very flat, and its slope approaches 0. This means that when a certain cell has a stable strong correlation with all its neighboring cells, the influence on the amplification factor becomes negligible, and eventually it smoothly converges to the maximum value of 2. This makes the model very robust when facing extreme systematic failures, avoiding drastic jumps or unbounded growth in scores due to small data disturbances, and ensuring the reliability of the algorithm in industrial environments. Part of it is the sine function The output range is shifted upwards overall, thus strictly limiting the range of the final nonlinear risk amplification factor to within a certain range. This means that the minimum value of a battery cell's dynamic risk score is the parameter volatility index of that battery cell, and the maximum value is the parameter volatility magnified by 100%.

[0081] Thus, when a single point of failure occurs in the battery pack, meaning a single cell is abnormal, Approaching 0, the sine function part is 0, and the amplification factor is... , , which realizes the risk amplification of single cell failure and only evaluates the risk according to the parameter fluctuation rate of single cell itself. When the battery pack has a systematic failure, the abnormality of the cells in the piece will be generated, tends to 1, the sine function part is 1, and the amplification coefficient is , , which realizes the risk amplification of such a systematic failure.

[0082] In summary, this step uses the nonlinear characteristics of the sine function to weight the spatial correlation weight. When the spatial correlation weight changes from 0 (isolated failure) to 1 (systematic failure), the amplification coefficient smoothly and nonlinearly increases from 1 to 2, so that the systematic fluctuation of the piece is given a higher risk score.

[0083] S3: Analyze the repeated cumulative effect of historical damage events of each cell based on historical log data to determine the historical damage degree of each cell.

[0084] After analyzing the dynamic instability scores of each cell in the battery pack, the historical working condition data of each cell is further introduced to evaluate the real risk level of these dynamic instabilities under the specific cumulative stress history background. The core is that the same dynamic instability score will have a greater failure risk if it is a cell with historical damage accumulation effect and a cell without historical damage accumulation effect. Therefore, by analyzing the repeated cumulative effect of historical damage events of each cell, the historical damage degree of each cell is determined, including:

[0085] S31: Extract historical damage events and related statistical features of each cell.

[0086] For any one cell, first filter out all the historical damage events directly related to the cell from the historical log data, and then for each historical damage event filtered out, extract the basic severity weight of the historical damage event, the total number of times the historical damage event has occurred among all historical damage events of the cell, and the time interval between the last time the historical damage event occurred and the time being tested.

[0087] Specifically, typical historical damage events include:

[0088] Damage events caused by single cell voltage transient overcharging: high voltage will directly damage the crystal structure of the positive electrode material, and at the same time cause the oxidation and decomposition of the electrolyte, generate gas, and cause the internal pressure to rise. This is an irreversible damage that will permanently reduce the capacity and safety of the battery. Since overcharging directly and quickly damages the cell, it is one of the direct causes of thermal runaway, and it is the most direct threat to the battery pack, so it is given the highest basic severity weight: 0.6.

[0089] Damage events caused by high-load discharge: High-current discharge generates enormous heat and mechanical stress. Over time, this can lead to the breakage of active material particles and their peeling off from the current collector, resulting in cumulative potential abnormal states. Because this event is a key factor accelerating battery cycle aging, its damage is complex. Its weight is moderate because it represents a significant loss to battery life and performance. However, its directness in triggering sudden safety accidents is generally lower than that of overcharge events; therefore, it is assigned a moderate base severity weight: 0.3.

[0090] Damage caused by excessive temperature: High temperatures accelerate the excessive thickening of the SEI film on the negative electrode surface. This process continuously consumes the limited active lithium ions within the battery, leading to capacity decay and increased internal resistance. Since this event primarily affects the long-term performance degradation of the battery, compared to the other two events, the process is milder and poses the least direct threat to the battery pack. Therefore, it is assigned a relatively small base severity weight: 0.1.

[0091] S32: Use the exponential decay function to determine the time decay coefficient for each historical damage event.

[0092] For the The first cell Historical damage events The time decay coefficient is: , It is a natural exponential function. It is the first The first cell The time interval between the most recent occurrence of a historical damage event and the current testing time. The preset time decay constant is empirically set to 0.05.

[0093] This is a format for an exponential decay function. The exponential decay function measures the impact of a historical damage event on the current test by the time interval between the most recent occurrence of that event and the current test. Specifically, for any given historical damage event, the larger the time interval between its most recent occurrence and the current test, the smaller the time decay coefficient; conversely, the smaller the time interval, the larger the time decay coefficient. This reflects the principle that more recent damage events have a greater impact on the current test.

[0094] for It also provides another way to determine:

[0095] The higher the base severity weight of a historical damage event, the more persistent its impact, i.e., the longer its half-life, and the smaller its corresponding time decay constant, and vice versa.

[0096] The base severity weight of each historical damage event is mapped to the half-life of the historical damage event, and the time decay constant of the historical event is determined by the half-life, which requires setting two base parameters: the base half-life, representing the shortest memory duration, set to 30 days (empirical value); and the impact coefficient: set to 100 (empirical value), representing that for every 0.1 increase in the base severity weight, the half-life increases by 10 days, used to amplify the persistent impact of serious events.

[0097] For each historical damage event, the product of the base severity weight and the impact coefficient of the historical damage event is first calculated, and then the base half-life is added to obtain the half-life of the historical damage event, and then the time decay constant is calculated according to the half-life of each historical damage event: , wherein, is the half-life of the historical damage event.

[0098] S33: Determine the historical damage degree of each battery cell.

[0099] For each battery cell, the base severity weight, the total number of occurrences, and the time decay coefficient of each historical damage event of the battery cell are multiplied to obtain the weighted damage degree of the historical damage event, and finally, the weighted damage degrees of all historical damage events of the battery cell are accumulated to obtain the historical damage degree of the battery cell.

[0100] Specifically, it is calculated by the following formula:

[0101]

[0102] , wherein, is the historical damage degree of the i-th battery cell, is the i-th historical damage event of the i-th battery cell, is the total number of historical damage events of the i-th battery cell, is the base severity weight of the j-th historical damage event of the i-th battery cell, reflecting the damage degree of different types of historical damage events to the electrochemical system of the battery cell, is the total number of occurrences of the j-th historical damage event of the i-th battery cell in all historical damage events of the i-th battery cell, which embodies the repeatability and cumulative effect of historical damage, and a one-time accidental historical damage event and a repeated historical damage event cause different long-term damage to the battery cell, is the i-th historical damage event of the i-th battery cell, is the total number of historical damage events of the i-th battery cell, is the base severity weight of the j-th historical damage event of the i-th battery cell, reflecting the damage degree of different types of historical damage events to the electrochemical system of the battery cell, is the total number of occurrences of the j-th historical damage event of the i-th battery cell in all historical damage events of the i-th battery cell, which embodies the repeatability and cumulative effect of historical damage, and a one-time accidental historical damage event and a repeated historical damage event cause different long-term damage to the battery cell, is the i-th historical damage event of the i-th battery cell, is the total number of historical damage events of the i-th battery cell, is the base severity weight of the j-th historical damage event of the i-th battery cell, reflecting the damage degree of different types of historical damage events to the electrochemical system of the battery cell, is the total number of occurrences of the j-th historical damage event of the i-th battery cell in all historical damage events of the i-th battery cell, which embodies the repeatability and cumulative effect of historical damage, and a one-time accidental historical damage event and a repeated historical damage event cause different long-term damage to the battery cell, is the i-th historical damage event of the i-th battery cell, is the total number of historical damage events of the i-th battery cell, the time interval between the time of the last historical damage event of the i-th battery cell and the testing time, in days.

[0103] wherein, is the time decay coefficient of the j-th historical damage event of the i-th battery cell, embodying the principle that the events occurring recently have higher relevance, part of the multiplication of the eigenvalues of the three dimensions, the weighted damage degree of the j-th historical damage event of the i-th battery cell is obtained, followed by an accumulation operation by the summation symbol, the weighted damage degrees of all relevant historical damage events of the battery cell are accumulated, and the historical damage degree which can comprehensively reflect the historical cumulative damage degree of the battery cell is obtained.

[0104] Through this decay accumulation model, the influence of the historical damage events recorded in the historical log on the battery cell is effectively evaluated and accumulated, and the potential cumulative abnormality generated by the long-term, repeated and cumulative growth of the micro-damage (such as continuous lithium precipitation) can be accurately identified, so as to timely take preventive maintenance strategies for the tested battery pack.

[0105] S4: Fusion of the dynamic instability scores and the historical damage degrees of each battery cell to generate a comprehensive risk index of each battery cell.

[0106] After obtaining the dynamic instability score representing the current dynamic characteristics of the battery cell and the historical damage degree representing the historical cumulative damage of the battery cell, any single indicator cannot completely evaluate the real risk of the battery cell. A battery cell with a high historical damage degree, even if its instantaneous dynamic instability score is low, its failure probability under disturbance is still much higher than that of a battery cell with a low historical damage degree. Therefore, in this step, the dynamic instability score and the historical damage degree are deeply fused to generate a comprehensive risk index. The comprehensive risk can more accurately reflect the real failure risk of the battery cell with cumulative damage under dynamic disturbance, thereby providing a more accurate and comprehensive quantitative decision basis for subsequent system-level health state evaluation.

[0107] Specifically, it is based on the following formula:

[0108]

[0109] wherein, is the comprehensive risk index of the i-th battery cell, is the dynamic instability score of the i-th battery cell, is the dynamic instability score of the i-th battery cell, is the dynamic instability score of the i-th battery cell, ​​​​​Max (Dynamic Instability Score of all cells), Hyperbolic Tangent Function, Natural Exponential Function, Historical Damage Degree of the i-th cell, Average of Historical Damage Degree of all cells, Parameter to prevent denominator from being zero, taking value .

[0110] In this formula, is a base risk term, which is the base part of the formula, representing the immediate risk derived from the current dynamic instability assessment, it ensures that the final comprehensive risk index of a cell is at least equal to its current dynamic instability score, a cell without historical damage events will also generate an immediate risk if it currently shows severe fluctuations.

[0111] In this formula, is a risk penalty term, which is derived by multiplying three parts, only under certain conditions will it produce a larger value, the first part : the size of the risk penalty term is proportional to the base risk, the higher the dynamic instability of a cell, the greater the additional penalty from its historical damage; the second part is a dynamic risk contribution factor, this part assesses the relative severity of the current dynamic instability of the cell in its battery pack group, normalizes the score of a single cell relative to the maximum score in the entire battery pack, the effect of the function is that when the of a cell is much smaller than , the dynamic risk contribution factor is also small; when approaches , the dynamic risk contribution factor is larger, tending to 1, which plays a kind of dynamic self-calibration role, when the dynamic instability of a cell is larger, it will contribute a higher dynamic risk factor. is a historical damage contribution factor, this part assesses the relative severity of the historical damage of the cell in its battery pack group, normalizes the historical damage of a single cell relative to the average level of the entire battery pack, the function has a saturation effect, when the historical damage of a cell is much lower than the average level , the factor value approaches 0; when ​When far above average, the factor value approaches 1, which also achieves dynamic self-calibration: only when the historical damage of a cell is far above average, it will contribute a higher historical damage factor.

[0112] The core effect of this formula is the accurate focus on risk, through the multiplication of each part, it realizes the identification and amplification of the most dangerous cell, and the additional risk penalty term will only get a significant penalty value when both the dynamic risk contribution factor and the historical damage contribution factor are large, which means that if a cell with serious historical damage is currently showing severe fluctuations, the additional risk penalty will be the largest, and its comprehensive risk index will be significantly raised, a cell with serious historical damage but currently showing stable performance, its additional risk penalty will be appropriately reduced, a cell currently showing severe fluctuations but with little historical damage, which can be ignored, it is considered that this is only temporary and accidental fluctuations, not enough to constitute a major risk, the additional risk penalty term will also be appropriately reduced.

[0113] By introducing and , these two statistical characteristic parameters, it can distinguish between abnormal cells in a healthy battery pack and cells with similar indicators in a generally aging battery pack, this judgment logic that combines individual state with group background improves the accuracy and robustness of the test.

[0114] In summary, this step accurately assesses the risk of each cell through a nonlinear fusion model, obtaining a comprehensive index that can reflect the true risk in depth.

[0115] From dynamic working condition analysis to comprehensive risk assessment, it generates a comprehensive risk index for each cell that deeply integrates current dynamic instability and historical cumulative damage, this index can better reflect the true health status and potential failure risk of the cell than a simple dynamic score or historical score, providing a reliable decision basis for the final system-level health test.

[0116] As shown in Figure 3 , taking 50 cells as an example, a distribution diagram of the comprehensive risk index of 50 cells is provided, from the figure, it can be seen that the comprehensive risk index of most cells is at a low level, below 0.0005, indicating that they are healthy in both current dynamic test and historical record, however, there are several cells whose comprehensive risk index is significantly higher, forming several prominent peaks, among them, the comprehensive risk index of cell No. 25 reaches a peak of about 0.0032, far exceeding all other cells, this cell meets the high and high two conditions, and has the greatest risk of failure.

[0117] S5: Determine the failure degree of the battery pack based on the comprehensive risk index of each battery cell, and determine the test result according to the failure degree.

[0118] The final risk of a battery pack is jointly determined by two core dimensions, one is the risk degree of its weakest link, and the other is the spread of its internal problems. In this step, the two dimensions are aggregated to make a final decision on the test result of the battery pack. Specifically, it includes:

[0119] S51: Determine the extreme risk index.

[0120] The single-point vulnerability of the system is evaluated by applying the barrel theory. In a battery pack composed of hundreds or thousands of battery cells in series, the first failure (such as internal short circuit) of any battery cell can trigger catastrophic consequences for the entire system. Therefore, the overall safety of the system is largely limited by the worst battery cell. Therefore, the maximum value of the comprehensive risk index of all battery cells is found as the extreme risk index, which represents the most explicit and direct danger faced by the battery pack. It plays a single-point veto role in the risk model, ensuring that if there is any state that is extremely poor and risk is extremely high, the final health score will inevitably be reduced, regardless of how well other battery cells perform, avoiding the neglect of single-point failure when evaluating the health of the battery pack.

[0121] S52: Evaluate the spread of risk.

[0122] In a battery pack, the higher the proportion of high-risk battery cells, the greater the spread of risk, and the greater the risk of systemic collapse. Conversely, the opposite is also true. Therefore, the number of battery cells with a comprehensive risk index exceeding a predetermined comprehensive risk index threshold is calculated, and the proportion of the number of battery cells in the total number of battery cells in the battery pack is calculated , The greater the proportion, the greater the spread of risk, and vice versa. The comprehensive risk index threshold is set based on the following method: the comprehensive risk indexes of all battery cells are sorted in ascending order, and the third quartile of the sorted values is used as the comprehensive risk index threshold.

[0123] S53: Calculate the systemic risk index of the battery pack.

[0124] The reflecting the spread of risk is nonlinearly amplified to obtain the systemic risk index. The specific nonlinear amplification is based on the following formula:

[0125]

[0126] wherein, is the systemic risk index of the battery pack, the proportion of the number of battery cells whose comprehensive risk index exceeds the comprehensive risk index threshold in the total number of battery cells, a parameter for preventing the denominator from being zero, set as .

[0127] This formula reflects the accelerating deterioration effect of systemic risk. In a system, the increase in failure points does not linearly superimpose the overall collapse risk, but accelerates the climb. When a large number of battery cells are in a sub-healthy state, it often indicates that there are batch manufacturing defects or systemic design problems, and the probability of battery pack failure chain reaction and battery cell cooperative failure will increase dramatically. very small (such as only 1% of the battery cells whose comprehensive risk index exceeds the threshold), the amplification effect is minimal, indicating that the battery pack only has sporadic battery cell failures, when reaches 0.5, the risk is amplified by one, indicating that the battery pack is in a significant sub-healthy state, when as high as 0.9, increases dramatically to 10, and the risk is amplified by 10 times, indicating that the battery pack has a very high risk sensitivity to battery cell failures.

[0128] S54: Evaluate the failure degree of the battery pack.

[0129] The extreme risk index is multiplied by the systemic risk index to obtain the failure degree of the battery pack, which takes into account both the short board and the universality of the problem. Specifically, the system-level failure index , where is the extreme risk, is the systemic risk coefficient. When there is a serious failure of a battery cell in the battery pack, extremely high, even if the risk spread degree is very low, the system-level failure index will be directly raised, thus accurately capturing the single-point failure risk of the short board. When there is a universal slow degradation of the battery cells in the battery pack, there may be no particularly prominent failure risk of any battery cell, the value is not large, but due to a large number of battery cells in a sub-healthy state, the risk spread degree is very high, resulting in a system-level failure index becoming extremely large, and after multiplication, will also be amplified to a very high level, thus accurately capturing this universal slow degradation failure.

[0130] S55: Determine whether the battery pack is healthy according to the failure degree of the battery pack to complete the test.

[0131] The failure degree of the battery pack is compared with a preset failure degree threshold 0.8, if the failure degree is not more than 0.8, it is determined that the health state of the battery pack is qualified, if the failure degree is more than 0.8, it is determined that the health state of the battery pack is unqualified, and a final test result is obtained.

[0132] As shown in Figure 4 , the figure contains two core elements: the column on the left represents the failure degree of the battery pack calculated according to the comprehensive risk index of all battery cells, the value is 0.0043, and the column on the right represents the preset failure degree threshold, the value is 0.8. Comparing the failure degree of the battery pack with the failure degree threshold, it is found that 0.0043 is much smaller than 0.8, which indicates that although there may be individual battery cells with high risk (such as the battery cell numbered 25) in this battery pack, the overall failure degree obtained by multiplying the extreme risk index and the systematic risk index has not reached the dangerous level that needs to be determined as unqualified. Therefore, the system finally determines that the health state of the battery pack is qualified.

[0133] Among them, the failure degree threshold is set to 0.8, which is determined based on the following way:

[0134] Firstly, 1000 battery pack samples, including 800 healthy battery pack samples and 200 failed battery pack samples, are pre-acquired, and a failure degree is calculated for each battery pack sample according to the method of the present technical solution, obtaining the failure degree distribution as shown in Figure 5 , it can be seen that the failure degrees of the healthy battery pack samples and the failed battery pack samples form two distribution areas that have both significant distinction and marginal overlap. The failure degrees of the 800 healthy battery pack samples are highly concentrated in the low segment, mainly distributed below 0.6, forming a high-density, narrow-band distribution, and the failure degrees of the 200 failed samples are mainly distributed in the high segment, most of which are above 0.7, with a wider distribution range. There is a small amount of overlap between the two distributions in the interval of 0.7 to 0.9. At the same time, a test result set containing 1000 rows of data is obtained, each row of data contains two key information: one is the failure degree of the battery pack sample, and the other is the true label of the battery pack sample. The label of the healthy battery pack sample is 0, and the label of the failed battery pack sample is 1;

[0135] Then, the failure degree threshold is defined as a classification rule, and the essence of the threshold is a rule for making yes or no judgment. In the current scenario, the classification rule is:

[0136] If the failure degree of a battery pack sample is greater than or equal to the failure degree threshold, the system determines that it has a failure and the health state is unqualified, otherwise, it is determined that it does not have a failure and the health state is qualified.​

[0137] Then, the accuracy of the evaluation result is evaluated by the confusion matrix for a single threshold value. Since the fault degree ranges from 0 to 1, starting from 0.1 to 1, every 0.1 is taken as a candidate threshold value. The candidate threshold values are 0.1, 0.2, 0.3, and so on to 1. Then, the classification rule corresponding to each candidate threshold value is applied to 1000 battery pack samples. Then, the determination result of the classification rule corresponding to the candidate threshold value is compared with the true label one by one. This comparison will produce four cases:

[0138] The first case is true positive. A fault battery pack sample, whose fault degree is also greater than or equal to the candidate threshold value, is determined to be faulty, and the health status is unqualified. It shows that the determination result of the candidate threshold value is consistent with the true label, and the determination result is correct.

[0139] The second case is false positive. A healthy battery pack sample, whose fault degree is also greater than or equal to the candidate threshold value, is determined to be faulty, and the health status is unqualified. It shows that the determination result of the candidate threshold value is inconsistent with the true label, and the determination result is incorrect.

[0140] The third case is true negative. A healthy battery pack sample, whose fault degree is less than the candidate threshold value, is determined to be non-faulty, and the health status is qualified. It shows that the determination result of the candidate threshold value is consistent with the true label, and the determination result is correct.

[0141] The fourth case is false negative. A fault battery pack sample, whose fault degree is less than the candidate threshold value, is determined to be non-faulty, and the health status is qualified. It shows that the determination result of the candidate threshold value is inconsistent with the true label, and the determination result is incorrect.

[0142] By counting the number of 1000 battery pack samples in the four cases, the confusion matrix of the candidate threshold value is obtained.

[0143] Specifically, the confusion matrix is constructed as follows: the confusion matrix takes the true label as the row and the determination result as the column. The determination result of the candidate threshold value for each battery pack sample is counted in the corresponding cell of the table by traversing 1000 battery pack samples.

[0144] The content of the confusion matrix of the candidate threshold value is shown in Table 1, as follows:

[0145] Table 1

[0146]

[0147] The confusion matrix clearly quantifies the correct and incorrect cases of the determination result under the candidate threshold value, and provides a direct data basis for calculating the true positive rate and the false positive rate subsequently.

[0148] Then, the Youden index is calculated, and the final fault degree threshold is determined based on the Youden index.

[0149] The Youden index is an important indicator for evaluating the performance of a binary classification model. The larger the value, the better the classification effect. Based on the statistical results of the confusion matrix, two core ratios are calculated:

[0150] True positive rate The proportion of all fault battery pack samples that are successfully determined as having faults by the candidate threshold, The higher, the higher the true positive rate of the classification based on the candidate threshold, and the lower the risk of misjudging the real fault battery pack as a healthy battery pack.

[0151] False positive rate The proportion of all healthy battery pack samples that are incorrectly determined as having faults by the candidate threshold, The lower, the lower the false positive rate of the classification based on the candidate threshold, and the lower the risk of misjudging the real healthy battery pack as a fault battery pack.

[0152] Youden index The Youden index cleverly combines the two abilities of false negative prevention and false positive prevention. An ideal fault degree threshold will make as high as possible, while as low as possible, so that their difference will reach the maximum.

[0153] Finally, for each candidate threshold from 0.1 to 1, a classification rule is defined, the confusion matrix is obtained, the true positive rate , false positive rate and Youden index are calculated, and the true positive rate , false positive rate and Youden index of all candidate thresholds are statistically analyzed to obtain the statistical results as shown in Table 2:

[0154] Table 2

[0155]

[0156] As can be seen from Table 2, when the threshold is set to 0.8, the Youden index reaches the maximum value of 0.985, proving that it is the best balance point for distinguishing between healthy battery pack samples and fault battery pack samples. Therefore, 0.8 is taken as the final fault degree threshold.

[0157] In summary, this step successfully converts the dispersed cell-level risk information into an indicator for accurately determining the health status of the battery pack as a whole, thereby achieving health testing of the battery pack.

[0158] The application further provides a battery test system, which comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of the battery test method in any of the above embodiments.

[0159] The above merely provides the preferred embodiments of the application, and is not intended to limit the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A battery testing method, characterized by, The method comprises: acquiring real-time voltage data sequences of each battery cell in the battery pack to be tested and historical log data of the battery pack; calculating a parameter fluctuation rate index of each battery cell based on the voltage data sequence of each battery cell, to represent the fluctuation intensity of the voltage data of each battery cell; determining all adjacent battery cells of each battery cell according to the physical topology structure of the battery pack, and calculating a spatial correlation weight between each battery cell and all adjacent battery cells by using the parameter fluctuation rate index of each battery cell and all adjacent battery cells, to satisfy: ； For the first Spatial association weights between each cell and all its adjacent cells For cell numbering, , The first The first battery cell, the first The first cell The parameter volatility index of adjacent cells, For the first The total number of all adjacent cells of a given cell. A preset positive number used to prevent the denominator from being zero. It is the absolute value symbol; calculating a dynamic instability score of each battery cell by combining the parameter fluctuation rate index and the spatial correlation weight of each battery cell, to satisfy: ； the dynamic instability score for the nth cell, is a sinusoidal function;​ analyzing the historical log data to extract all historical damage events corresponding to each battery cell, determining a historical damage degree of each battery cell by analyzing the repeated cumulative effect of all historical damage events, and fusing the dynamic instability score and the historical damage degree of each battery cell to generate a comprehensive risk index of each battery cell, to satisfy: ； For the first The comprehensive risk index of each battery cell This represents the maximum value of the dynamic instability score for all battery cells. It is the hyperbolic tangent function. It is a natural exponential function. For the first The extent of historical damage to each battery cell This represents the average historical damage level of all battery cells. Computing a time decay coefficient , is a natural exponential function, is a preset time decay constant, is a time interval between a time when the i th historical damage event of the i th battery cell occurred last time and a testing time. multiplying the basic severity weight, the total number of occurrences, and the time decay coefficient of each historical damage event to obtain a weighted damage degree of the historical damage event; accumulating the weighted damage degrees of all historical damage events to obtain the historical damage degree of the battery cell; fusing the comprehensive risk indexes of all battery cells to calculate a failure degree of the battery pack, and determining whether the health state of the battery pack is qualified according to the comparison result of the failure degree and a preset failure degree threshold, to complete the battery test.

2. The battery testing method of claim 1, wherein, The method comprises: presetting a test time window and a sampling frequency, and continuously collecting the voltage of each battery cell in the test time window according to the sampling frequency to form a voltage data sequence of each battery cell.

3. The battery testing method of claim 1, wherein, The parameter fluctuation rate index of each battery cell is determined based on the following method: for any battery cell, performing first-order difference processing on the voltage data sequence of the battery cell to obtain a first-order difference sequence; calculating the average value of the absolute values of all data in the first-order difference sequence, and taking the average value as the parameter fluctuation rate index of the battery cell.

4. The battery testing method of claim 1, wherein, The failure degree of the battery pack is determined based on the following method: taking the maximum value in the comprehensive risk indexes of all battery cells as an extreme risk index; counting the number of battery cells whose comprehensive risk index exceeds a preset comprehensive risk index threshold, calculating the proportion of the number of battery cells in the total number of battery cells in the battery pack, and performing a nonlinear amplification operation on the proportion to obtain a systematic risk index; multiplying the extreme risk index and the systematic risk index to obtain the failure degree of the battery pack.

5. A battery testing system, comprising: The battery test device comprises a memory and a processor, and the memory stores a computer program, and the processor executes the computer program to realize the steps of the battery test method according to any one of claims 1-4.

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