A method, apparatus, device, and storage medium for detecting abnormal battery charging and discharging.

Abstract

This application provides a method, apparatus, device, and storage medium for detecting abnormal battery charging and discharging. The method includes: performing pressure calibration on the battery's clamping state to obtain calibrated clamping state parameters; correcting the original battery data collected by the detection circuit based on the calibrated clamping state parameters to generate reference characteristic data; performing differential calculation between the reference characteristic data and real-time detection data during the charging and discharging test to determine dynamic offset parameters; and aligning the dynamic offset parameters with a standard charging and discharging curve to determine abnormal battery state parameters. By performing pressure calibration on the battery clamping state to obtain calibration parameters and correcting the original data to generate reference characteristic data, and then performing differential calculation and database comparison with real-time detection data, data deviations caused by poor clamp contact can be effectively eliminated, thereby accurately identifying abnormal battery states and improving the reliability of charging and discharging test data and the real-time nature of anomaly determination.

Description

Technical Field

[0001] This application relates to the field of battery testing technology, and in particular to a method, apparatus, equipment and storage medium for detecting abnormal battery charging and discharging. Background Technology

[0002] In the field of 3C digital products, such as smartphones, laptops, tablets, and digital cameras, the charge-discharge performance and safety of batteries are key indicators for production and use. To evaluate battery performance, battery manufacturers, R&D laboratories, and quality control agencies typically need to install batteries on testing equipment for charge-discharge cycle testing, while simultaneously collecting parameters such as battery voltage, current, and internal resistance. However, in existing technologies, the connection between the battery and the testing equipment generally uses flat clamps or alligator clips for direct contact. This connection method is prone to poor contact, leading to increased local contact resistance and distorting the collected charge-discharge data. Furthermore, poor contact can also cause localized overheating during testing, increasing safety risks. Existing testing methods lack a mechanism for handling the connection status between the battery and the clamp, making it difficult to accurately distinguish between data deviations caused by clamp contact problems and changes in the battery's own performance. Summary of the Invention

[0003] This application provides a method, apparatus, device, and storage medium for detecting abnormal battery charging and discharging, which solves the problem that related technologies have difficulty in accurately distinguishing between data deviations caused by fixture contact issues and changes in the battery's own performance.

[0004] The first aspect of this application provides a method for detecting abnormal battery charging and discharging, the method comprising: The pressure of the battery clamping state is calibrated according to the battery model parameters to obtain the calibrated clamping state parameters. Based on the calibrated clamping state parameters, the original data of the battery collected by the detection circuit is corrected to generate reference characteristic data; The dynamic offset parameters are determined by performing differential calculations between the reference characteristic data and the real-time detection data during the charge and discharge test. The abnormal state parameters of the battery are determined by aligning the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database.

[0005] Optionally, in the first implementation of the first aspect of this application, the step of performing pressure calibration on the battery's clamping state according to the battery model parameters to obtain calibrated clamping state parameters includes: The initial clamping force of the battery is determined based on the battery model parameters; The battery is fixed according to the initial clamping force, and the initial internal resistance data of the battery is collected; The clamping force is increased by a preset step, and the internal resistance value is remeasured after each step to obtain a continuous internal resistance measurement sequence. The rate of change of internal resistance within a preset number of measurements is determined based on the continuous internal resistance measurement sequence, and when the rate of change of internal resistance is lower than a preset change threshold, the calibrated clamping state parameters are determined.

[0006] Optionally, in a second implementation of the first aspect of this application, the step of correcting the original data of the battery collected by the detection circuit based on the calibrated clamping state parameters to generate reference characteristic data includes: Based on the first internal resistance value corresponding to the calibrated clamping state parameters, the contact resistance value of the detection circuit when detecting the battery is corrected, and a correction coefficient is generated. The collected raw data is corrected according to the correction coefficient to generate the reference characteristic data of the battery.

[0007] Optionally, in the third implementation of the first aspect of this application, the step of aligning the dynamic offset parameter with the standard charge-discharge curve corresponding to the battery model parameter in the battery characteristic database to determine the abnormal state parameter of the battery includes: Based on the dynamic offset parameters, the inflection point voltage, pulsating current peak value, and temperature gradient change rate during the charging and discharging process are extracted and combined in time order to form a multidimensional feature matrix. The multidimensional feature matrix is ​​aligned with the standard charge-discharge curves in the battery characteristic database, and residual sequences are calculated for the voltage, current, and temperature characteristics in each aligned interval. Based on the density and duration of feature points exceeding the preset interval in the residual sequence, an abnormal interval and the corresponding parameter set are determined, and the abnormal interval and the parameter set are integrated to generate the abnormal state parameters of the battery.

[0008] Optionally, in the fourth implementation of the first aspect of this application, after the step of aligning the dynamic offset parameter with the standard charge-discharge curve corresponding to the battery model parameter in the battery characteristic database to determine the abnormal state parameter of the battery, the method further includes: During the interval or after the charge-discharge test, the battery is controlled to disconnect the charge-discharge circuit and enter a resting state according to the time node recorded by the dynamic offset parameter, and the high-frequency sampling channel is started to obtain the relaxation voltage sequence of the resting state. Voltage data within a preset time period is extracted based on the relaxed voltage sequence, and the standard deviation of voltage fluctuation is calculated based on a continuous fixed time window. The voltage fluctuation standard deviation is compared with a preset standard deviation threshold. When there is a target time window where the standard deviation of voltage fluctuation is greater than the preset standard deviation threshold, the time period corresponding to the target time window is marked as a contact instability segment, and contact instability identification parameters are generated.

[0009] Optionally, in the fifth implementation of the first aspect of this application, after the step of aligning the dynamic offset parameter with the standard charge-discharge curve corresponding to the battery model parameter in the battery characteristic database to determine the abnormal state parameter of the battery, the method further includes: Obtain a set of dynamic offset curves for several batteries with the same battery model parameters in the same test batch; Each dynamic offset curve in the set of dynamic offset curves is cross-correlated with other dynamic offset curves to obtain the correlation coefficient matrix of each dynamic offset curve. The average similarity parameter between the corresponding dynamic offset curve and other dynamic offset curves is determined based on the correlation coefficient matrix. Dynamic offsets with an average similarity parameter lower than a preset similarity threshold are marked as outlier curves, and the batteries corresponding to the outlier curves are marked as batteries with abnormal connections.

[0010] Optionally, in the sixth implementation of the first aspect of this application, after the step of aligning the dynamic offset parameter with the standard charge-discharge curve corresponding to the battery model parameter in the battery characteristic database to determine the abnormal state parameter of the battery, the method further includes: Based on the health indicators in the abnormal state parameters, the connection health parameters of the battery are numerically adjusted according to the preset deduction rules, and the adjusted connection health parameters are input into the historical data sequence of the battery. By continuously detecting the historical data sequence, it is determined whether the connection health parameter shows a monotonically decreasing trend in a preset number of charge and discharge tests and whether the cumulative decrease exceeds a preset percentage threshold. If so, generate a warning indicator for the battery and trigger a prompt signal before the next test based on the warning indicator.

[0011] A second aspect of this application provides a battery charging / discharging anomaly detection device, which is used to implement a battery charging / discharging anomaly detection method. The battery charging / discharging anomaly detection device includes: The calibration module is used to perform pressure calibration on the battery's clamping state according to the battery model parameters, and obtain the calibrated clamping state parameters. The correction module is used to correct the original data of the battery collected by the detection circuit based on the calibrated clamping state parameters, and generate reference characteristic data. The calculation module is used to perform differential calculation between the reference characteristic data and the real-time detection data during the charge and discharge test to determine the dynamic offset parameters; The processing module is used to align the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database to determine the abnormal state parameters of the battery.

[0012] A third aspect of this application provides an electronic device, including a memory and a processor, wherein the processor is configured to execute a computer program stored in the memory, and when the processor executes the computer program, it implements the steps of the battery charging and discharging abnormality detection method provided in the first aspect of this application.

[0013] The fourth aspect of this application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the battery charging and discharging abnormality detection method provided in the first aspect of this application.

[0014] In summary, the battery charging / discharging anomaly detection method, apparatus, device, and storage medium provided in this application involves pressure calibration of the battery's clamping state based on battery model parameters to obtain calibrated clamping state parameters; correction of the original battery data collected by the detection circuit based on the calibrated clamping state parameters to generate baseline characteristic data; differential calculation of the baseline characteristic data with real-time detection data during the charging / discharging test to determine dynamic offset parameters; and alignment of the dynamic offset parameters with the standard charging / discharging curves corresponding to the battery model parameters in the battery characteristic database to determine the abnormal state parameters of the battery. This application, by pressure calibration of the battery clamping state to obtain calibration parameters and correcting the original collected data to generate baseline characteristic data, and then performing differential calculation and database comparison with the real-time detection data, can effectively eliminate data deviations caused by poor clamp contact, thereby accurately identifying abnormal battery states and improving the reliability of charging / discharging test data and the real-time nature of anomaly determination. Attached Figure Description

[0015] Figure 1 A schematic flowchart of the battery charging and discharging abnormality detection method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the program modules of the battery charging and discharging abnormality detection device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0016] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] To address the difficulty in accurately distinguishing between data deviations caused by fixture contact issues and changes in battery performance in related technologies, embodiments of this application provide a method for detecting abnormal battery charging and discharging, such as... Figure 1 This is a flowchart illustrating the battery charging / discharging anomaly detection method provided in this embodiment. The battery charging / discharging anomaly detection method includes the following steps: Step 110: Perform pressure calibration on the battery's clamping state according to the battery model parameters to obtain the calibrated clamping state parameters.

[0018] Specifically, when calibrating the battery's clamping state based on the battery model parameters, the mechanical state information of the battery under the current clamping environment is obtained by measuring and adjusting the force on the battery installed in the fixture. This includes the clamping force, clamping uniformity, and terminal contact. This information can reflect the possible impact of the fixture on the overall battery performance test. By collecting and recording these parameters, a quantitative description of the clamping state can be formed, which can avoid test deviations caused by uneven pressure or poor contact of the fixture, thereby ensuring that subsequent measurement data has a reference benchmark at the mechanical installation level.

[0019] In one optional embodiment of this example, the step of calibrating the clamping state of the battery according to the battery model parameters to obtain calibrated clamping state parameters includes: determining the initial clamping force of the battery according to the battery model parameters; fixing the battery according to the initial clamping force and collecting the initial internal resistance data of the battery; increasing the clamping force based on a preset step, and re-measuring the internal resistance value after each step to obtain a continuous internal resistance measurement sequence; determining the rate of change of internal resistance within a preset number of measurements based on the continuous internal resistance measurement sequence, and determining the calibrated clamping state parameters when the rate of change of internal resistance is lower than a preset change threshold.

[0020] In this embodiment, when determining the initial clamping force of the battery based on the battery model parameters, the mechanical pressure range applied by the clamp to the battery terminals and casing is calculated by consulting information such as the battery's size, thickness, terminal structure, and material properties. This ensures that the initial clamping force guarantees good contact between the battery terminals and the clamp without causing structural deformation or localized stress concentration due to excessive pressure, thus avoiding affecting the accuracy of internal resistance measurement and battery safety. In actual operation, the initial clamping force can be set to the middle value of the reference range. For example, for a certain model of lithium-ion polymer battery, the initial clamping force can be calculated by converting the terminal contact area and the spring force constant. Then, the force sensor on the clamp is used for real-time monitoring and adjustment to keep the battery stable within the clamp. The battery is fixed under this force condition, and initial internal resistance data is collected in a static charging / discharging state. The internal resistance data reflects the battery's internal resistance characteristics under a specific clamping force. The internal resistance value is measured by the small current pulse method or AC impedance method to measure the battery's impedance under instantaneous voltage changes. The obtained initial internal resistance data can provide a benchmark reference for subsequent clamping force optimization. During the process of increasing the clamping force in preset steps, the clamping force is gradually increased in preset increments by controlling the clamping drive mechanism, such as a micro motor or mechanical screw device. Each increase in clamping force is adjusted under the monitoring of the clamping force sensor, and the internal resistance value is remeasured in the stable state after each step. The data generated by each measurement includes the battery terminal voltage, current, and the internal resistance value calculated from the changes in current and voltage, forming a continuous internal resistance measurement sequence. These sequences can reflect the influence of clamping force changes on the battery's internal resistance. During the internal resistance measurement process, filters can be used to denoise the voltage and current signals to ensure the stability and continuity of the measurement data. At the same time, by controlling the sampling time interval, each measurement is performed under static battery conditions to avoid interference from dynamic changes in charging and discharging. When determining the internal resistance change rate based on the continuous internal resistance measurement sequence, the internal resistance values ​​within a preset number of measurements are differentially calculated. For example, three adjacent measurements are taken, and the percentage of the difference between the maximum and minimum values ​​to the average value is calculated to obtain the internal resistance change rate. When the rate of change of internal resistance in continuous measurements is lower than a preset threshold, such as 1%, it indicates that under the clamping force, the contact state between the battery terminals and the clamp tends to stabilize, and the internal resistance no longer changes significantly with the increase of clamping force, forming an internal resistance plateau region. Under this condition, the current clamping force and the corresponding internal resistance value can be recorded as calibrated clamping state parameters for subsequent correction of the original collected data. For example, for a 3.7V, 2500mAh lithium-ion battery, the initial clamping force is set to 20 Newtons. When the force is increased to 25, 30, and 35 Newtons in three consecutive steps, the rate of change of internal resistance measured in each step is 2.5%, 1.2%, and 0.8%, respectively. When the rate of change of internal resistance is less than 1% at 35 Newtons, it is determined that the relationship between clamping force and internal resistance has entered the plateau region, and 35 Newtons and the corresponding internal resistance value are recorded as calibrated clamping state parameters.

[0021] Step 120: Correct the original data of the battery collected by the detection circuit based on the calibrated clamping state parameters to generate reference characteristic data.

[0022] Specifically, when correcting the raw battery data collected by the detection circuit based on the calibrated clamping state parameters, the clamping force and contact characteristic parameters obtained in the previous step are used as a reference to adjust the voltage, current, and internal resistance signals collected during the battery's charging and discharging process. The deviation caused by the clamping action is calculated, and this deviation is subtracted from the raw collected data or mapped to obtain reference characteristic data reflecting the battery's own characteristics. This correction process can eliminate or reduce the interference of the clamping environment on the measurement signal, making the data more accurately reflect the battery's performance. In one optional implementation of this embodiment, the step of correcting the original data of the battery collected by the detection circuit based on the calibrated clamping state parameters to generate reference characteristic data includes: correcting the contact resistance value of the detection circuit when collecting the battery according to the first internal resistance value corresponding to the calibrated clamping state parameters, and generating a correction coefficient; correcting the collected original data according to the correction coefficient to generate the reference characteristic data of the battery.

[0023] In this embodiment, when correcting the contact resistance value of the detection circuit during battery data acquisition based on the first internal resistance value corresponding to the calibrated clamping state parameters, the contact resistance between the battery terminals and the clamp is identified by using the clamping state parameters and the corresponding internal resistance as a reference. This contact resistance is caused by uneven force applied by the clamp or minor unevenness on the terminal surface, resulting in a local resistance increment that is superimposed on the voltage and current signals acquired by the detection circuit, thus affecting the accuracy of the measurement. By comparing the first internal resistance value with the real-time measured internal resistance, the deviation caused by the contact resistance under the current clamping conditions is calculated and converted into a correction coefficient. The correction coefficient is a numerical value or vector representing the amount that needs to be subtracted or mapped from the voltage, current, and internal resistance signals, used to eliminate the additional impedance introduced by the contact between the clamp and the terminals. This correction coefficient can be generated by a linear proportional method or a weighted mapping method. For example, the measured value of the internal resistance plateau region can be mapped to the actual current change amplitude to obtain the voltage or current increment that should be corrected at each sampling point, thereby forming a correction sequence that can be directly applied to the original data. When generating baseline characteristic data for the battery by correcting the collected raw data using correction factors, the correction factors are applied to the originally collected voltage, current, and internal resistance signals to subtract or adjust the resistance added due to poor contact, making the signals closer to the battery's true characteristics. The correction process can be achieved through numerical calculations. For example, subtracting the voltage drop caused by contact resistance from the original voltage signal, and subtracting the deviation value of the calibrated internal resistance in the plateau region from the original internal resistance data, yields corrected data reflecting the battery's internal electrical characteristics. For instance, for a lithium-ion battery with an initial measured internal resistance of 50 milliohms, the first internal resistance value determined after clamping force calibration is 48 milliohms. The raw data collected by the detection circuit shows 52 milliohms due to contact resistance. Through calculation, a correction factor of -4 milliohms can be obtained. After subtracting this value from the current and voltage measurements, the generated baseline characteristic data represents the battery's true charge and discharge characteristics under stable clamping conditions.

[0024] Step 130: Perform differential calculation between the reference characteristic data and the real-time detection data during the charge and discharge test to determine the dynamic offset parameters.

[0025] Specifically, when performing differential calculations between the baseline characteristic data and the real-time detection data during the charge and discharge test, the difference or offset between the real-time collected voltage, current, and internal resistance characteristics and the corrected baseline characteristic data is calculated to form a dynamic offset parameter. This parameter can reflect the instantaneous changes and abnormal trends of the battery during the charge and discharge process. By comparing the data differences at different time points, the fluctuation characteristics of battery performance can be identified. Differential calculations can correlate the baseline state with the real-time state to form dynamic change information that can be analyzed.

[0026] Step 140: Align the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database to determine the abnormal state parameters of the battery.

[0027] Specifically, when aligning the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database, the offset parameters obtained in the previous step are compared segment by segment with the standard charge-discharge curves of the same model batteries stored in the database at the corresponding time or capacity intervals. The distribution and trend of the offset parameters in each interval are identified. By judging whether the offset amplitude exceeds the preset threshold or falls into a specific interval, it can be determined whether the battery has an abnormal state under the current charge-discharge conditions. This comparison process uses the reference information provided by the standard curves in the database to associate real-time measurement data with historical standards, thereby generating abnormal battery state parameters.

[0028] In one optional implementation of this embodiment, the step of aligning the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database to determine the abnormal state parameters of the battery includes: extracting the inflection point voltage, pulsating current peak value, and temperature gradient change rate during the charge-discharge process based on the dynamic offset parameters, and combining them in chronological order to form a multi-dimensional feature matrix; aligning the multi-dimensional feature matrix with the standard charge-discharge curves in the battery characteristic database by interval, and calculating the residual sequence for the voltage, current, and temperature characteristics in each aligned interval; determining the abnormal interval and the corresponding parameter set based on the feature point density and duration exceeding the preset interval in the residual sequence, and integrating the abnormal interval and the parameter set to generate the abnormal state parameters of the battery.

[0029] In this embodiment, when extracting the inflection point voltage, pulsating current peak value, and temperature gradient change rate during the charging and discharging process based on the dynamic offset parameters, the key feature points on the curves are identified by analyzing the voltage, current, and temperature signals collected by the battery during the charging and discharging cycle. The inflection point voltage refers to the position where the voltage curve undergoes a significant curvature change, which usually corresponds to the change in the rate of chemical reaction or the inflection point of charge state inside the battery. The pulsating current peak value is the local maximum value in the current waveform, reflecting the impact of transient charging and discharging load on the battery. The temperature gradient change rate represents the rate of temperature change per unit time, which can reflect the internal impedance or heat accumulation. By arranging and combining these feature values ​​in chronological order, a multi-dimensional feature matrix containing various electrical and thermal information is formed. Each row corresponds to the feature set at a certain time point, and each column corresponds to a specific type of feature, thereby achieving a comprehensive characterization of the charging and discharging process. When aligning the multidimensional feature matrix with the standard charge-discharge curves in the battery characteristic database, the feature matrix collected during the actual charge-discharge process is divided into several intervals according to capacity or time nodes. These intervals are then compared one-to-one with the standard curve intervals of the corresponding battery model in the database to form a time or capacity mapping relationship. This allows for the comparison of actual voltage, current, and temperature characteristics with standard reference characteristics within each aligned interval, and the calculation of residual sequences. The residual sequence refers to the difference between the actual feature value and the standard feature value at each sampling point, which reflects the degree of deviation of the battery from the standard behavior during charge-discharge. This deviation may originate from abnormal internal chemical state of the battery, poor local contact, or thermal management issues.

[0030] In one optional implementation of this embodiment, after aligning the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database to determine the abnormal state parameters of the battery, the method further includes: during the interval or after the charge-discharge test, controlling the battery to disconnect the charge-discharge circuit and enter a static state according to the time node recorded by the dynamic offset parameters, and starting the high-frequency sampling channel to obtain the relaxation voltage sequence of the static state; extracting voltage data within a preset time period according to the relaxation voltage sequence, and calculating the voltage fluctuation standard deviation according to a continuous fixed time window; comparing the voltage fluctuation standard deviation with a preset standard deviation threshold; when there is a target time window where the voltage fluctuation standard deviation is greater than the preset standard deviation threshold, marking the time period corresponding to the target time window as a contact instability segment, and generating a contact instability identification parameter.

[0031] In this embodiment, during the intervals or after the charge / discharge test, when the battery is controlled to disconnect the charge / discharge circuit and enter a resting state according to the time node recorded by the dynamic offset parameters, the external current path is cut off, so that the battery is no longer subjected to charging or discharging load, thereby eliminating the influence of external power interference on voltage changes. Simultaneously, it ensures that the internal chemical reaction of the battery naturally relaxes under resting conditions. The relaxation voltage refers to the characteristic curve of the battery voltage gradually recovering to an equilibrium state over time during the resting process. This curve reflects the internal chemical reaction kinetics and terminal contact state of the battery. By activating the high-frequency sampling channel, the voltage signal under resting conditions can be continuously acquired at millisecond or higher frequencies to form a relaxation voltage sequence. Voltage data within a preset time period is extracted based on the relaxation voltage sequence. When calculating the standard deviation of voltage fluctuations based on a continuous fixed time window, the relaxed voltage sequence is divided into segments according to a set time interval, each segment being called a time window. The standard deviation of the voltage value within each window is then calculated. The standard deviation is a statistical indicator that measures the amplitude of voltage fluctuations, reflecting whether the voltage is stable under static conditions. Voltage fluctuations may originate from uneven internal chemical reactions, poor terminal contact, or minor thermal effects. By using sliding or continuous windows, the voltage fluctuation characteristics over the time series can be obtained. For example, if the voltage fluctuates from 3.70 volts to 3.71 volts within the first 10 seconds of static conditions, the standard deviation is approximately 0.005 volts. However, under unstable contact conditions, the voltage in the same window may fluctuate to 3.73 volts, and the standard deviation increases significantly, thus forming a quantifiable fluctuation indicator. When comparing the standard deviation of voltage fluctuation with the preset standard deviation threshold, the standard deviation of each time window is compared with the preset threshold. The standard deviation threshold is a reference range set according to the battery model, capacity and resting conditions. It is used to distinguish between normal relaxation fluctuations and abnormal fluctuations caused by unstable contact. When the standard deviation of the window exceeds the threshold, it indicates that there may be instantaneous instability or clamping influence in the battery terminal contact during that time period, resulting in voltage jumps or pulsations. When a target time window exists where the standard deviation of voltage fluctuation exceeds a preset standard deviation threshold, the time period corresponding to this window is marked as an unstable contact segment, and an unstable contact identification parameter is generated. By recording the start and end times of the segment and the fluctuation amplitude information, structured parameters can be formed for subsequent charging and discharging anomaly analysis or alarm. For example, if the standard deviation reaches 0.008 volts within 5 to 10 seconds of rest, exceeding the threshold of 0.005 volts, then this 5 to 10-second segment is marked as an unstable contact segment. The unstable contact identification parameter records the time range and corresponding fluctuation value, providing a quantitative basis for determining the connection status between the battery and the clamp, ensuring that subsequent charging and discharging data analysis and anomaly identification can distinguish between the battery's own characteristics and the influence of clamp contact.

[0032] In one optional implementation of this embodiment, after aligning the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database to determine the abnormal state parameters of the battery, the method further includes: obtaining a set of dynamic offset curves for several batteries with the same battery model parameters in the same test batch; performing cross-correlation calculations on each dynamic offset curve in the set of dynamic offset curves with other dynamic offset curves to obtain the correlation coefficient matrix of each dynamic offset curve; determining the average similarity parameter between the corresponding dynamic offset curve and other dynamic offset curves based on the correlation coefficient matrix; marking dynamic offsets with an average similarity parameter lower than a preset similarity threshold as outlier curves, and marking the batteries corresponding to the outlier curves as batteries with abnormal connections.

[0033] In this embodiment, when obtaining a set of dynamic offset curves for several batteries with the same battery model and parameters in the same test batch, the sequence of dynamic offset parameters generated by each battery during charge-discharge cycles and their changes over time or capacity is collected. The dynamic offset curves corresponding to each battery are integrated into a curve set. The dynamic offset curve represents the deviation between the actual measured characteristics and the reference characteristics during charge-discharge, reflecting the instantaneous shift of battery performance with time, capacity, and environmental conditions. When performing cross-correlation calculations on each dynamic offset curve in the set with other dynamic offset curves, the similarity between the two curves at different time delays is calculated using mathematical methods. The cross-correlation calculation can reveal the synchronicity of the curve shapes and the degree of matching of the changing trends. The calculation results are expressed as correlation coefficients, with the correlation coefficient ranging from -1 to 1, where 1 represents perfect positive correlation, 0 represents no correlation, and -1 represents perfect negative correlation. This quantifies the similarity between each dynamic offset curve and other curves, forming a correlation coefficient matrix, where each matrix element corresponds to the correlation coefficient between two curves. When determining the average similarity parameter between a corresponding dynamic offset curve and other dynamic offset curves based on the correlation coefficient matrix, the correlation coefficients of each curve in the matrix with other curves are averaged to obtain the average similarity parameter. The average similarity parameter reflects the consistency of the curve with the dynamic offset behavior of other batteries in the same batch. For example, if the correlation coefficients of a certain curve with the other nine curves are 0.98, 0.96, 0.95, 0.97, 0.99, 0.94, 0.96, 0.95, and 0.97 respectively, then the average similarity is approximately 0.964. This parameter can be used as an important indicator to measure whether the offset curve of a single battery meets the consistency within the batch. Dynamic offsets with an average similarity parameter below a preset similarity threshold are marked as outlier curves, and the batteries corresponding to these outlier curves are marked as batteries with abnormal connections. By setting a reasonable threshold, such as 0.95, curves that differ significantly from other battery curves in the batch and exhibit abnormal offset trends are identified. This indicates that the battery has a problem in its clamping or contact state, and its dynamic offset characteristics deviate from standard behavior. Therefore, the corresponding batteries are marked as batteries with abnormal connections, providing a basis for subsequent abnormal charging and discharging analysis and safety monitoring. At the same time, it can quantify the consistency between batteries in the batch, providing an operable judgment standard for production quality control and anomaly screening.

[0034] In one optional implementation of this embodiment, after aligning the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database to determine the abnormal state parameters of the battery, the method further includes: adjusting the battery's connection health parameters based on the health indicators in the abnormal state parameters according to a preset deduction rule, and inputting the adjusted connection health parameters into the battery's historical data sequence; continuously detecting the historical data sequence to determine whether the connection health parameters show a monotonically decreasing trend in a preset number of charge-discharge tests and whether the cumulative decrease exceeds a preset percentage threshold; if so, generating a warning indicator for the battery, and triggering a prompt signal before the next test based on the warning indicator.

[0035] In this embodiment, when adjusting the battery's connection health parameters based on the health indicators in the abnormal state parameters and according to preset deduction rules, the abnormal characteristics generated by the battery during charging and discharging, including voltage inflection point deviation, pulsating current peak, abnormal temperature gradient, and unstable contact sections, are analyzed. These indicators are quantified into health loss values, and the current connection health parameters are deducted according to the preset deduction rules. The deduction rules can be in the form of linear proportion, weighted superposition, or piecewise function, used to adjust different types of abnormalities according to their importance. For example, if the voltage inflection point deviation is large in a certain test, the connection health is reduced proportionally. The deducted connection health parameters reflect the impact of the battery's current clamping or terminal contact condition on the overall performance, and are recorded and input into the battery's historical data sequence. The historical data sequence is a time series that stores the health parameter change information for each test, indexed by time or charge / discharge cycle number, and is used to track the evolution of the battery's long-term connection state. By continuously detecting historical data sequences, it is determined whether the connection health parameter shows a monotonically decreasing trend in a preset number of charge-discharge tests and whether the cumulative decrease exceeds a preset percentage threshold. The health parameter of several consecutive charge-discharge cycles in the historical sequence is analyzed, the difference between each measurement and the previous measurement is calculated, and all decrease differences are accumulated to obtain the cumulative decrease. At the same time, it is determined whether the health is continuously decreasing without rebounding. A monotonically decreasing trend indicates that the battery connection condition is continuously deteriorating in multiple cycles rather than fluctuating occasionally. For example, if the health values ​​of five consecutive tests are 0.98, 0.96, 0.94, 0.92, and 0.90, it shows a monotonically decreasing trend and the cumulative decrease is 0.08. If the threshold is set to 0.05, it exceeds the threshold, indicating that the connection condition has significantly deteriorated. If the battery meets the condition of monotonically decreasing and the cumulative decrease exceeds the threshold, a warning sign is generated. Based on the warning sign, when a prompt signal is triggered before the next test, the battery is recorded as a state requiring attention. The warning sign is then bound to the battery's unique identifier. Through the charge / discharge test system or the host computer control module, an audible and visual warning, interface prompt, or system pop-up is issued before the next test to remind the operator of potential connection abnormalities or clamping problems. This ensures that inspection or adjustment measures are taken in subsequent charge / discharge tests to reduce potential risks and provides quantitative monitoring data for long-term safe use of the battery and data reliability.

[0036] According to the battery charging and discharging abnormality detection method provided in this application, the battery clamping state is pressure calibrated to obtain calibration parameters and correct the original collected data to generate reference characteristic data. Then, the data is compared with the real-time detection data by differential analysis and database comparison. This can effectively eliminate data deviations caused by poor clamping contact, thereby accurately identifying abnormal battery states and improving the reliability of charging and discharging test data and the real-time nature of abnormality judgment.

[0037] Figure 2 This application provides a battery charging / discharging anomaly detection device, which can be used to implement the battery charging / discharging anomaly detection method described in the foregoing embodiments. Figure 2 As shown, the battery charging and discharging abnormality detection device mainly includes: The calibration module 10 is used to perform pressure calibration on the battery's clamping state according to the battery model parameters, and obtain the calibrated clamping state parameters. The correction module 20 is used to correct the original data of the battery collected by the detection circuit based on the calibrated clamping state parameters, and generate reference characteristic data. The calculation module 30 is used to perform differential calculation between the reference characteristic data and the real-time detection data during the charge and discharge test to determine the dynamic offset parameters; The processing module 40 is used to align the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database to determine the abnormal state parameters of the battery.

[0038] In one optional implementation of this embodiment, the calibration module is specifically used for: determining the initial clamping force of the battery based on the battery model parameters; fixing the battery based on the initial clamping force and collecting the initial internal resistance data of the battery; increasing the clamping force based on a preset step, and re-measuring the internal resistance value after each step to obtain a continuous internal resistance measurement sequence; determining the rate of change of internal resistance within a preset number of measurements based on the continuous internal resistance measurement sequence, and determining the calibrated clamping state parameters when the rate of change of internal resistance is lower than a preset change threshold.

[0039] In one optional implementation of this embodiment, the correction module is specifically used to: correct the contact resistance value of the detection circuit when sampling the battery based on the first internal resistance value corresponding to the calibrated clamping state parameters, and generate a correction coefficient; and correct the collected raw data based on the correction coefficient to generate the battery's reference characteristic data.

[0040] In one optional implementation of this embodiment, the processing module is specifically used to: extract the inflection point voltage, pulsating current peak value, and temperature gradient change rate during the charging and discharging process based on the dynamic offset parameters, and combine them in chronological order to form a multidimensional feature matrix; align the multidimensional feature matrix with the standard charging and discharging curves in the battery characteristic database by interval, and calculate the residual sequence for the voltage, current, and temperature characteristics in each aligned interval; determine the abnormal interval and the corresponding parameter set based on the feature point density and duration exceeding the preset interval in the residual sequence, and integrate the abnormal interval and the parameter set to generate the abnormal state parameters of the battery.

[0041] In an optional embodiment of this example, the processing module is further configured to: during the interval or after the charge-discharge test, control the battery to disconnect the charge-discharge circuit and enter a static state according to the time node recorded by the dynamic offset parameter, and start the high-frequency sampling channel to obtain the relaxation voltage sequence of the static state; extract voltage data within a preset time period according to the relaxation voltage sequence, and calculate the voltage fluctuation standard deviation according to a continuous fixed time window; compare the voltage fluctuation standard deviation with a preset standard deviation threshold; when there is a target time window where the voltage fluctuation standard deviation is greater than the preset standard deviation threshold, mark the time period corresponding to the target time window as the contact instability segment, and generate contact instability identification parameters.

[0042] In an optional embodiment of this example, the processing module is further configured to: acquire a set of dynamic offset curves for several batteries with the same battery model parameters in the same test batch; perform cross-correlation calculation on each dynamic offset curve in the set of dynamic offset curves with other dynamic offset curves to obtain a correlation coefficient matrix for each dynamic offset curve; determine the average similarity parameter between the corresponding dynamic offset curve and other dynamic offset curves based on the correlation coefficient matrix; mark dynamic offset curves with an average similarity parameter lower than a preset similarity threshold as outlier curves, and mark the batteries corresponding to the outlier curves as batteries with abnormal connections.

[0043] In an optional implementation of this embodiment, the processing module is further configured to: adjust the battery's connection health parameters according to the health indicators in the abnormal state parameters based on a preset deduction rule, and input the adjusted connection health parameters into the battery's historical data sequence; determine whether the connection health parameters show a monotonically decreasing trend and the cumulative decrease exceeds a preset percentage threshold by continuously detecting the historical data sequence; if so, generate a warning sign for the battery, and trigger a prompt signal before the next test based on the warning sign.

[0044] According to the battery charging and discharging abnormality detection device provided in this application, by performing pressure calibration on the battery clamping state, obtaining calibration parameters and correcting the original collected data, generating reference characteristic data, and then performing differential and database comparison with real-time detection data, the data deviation caused by poor clamping contact can be effectively eliminated, thereby accurately identifying abnormal battery states, improving the reliability of charging and discharging test data and the real-time nature of abnormality judgment.

[0045] According to the scheme provided in this application Figure 3 An electronic device is provided as an embodiment of this application. This electronic device can be used to implement the battery charging / discharging anomaly detection method in the foregoing embodiments, and mainly includes: The system includes a memory 301, a processor 302, and a computer program 303 stored on the memory 301 and executable on the processor 302. The memory 301 and the processor 302 are connected via communication. When the processor 302 executes the computer program 303, it implements the battery charging / discharging anomaly detection method described in the foregoing embodiments. The number of processors can be one or more.

[0046] The memory 301 can be a high-speed random access memory (RAM) or a non-volatile memory, such as a disk storage device. The memory 301 is used to store executable program code, and the processor 302 is coupled to the memory 301.

[0047] Furthermore, embodiments of this application also provide a computer-readable storage medium, which may be disposed in the electronic device described in the above embodiments, and the computer-readable storage medium may be as described above. Figure 3 The memory in the illustrated embodiment.

[0048] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the battery charging / discharging anomaly detection method described in the foregoing embodiments. Furthermore, the computer-readable storage medium can also be a USB flash drive, external hard drive, read-only memory (ROM), RAM, magnetic disk, or optical disk, or any other medium capable of storing program code.

[0049] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0050] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0051] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for detecting abnormal charging and discharging of a battery, characterized in that, include: The pressure of the battery clamping state is calibrated according to the battery model parameters to obtain the calibrated clamping state parameters. Based on the calibrated clamping state parameters, the original data of the battery collected by the detection circuit is corrected to generate reference characteristic data; The dynamic offset parameters are determined by performing differential calculations between the reference characteristic data and the real-time detection data during the charge and discharge test. The abnormal state parameters of the battery are determined by aligning the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database.

2. The battery charging and discharging abnormality detection method according to claim 1, characterized in that, The step of calibrating the pressure of the battery's clamping state according to the battery model parameters to obtain the calibrated clamping state parameters includes: The initial clamping force of the battery is determined based on the battery model parameters; The battery is fixed according to the initial clamping force, and the initial internal resistance data of the battery is collected; The clamping force is increased by a preset step, and the internal resistance value is remeasured after each step to obtain a continuous internal resistance measurement sequence. The rate of change of internal resistance within a preset number of measurements is determined based on the continuous internal resistance measurement sequence, and when the rate of change of internal resistance is lower than a preset change threshold, the calibrated clamping state parameters are determined.

3. The battery charging and discharging abnormality detection method according to claim 2, characterized in that, The step of correcting the raw data of the battery collected by the detection circuit based on the calibrated clamping state parameters to generate reference characteristic data includes: Based on the first internal resistance value corresponding to the calibrated clamping state parameters, the contact resistance value of the detection circuit when detecting the battery is corrected, and a correction coefficient is generated. The collected raw data is corrected according to the correction coefficient to generate the reference characteristic data of the battery.

4. The battery charging and discharging abnormality detection method according to claim 1, characterized in that, The step of aligning the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database to determine the abnormal state parameters of the battery includes: Based on the dynamic offset parameters, the inflection point voltage, pulsating current peak value, and temperature gradient change rate during the charging and discharging process are extracted and combined in time order to form a multidimensional feature matrix. The multidimensional feature matrix is ​​aligned with the standard charge-discharge curves in the battery characteristic database, and residual sequences are calculated for the voltage, current, and temperature characteristics in each aligned interval. Based on the density and duration of feature points exceeding the preset interval in the residual sequence, an abnormal interval and the corresponding parameter set are determined, and the abnormal interval and the parameter set are integrated to generate the abnormal state parameters of the battery.

5. The battery charging and discharging abnormality detection method according to claim 1, characterized in that, After the step of aligning the dynamic offset parameter with the standard charge-discharge curve corresponding to the battery model parameter in the battery characteristic database to determine the abnormal state parameter of the battery, the method further includes: During the interval or after the charge-discharge test, the battery is controlled to disconnect the charge-discharge circuit and enter a resting state according to the time node recorded by the dynamic offset parameter, and the high-frequency sampling channel is started to obtain the relaxation voltage sequence of the resting state. Voltage data within a preset time period is extracted based on the relaxed voltage sequence, and the standard deviation of voltage fluctuation is calculated based on a continuous fixed time window. The voltage fluctuation standard deviation is compared with a preset standard deviation threshold. When there is a target time window where the standard deviation of voltage fluctuation is greater than the preset standard deviation threshold, the time period corresponding to the target time window is marked as a contact instability segment, and contact instability identification parameters are generated.

6. The battery charging and discharging abnormality detection method according to claim 1, characterized in that, After the step of aligning the dynamic offset parameter with the standard charge-discharge curve corresponding to the battery model parameter in the battery characteristic database to determine the abnormal state parameter of the battery, the method further includes: Obtain a set of dynamic offset curves for several batteries with the same battery model parameters in the same test batch; Each dynamic offset curve in the set of dynamic offset curves is cross-correlated with other dynamic offset curves to obtain the correlation coefficient matrix of each dynamic offset curve. The average similarity parameter between the corresponding dynamic offset curve and other dynamic offset curves is determined based on the correlation coefficient matrix. Dynamic offsets with an average similarity parameter lower than a preset similarity threshold are marked as outlier curves, and the batteries corresponding to the outlier curves are marked as batteries with abnormal connections.

7. The battery charging and discharging abnormality detection method according to claim 1, characterized in that, After the step of aligning the dynamic offset parameter with the standard charge-discharge curve corresponding to the battery model parameter in the battery characteristic database to determine the abnormal state parameter of the battery, the method further includes: Based on the health indicators in the abnormal state parameters, the connection health parameters of the battery are numerically adjusted according to the preset deduction rules, and the adjusted connection health parameters are input into the historical data sequence of the battery. By continuously detecting the historical data sequence, it is determined whether the connection health parameter shows a monotonically decreasing trend in a preset number of charge and discharge tests and whether the cumulative decrease exceeds a preset percentage threshold. If so, generate a warning indicator for the battery and trigger a prompt signal before the next test based on the warning indicator.

8. A battery charging / discharging abnormality detection device, characterized in that, The battery charging / discharging anomaly detection device is used to implement the battery charging / discharging anomaly detection method according to claim 1, and the battery charging / discharging anomaly detection device includes: The calibration module is used to perform pressure calibration on the battery's clamping state according to the battery model parameters, and obtain the calibrated clamping state parameters. The correction module is used to correct the original data of the battery collected by the detection circuit based on the calibrated clamping state parameters, and generate reference characteristic data. The calculation module is used to perform differential calculation between the reference characteristic data and the real-time detection data during the charge and discharge test to determine the dynamic offset parameters; The processing module is used to align the dynamic offset parameters with the standard charge-discharge curves corresponding to the battery model parameters in the battery characteristic database to determine the abnormal state parameters of the battery.

9. An electronic device, characterized in that, Includes memory and processor, of which: The processor is used to execute computer programs stored in the memory; When the processor executes the computer program, it implements the steps in the battery charging and discharging abnormality detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the battery charging and discharging abnormality detection method according to any one of claims 1 to 7.

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