A lithium battery deformation detection method and system

Abstract

The present invention discloses a lithium battery deformation detection method and system, and relates to the technical field of lithium battery deformation detection. Specifically, the invention discloses applying deformation test to lithium batteries, and performing state detection on each test battery to obtain a state change model, screening out abnormally deformed test lithium batteries that do not meet preset safety standards, collecting test pressure data of the periphery of these abnormally deformed batteries, constructing a test lithium battery pressure change model set, collecting pressure data of the periphery of the lithium battery in real time in actual application, constructing a real-time pressure change curve, setting a pressure threshold for the real-time pressure change curve of each pressure detection node, performing feature analysis if the threshold is exceeded, and matching with the test lithium battery pressure change model set to determine a prediction reference model for the lithium battery. The technical solution disclosed by the present invention realizes accurate detection of lithium battery deformation, and provides reference support for lithium battery state prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery deformation detection, and in particular to a lithium battery deformation detection method and system. Background Art

[0002] As an important component of modern energy storage, lithium batteries have been widely used in various electronic devices, electric vehicles and energy storage systems. However, during use, lithium batteries may deform due to factors such as charge and discharge cycles, temperature changes and external mechanical stress. This deformation not only affects the performance and service life of the battery, but may also cause safety hazards such as internal short circuits and electrolyte leakage, and even cause fires or explosions in severe cases.

[0003] Traditional lithium battery deformation detection methods mainly rely on post-inspection manual inspection or simple mechanical measurement. These methods have problems such as low accuracy, low efficiency, and inability to monitor in real time. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system capable of performing real-time and accurate deformation detection on a lithium battery.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A lithium battery deformation detection method, comprising:

[0007] Step S100, performing an application deformation test on the lithium battery, performing a state detection on each test lithium battery subjected to the application deformation test, obtaining a state change model, and comparing the state change model with a preset safety standard, screening out the test lithium batteries that do not meet the preset safety standard, and recording them as abnormal deformation test lithium batteries;

[0008] Step S200, collecting test pressure data of the periphery of the abnormal deformation test lithium battery, analyzing the test pressure data, constructing a test lithium battery pressure change model, and recording a combination of several test lithium battery pressure change models as a test lithium battery pressure change model set;

[0009] Step S300, in the process of testing the applied lithium battery, real-time pressure data of the periphery of the lithium battery is collected in real time, and the real-time pressure data is analyzed to construct a real-time pressure change curve for the pressure detection nodes of the periphery of the lithium battery;

[0010] Step S400, a pressure threshold is set for the real-time pressure change curve of each pressure detection node. If the real-time pressure change curve of a pressure detection node exceeds the pressure threshold, a feature analysis is performed on the real-time pressure change curve to determine the feature factor. If a test lithium battery pressure change model that matches the feature factor exists in the test lithium battery pressure change model set, the state change model corresponding to the matching test lithium battery pressure change model is identified as the prediction reference model of the lithium battery.

[0011] In an embodiment disclosed in the present invention, a method for detecting the state of each test lithium battery subjected to a deformation test includes:

[0012] Step S101, using voltage detection technology or internal resistance detection technology to determine the initial health state of the tested lithium battery, and simultaneously determine the temperature value of the tested lithium battery;

[0013] Step S102, constructing a test temperature change curve based on the temperature value change of the test lithium battery, and determining that the test lithium battery is an abnormally deformed test lithium battery if the duration of the peak value and the rising curvature of the test temperature change curve is greater than or equal to a preset value;

[0014] Step S103, performing a linear fit on the initial health state degree corresponding to the abnormal deformation test lithium battery to obtain an initial health state degree curve.

[0015] In some embodiments disclosed in the present invention, the method of analyzing abnormal pressure data and constructing an abnormal lithium battery pressure change model includes:

[0016] Step S201, constructing a three-dimensional model of a lithium battery for a lithium battery, and setting a plurality of pressure detection nodes on the three-dimensional model of the lithium battery;

[0017] Step S202, obtaining the test pressure data of each pressure detection node, and analyzing the test pressure data of each pressure detection node to determine the test pressure values ​​at different time nodes;

[0018] Step S203, identifying the combination of test pressure values ​​of different pressure detection nodes but belonging to the same time node as a test pressure value group, and sorting the test pressure value group based on the time progress to obtain a test pressure value group sequence;

[0019] In step S204, a pressure reference line is vertically set for the pressure detection node on the three-dimensional model of the lithium battery. The length of the pressure reference line is used to express the high and low test pressure values. Based on the test pressure value group sequence, the length of the pressure reference line of different pressure detection nodes is driven to change, and a test lithium battery pressure change model is obtained.

[0020] In some embodiments disclosed in the present invention, the method for obtaining a pressure variation model for testing a lithium battery further includes:

[0021] Step S2041, configuring the pressure reference line of each pressure detection node on the lithium battery three-dimensional model based on the test pressure value group to form a test lithium battery pressure change frame corresponding to the test pressure value group;

[0022] Step S2042, determining a change sequence of a test lithium battery pressure change frame based on the test pressure value group sequence, and determining a change rate of the test lithium battery pressure change frame based on the time interval between the test pressure value groups in the test pressure value group sequence.

[0023] In some embodiments disclosed in the present invention, the method for performing characteristic analysis on the real-time pressure change curve includes:

[0024] Step S401, marking the portion of the real-time pressure change curve that is higher than the pressure threshold as a characteristic curve segment;

[0025] Step S402, determine the first time length and peak value of the characteristic curve segment, record them as the first characteristic factor, determine the rising curvature of the characteristic curve segment at different positions, and mark the rising curve segments whose rising curvature is greater than or equal to a preset value, and record the second time length corresponding to the rising curve segment and the time interval between the rising curve segments as the second characteristic factor.

[0026] In an embodiment disclosed in the present invention, a method for determining a test lithium battery pressure change model that matches a characteristic factor in a test lithium battery pressure change model set includes:

[0027] Step S403, performing multi-threaded simulation operation on several test lithium battery pressure change models in the test lithium battery pressure change model set, and taking the first characteristic factor as a monitoring condition, monitoring the simulated test lithium battery pressure change models, and screening out the test lithium battery pressure change models that meet the first characteristic factor;

[0028] Step S404: using the second characteristic factor, determining the matching degree of the screened test lithium battery pressure variation models, and marking the test lithium battery pressure variation model with the greatest matching degree as matching the characteristic factor.

[0029] In some embodiments disclosed in the present invention, the method for determining the matching degree of the pressure variation model of the tested lithium battery by using the second characteristic factor includes:

[0030] Step S4041, monitoring the pressure change model of the tested lithium battery, and determining and outputting the rising change rate of the pressure reference line corresponding to each pressure detection node of the pressure change model of the tested lithium battery;

[0031] Step S4042, monitoring the output rising change rate, if the rising change rate is greater than or equal to the preset value, starting to monitor the duration of the rising change rate, recording the state of the continuous rising change rate as a continuous state, and determining the time length difference between the duration length and the second time length, and calculating the average time length difference between all the time length differences;

[0032] Step S4043, comparing the time intervals between the continuous states of the rising change rate with the time intervals between the curve segments in sequence, determining the time interval differences, and calculating the average time interval difference between all the time interval differences;

[0033] Step S4044, calculate the sum of the average time length difference and the average time interval difference, record it as the total difference, and calculate the difference between the preset maximum matching degree and the total difference, record it as the initial matching degree.

[0034] In the embodiment disclosed in the present invention, the method for determining the matching degree of the pressure change model of the tested lithium battery further includes:

[0035] Step S4045, analyzing the maximum value among all time length differences, recording it as the maximum time length difference, analyzing the maximum value among all time interval differences, recording it as the maximum time interval difference;

[0036] Step S4046, for the maximum time length difference t0, a maximum time length difference comparison array t[t1, t2, t3, ..., tn] is set, wherein t1 is the first preset maximum time length difference, t2 is the second preset maximum time length difference, t3 is the third preset maximum time length difference, tn is the nth preset maximum time length difference, t1<t2<t3<...<tn, and for the maximum time interval difference T0, a maximum time interval difference comparison array T[T1, T2, T3, ..., Tn] is set, T1 is the first preset maximum time interval difference, T2 is the second preset maximum time interval difference, T3 is the third preset maximum time interval difference, Tn is the nth preset maximum time interval difference, T1<T2<T3<...<Tn;

[0037] If t0<t1, then the matching degree P=P0*K1, where P0 is the initial matching degree and K1 is the first preset time length difference correction coefficient;

[0038] If t1≤t0<t2, then the matching degree P=P0*K2, where K2 is the second preset time length difference correction coefficient;

[0039] If t2≤t0<t3, then the matching degree P=P0*K3, where K3 is the third preset time length difference correction coefficient; ...;

[0041] If tn-1≤t0<tn, then the matching degree P=P0*Kn, where Kn is the correction coefficient of the difference of the nth preset time length;

[0042] If T0<T1, then the matching degree P=P0*K*R1, where R1 is the first preset time interval difference correction coefficient;

[0043] If T1≤T0<T2, then the matching degree P=P0*K*R2, where R2 is the second preset time interval difference correction coefficient;

[0044] If T2≤T0<T3, then the matching degree P=P0*K*R3, where R3 is the third preset time interval difference correction coefficient; ...;

[0046] If Tn-1≤T0<Tn, then the matching degree P=P0*K*Rn, where Rn is the correction coefficient of the difference amount of the nth preset time interval.

[0047] In the embodiment disclosed in the present invention, there is also disclosed a lithium battery deformation detection system, comprising:

[0048] The first module is used to perform an application deformation test on the lithium battery, perform a state detection on each test lithium battery subjected to the application deformation test, obtain a state change model, and compare the state change model with a preset safety standard to screen out the test lithium batteries that do not meet the preset safety standard and record them as abnormal deformation test lithium batteries;

[0049] The second module is used to collect the test pressure data of the outer periphery of the abnormal deformation test lithium battery, analyze the test pressure data, construct a test lithium battery pressure change model, and record the combination of several test lithium battery pressure change models as a test lithium battery pressure change model set;

[0050] The third module is used to collect real-time pressure data of the periphery of the lithium battery in real time during the detection of the applied lithium battery, analyze the real-time pressure data, and construct a real-time pressure change curve for the pressure detection nodes of the periphery of the lithium battery;

[0051] The fourth module is used to set a pressure threshold for the real-time pressure change curve of each pressure detection node. If the real-time pressure change curve of a pressure detection node exceeds the pressure threshold, a feature analysis is performed on the real-time pressure change curve to determine the characteristic factor. If there is a test lithium battery pressure change model that matches the characteristic factor in the test lithium battery pressure change model set, the state change model corresponding to the matching test lithium battery pressure change model is identified as the prediction reference model of the lithium battery.

[0052] The present invention discloses a lithium battery deformation detection method and system, and relates to the technical field of lithium battery deformation detection. Specifically, the invention discloses applying deformation test to lithium batteries, and performing state detection on each test battery to obtain a state change model, screening out abnormally deformed test lithium batteries that do not meet preset safety standards, collecting test pressure data of the periphery of these abnormally deformed batteries, constructing a test lithium battery pressure change model set, collecting pressure data of the periphery of the lithium battery in real time in actual application, constructing a real-time pressure change curve, setting a pressure threshold for the real-time pressure change curve of each pressure detection node, performing feature analysis if the threshold is exceeded, and matching with the test lithium battery pressure change model set to determine a prediction reference model for the lithium battery. The technical solution disclosed by the present invention realizes accurate detection of lithium battery deformation, and provides reference support for lithium battery state prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A method step diagram of a lithium battery deformation detection method disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0055] The purpose of the present invention is to provide a method and system capable of performing real-time and accurate deformation detection on a lithium battery.

[0056] In order to achieve the above object, the present invention adopts the following technical solutions:

[0057] A lithium battery deformation detection method, comprising:

[0058] Step S100, applying deformation test to lithium batteries, performing state detection on each test lithium battery subjected to the application deformation test, obtaining a state change model, and comparing the state change model with a preset safety standard, screening out test lithium batteries that do not meet the preset safety standard and recording them as abnormal deformation test lithium batteries.

[0059] This step is to apply deformation testing to lithium batteries to simulate the deformation conditions in actual use. By performing state detection on each lithium battery undergoing deformation testing, the performance parameter changes of the battery under different deformation states can be obtained, and then a state change model can be obtained. This model reflects the relationship between battery deformation and performance. By comparing the state change model with the preset safety standards, lithium batteries whose performance deteriorates after deformation and do not meet safety standards can be screened out. These are recorded as abnormal deformation test lithium batteries and are the focus of subsequent analysis.

[0060] In an embodiment disclosed in the present invention, a method for detecting the state of each test lithium battery subjected to a deformation test includes:

[0061] Step S101, using voltage detection technology or internal resistance detection technology to determine the initial health state of the tested lithium battery, and simultaneously determine the temperature value of the tested lithium battery.

[0062] Step S102, based on the temperature value change of the test lithium battery, construct a test temperature change curve, and for the peak value and rising curvature of the test temperature change curve, if the duration of the peak value and the rising curvature is greater than or equal to a preset value, the test lithium battery is determined to be an abnormally deformed test lithium battery.

[0063] First, step S101 uses voltage detection technology or internal resistance detection technology to quantitatively evaluate the initial health status of the tested lithium battery and measures its temperature value at the same time. These two indicators are the basis for evaluating battery performance. The changes in voltage and internal resistance can directly reflect the health status of the battery, while temperature is an important factor affecting battery performance and life.

[0064] Next, step S102 constructs a test temperature change curve based on the temperature value change of the test lithium battery. By carefully analyzing the peak value and rising curvature of this curve, we can gain insight into the thermal behavior of the battery during the deformation process. If the duration of the peak value and the rising curvature is greater than or equal to the preset value, this indicates that the temperature change of the battery is abnormal and is caused by deformation, so the test lithium battery is identified as an abnormal deformation test lithium battery.

[0065] Step S103, performing a linear fit on the initial health state degree corresponding to the abnormal deformation test lithium battery to obtain an initial health state degree curve.

[0066] Step S200, collecting test pressure data of the periphery of the abnormal deformation test lithium battery, analyzing the test pressure data, constructing a test lithium battery pressure change model, and recording a combination of several test lithium battery pressure change models as a test lithium battery pressure change model set.

[0067] This step further studies the abnormal deformation test lithium battery. By collecting the test pressure data of the battery periphery and analyzing the data, a test lithium battery pressure change model can be constructed. This model reveals the change law of the peripheral pressure of the battery during deformation. Combining multiple such pressure change models forms a test lithium battery pressure change model set, which provides a reference for subsequent real-time pressure monitoring and prediction.

[0068] In some embodiments disclosed in the present invention, the method of analyzing abnormal pressure data and constructing an abnormal lithium battery pressure change model includes:

[0069] Step S201, constructing a three-dimensional model of a lithium battery for a lithium battery, and setting a plurality of pressure detection nodes on the three-dimensional model of the lithium battery.

[0070] Step S202, obtaining the test pressure data of each pressure detection node, and analyzing the test pressure data of each pressure detection node to determine the test pressure values ​​at different time nodes.

[0071] Step S203: identify the combination of test pressure values ​​of different pressure detection nodes but belonging to the same time node as a test pressure value group, and sort the test pressure value group based on the time progress to obtain a test pressure value group sequence.

[0072] In step S204, a pressure reference line is vertically set for the pressure detection node on the three-dimensional model of the lithium battery. The length of the pressure reference line is used to express the high and low test pressure values. Based on the test pressure value group sequence, the length of the pressure reference line of different pressure detection nodes is driven to change, and a test lithium battery pressure change model is obtained.

[0073] First, in step S201, an accurate three-dimensional model is constructed for the lithium battery to be studied. On this three-dimensional model, a number of pressure detection nodes are set, which will serve as key positions for subsequent pressure data collection and analysis.

[0074] Next, step S202 uses professional testing equipment or methods to obtain test pressure data at each pressure detection node. These data reflect the pressure distribution of the lithium battery under different deformation states. By analyzing these test pressure data, the test pressure values ​​at different time nodes can be determined, providing basic data for the subsequent pressure change model construction.

[0075] Then, step S203 combines the test pressure values ​​of different pressure detection nodes but at the same time node into a test pressure value group. As time goes by, these test pressure value groups are sorted in chronological order to obtain a test pressure value group sequence. This sequence clearly shows the change trend of the pressure value at each pressure detection node during the deformation process of the lithium battery.

[0076] Finally, step S204 sets a pressure reference line vertically on each pressure detection node on the three-dimensional model of the lithium battery. The length of this line is used to intuitively express the high and low test pressure values. Based on the previously obtained test pressure value group sequence, these pressure reference lines are driven to adjust their lengths according to the changes in the pressure values ​​in the sequence. In this way, we can obtain a dynamically changing test lithium battery pressure change model, which intuitively shows the pressure changes at various positions of the lithium battery during the deformation process.

[0077] In some embodiments disclosed in the present invention, the method for obtaining a pressure variation model for testing a lithium battery further includes:

[0078] Step S2041, configuring the pressure reference line of each pressure detection node on the lithium battery three-dimensional model based on the test pressure value group to form a test lithium battery pressure change frame corresponding to the test pressure value group.

[0079] Step S2042, determining a change sequence of a test lithium battery pressure change frame based on the test pressure value group sequence, and determining a change rate of the test lithium battery pressure change frame based on the time interval between the test pressure value groups in the test pressure value group sequence.

[0080] Step S300, in the process of detecting the applied lithium battery, real-time pressure data of the periphery of the lithium battery is collected in real time, and the real-time pressure data is analyzed to construct a real-time pressure change curve for the pressure detection nodes of the periphery of the lithium battery.

[0081] This step is to apply the previous research results to actual lithium battery testing; during the use of the lithium battery, real-time pressure data on the periphery of the battery is collected and analyzed; by constructing a real-time pressure change curve for the pressure detection nodes on the periphery of the lithium battery, the pressure changes of the battery during actual use can be intuitively observed; this provides the possibility of timely discovery of potential battery safety issues.

[0082] Step S400, a pressure threshold is set for the real-time pressure change curve of each pressure detection node. If the real-time pressure change curve of a pressure detection node exceeds the pressure threshold, a feature analysis is performed on the real-time pressure change curve to determine the feature factor. If a test lithium battery pressure change model that matches the feature factor exists in the test lithium battery pressure change model set, the state change model corresponding to the matching test lithium battery pressure change model is identified as the prediction reference model of the lithium battery.

[0083] This step is to further analyze and judge the real-time pressure change curve; first, set a pressure threshold for the real-time pressure change curve of each pressure detection node. This threshold is determined based on the previous test and analysis results, and represents the normal range of pressure changes in the battery during normal use; if the real-time pressure change curve exceeds this threshold, it means that the battery may have abnormal deformation or performance degradation. At this time, it is necessary to perform feature analysis on the real-time pressure change curve to determine the characteristic factor; then, find a model that matches this characteristic factor in the test lithium battery pressure change model set; if a matching model is found, the state change model corresponding to this model can be identified as the prediction reference model of the lithium battery. This prediction reference model can be used to predict future performance change trends and possible safety issues of the battery.

[0084] In some embodiments disclosed in the present invention, the method for performing characteristic analysis on the real-time pressure change curve includes:

[0085] Step S401: Mark the portion of the real-time pressure change curve that is higher than the pressure threshold as a characteristic curve segment.

[0086] Step S402, determine the first time length and peak value of the characteristic curve segment, record them as the first characteristic factor, determine the rising curvature of the characteristic curve segment at different positions, and mark the rising curve segments whose rising curvature is greater than or equal to a preset value, and record the second time length corresponding to the rising curve segment and the time interval between the rising curve segments as the second characteristic factor.

[0087] In an embodiment disclosed in the present invention, a method for determining a test lithium battery pressure change model that matches a characteristic factor in a test lithium battery pressure change model set includes:

[0088] Step S403, perform multi-threaded simulation operation on several test lithium battery pressure change models in the test lithium battery pressure change model set, and use the first characteristic factor as the monitoring condition to monitor the simulated test lithium battery pressure change model, and screen out the test lithium battery pressure change model that meets the first characteristic factor.

[0089] Step S404: using the second characteristic factor, determining the matching degree of the screened test lithium battery pressure variation models, and marking the test lithium battery pressure variation model with the greatest matching degree as matching the characteristic factor.

[0090] In some embodiments disclosed in the present invention, the method for determining the matching degree of the pressure variation model of the tested lithium battery by using the second characteristic factor includes:

[0091] Step S4041, monitoring the pressure variation model of the tested lithium battery, and determining and outputting the rising change rate of the pressure reference line corresponding to each pressure detection node of the pressure variation model of the tested lithium battery.

[0092] Step S4042, monitor the rising change rate of the output. If the rising change rate is greater than or equal to the preset value, start monitoring the duration of the rising change rate, record the state of the continuous rising change rate as a continuous state, determine the time length difference between the duration length and the second time length, and calculate the average time length difference between all time length differences.

[0093] Step S4043, comparing the time intervals between the continuous states of the rising change rate with the time intervals between the curve segments in sequence, determining the time interval differences, and calculating the average time interval difference between all the time interval differences.

[0094] Step S4044, calculate the sum of the average time length difference and the average time interval difference, record it as the total difference, and calculate the difference between the preset maximum matching degree and the total difference, record it as the initial matching degree.

[0095] In the embodiment disclosed in the present invention, the method for determining the matching degree of the pressure change model of the tested lithium battery further includes:

[0096] Step S4045, analyzing the maximum value among all time length differences, recording it as the maximum time length difference, analyzing the maximum value among all time interval differences, recording it as the maximum time interval difference.

[0097] Step S4046, setting a maximum time length difference comparison array t[t1, t2, t3, ..., tn] for the maximum time length difference t0, wherein t1 is the first preset maximum time length difference, t2 is the second preset maximum time length difference, t3 is the third preset maximum time length difference, tn is the nth preset maximum time length difference, t1<t2<t3<...<tn, and setting a maximum time interval difference comparison array T[T1, T2, T3, ..., Tn] for the maximum time interval difference T0, T1 is the first preset maximum time interval difference, T2 is the second preset maximum time interval difference, T3 is the third preset maximum time interval difference, Tn is the nth preset maximum time interval difference, T1<T2<T3<...<Tn.

[0098] If t0<t1, then the matching degree P=P0*K1, wherein P0 is the initial matching degree, and K1 is the first preset time length difference correction coefficient.

[0099] If t1≤t0<t2, then the matching degree P=P0*K2, wherein K2 is the second preset time length difference correction coefficient.

[0100] If t2≤t0<t3, then the matching degree P=P0*K3, wherein K3 is the third preset time length difference correction coefficient.

[0101] If tn-1≤t0<tn, then the matching degree P=P0*Kn, where Kn is the correction coefficient of the difference of the nth preset time length.

[0102] If T0<T1, then the matching degree P=P0*K*R1, where R1 is the first preset time interval difference correction coefficient.

[0103] If T1≤T0<T2, then the matching degree P=P0*K*R2, where R2 is the second preset time interval difference correction coefficient.

[0104] If T2≤T0<T3, then the matching degree P=P0*K*R3, where R3 is the third preset time interval difference correction coefficient.

[0105] If Tn-1≤T0<Tn, then the matching degree P=P0*K*Rn, where Rn is the correction coefficient of the difference amount of the nth preset time interval.

[0106] In the embodiment disclosed in the present invention, there is also disclosed a lithium battery deformation detection system, comprising:

[0107] The first module is used to perform an application deformation test on the lithium battery, perform a state detection on each test lithium battery subjected to the application deformation test, obtain a state change model, and compare the state change model with a preset safety standard to screen out the test lithium batteries that do not meet the preset safety standard and record them as abnormal deformation test lithium batteries;

[0108] The second module is used to collect the test pressure data of the outer periphery of the abnormal deformation test lithium battery, analyze the test pressure data, construct a test lithium battery pressure change model, and record the combination of several test lithium battery pressure change models as a test lithium battery pressure change model set;

[0109] The third module is used to collect real-time pressure data of the periphery of the lithium battery in real time during the detection of the applied lithium battery, analyze the real-time pressure data, and construct a real-time pressure change curve for the pressure detection nodes of the periphery of the lithium battery;

[0110] The fourth module is used to set a pressure threshold for the real-time pressure change curve of each pressure detection node. If the real-time pressure change curve of a pressure detection node exceeds the pressure threshold, a feature analysis is performed on the real-time pressure change curve to determine the characteristic factor. If there is a test lithium battery pressure change model that matches the characteristic factor in the test lithium battery pressure change model set, the state change model corresponding to the matching test lithium battery pressure change model is identified as the prediction reference model of the lithium battery.

[0111] The present invention discloses a lithium battery deformation detection method and system, and relates to the technical field of lithium battery deformation detection. Specifically, the invention discloses applying deformation test to lithium batteries, and performing state detection on each test battery to obtain a state change model, screening out abnormally deformed test lithium batteries that do not meet preset safety standards, collecting test pressure data of the periphery of these abnormally deformed batteries, constructing a test lithium battery pressure change model set, collecting pressure data of the periphery of the lithium battery in real time in actual application, constructing a real-time pressure change curve, setting a pressure threshold for the real-time pressure change curve of each pressure detection node, performing feature analysis if the threshold is exceeded, and matching with the test lithium battery pressure change model set to determine a prediction reference model for the lithium battery. The technical solution disclosed by the present invention realizes accurate detection of lithium battery deformation, and provides reference support for lithium battery state prediction.

[0112] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A lithium battery deformation detection method, characterized in that: include: Step S100, performing an application deformation test on the lithium battery, performing a state detection on each test lithium battery subjected to the application deformation test, obtaining a state change model, and comparing the state change model with a preset safety standard, screening out the test lithium batteries that do not meet the preset safety standard, and recording them as abnormal deformation test lithium batteries; Step S200, collecting test pressure data of the periphery of the abnormal deformation test lithium battery, analyzing the test pressure data, constructing a test lithium battery pressure change model, and recording a combination of several test lithium battery pressure change models as a test lithium battery pressure change model set; Step S300, in the process of testing the applied lithium battery, real-time pressure data of the periphery of the lithium battery is collected in real time, and the real-time pressure data is analyzed to construct a real-time pressure change curve for the pressure detection nodes of the periphery of the lithium battery; Step S400, a pressure threshold is set for the real-time pressure change curve of each pressure detection node. If the real-time pressure change curve of a pressure detection node exceeds the pressure threshold, a characteristic analysis is performed on the real-time pressure change curve to determine a characteristic factor. If a test lithium battery pressure change model matching the characteristic factor exists in the test lithium battery pressure change model set, the state change model corresponding to the matching test lithium battery pressure change model is identified as a prediction reference model for the lithium battery. Methods for characteristic analysis of real-time pressure change curves include: Step S401, marking the portion of the real-time pressure change curve that is higher than the pressure threshold as a characteristic curve segment; Step S402, determining a first time length and a peak value of the characteristic curve segment, recording them as a first characteristic factor, determining rising curvatures at different positions of the characteristic curve segment, marking rising curve segments whose rising curvatures are greater than or equal to a preset value, and recording a second time length corresponding to the rising curve segment and an inter-curve time interval between the rising curve segments as a second characteristic factor; The method for determining a test lithium battery pressure change model that matches the characteristic factor in a test lithium battery pressure change model set includes: Step S403, performing multi-threaded simulation operation on several test lithium battery pressure change models in the test lithium battery pressure change model set, and taking the first characteristic factor as a monitoring condition, monitoring the simulated test lithium battery pressure change models, and screening out the test lithium battery pressure change models that meet the first characteristic factor; Step S404, using the second characteristic factor, determining the matching degree of the screened test lithium battery pressure variation models, and marking the test lithium battery pressure variation model with the greatest matching degree as matching the characteristic factor; The method for judging the matching degree of the pressure change model of the tested lithium battery by using the second characteristic factor includes: Step S4041, monitoring the pressure change model of the tested lithium battery, and determining and outputting the rising change rate of the pressure reference line corresponding to each pressure detection node of the pressure change model of the tested lithium battery; Step S4042, monitoring the output rising change rate, if the rising change rate is greater than or equal to the preset value, starting to monitor the duration of the rising change rate, recording the state of the continuous rising change rate as a continuous state, and determining the time length difference between the duration length and the second time length, and calculating the average time length difference between all the time length differences; Step S4043, comparing the time intervals between the continuous states of the rising change rate with the time intervals between the curve segments in sequence, determining the time interval differences, and calculating the average time interval difference between all the time interval differences; Step S4044, calculate the sum of the average time length difference and the average time interval difference, record it as the total difference, and calculate the difference between the preset maximum matching degree and the total difference, record it as the initial matching degree.

2. A lithium battery deformation detection method according to claim 1, characterized in that: The method for performing condition detection on each test lithium battery subjected to the deformation test includes: Step S101, using voltage detection technology or internal resistance detection technology to determine the initial health state of the tested lithium battery, and simultaneously determine the temperature value of the tested lithium battery; Step S102, constructing a test temperature change curve based on the temperature value change of the test lithium battery, and determining that the test lithium battery is an abnormally deformed test lithium battery if the duration of the peak value and the rising curvature of the test temperature change curve is greater than or equal to a preset value; Step S103, performing a linear fit on the initial health state degree corresponding to the abnormal deformation test lithium battery to obtain an initial health state degree curve.

3. A lithium battery deformation detection method according to claim 2, characterized in that: The method of analyzing abnormal pressure data and constructing an abnormal lithium battery pressure change model includes: Step S201, constructing a three-dimensional model of a lithium battery for a lithium battery, and setting a plurality of pressure detection nodes on the three-dimensional model of the lithium battery; Step S202, obtaining the test pressure data of each pressure detection node, and analyzing the test pressure data of each pressure detection node to determine the test pressure values ​​at different time nodes; Step S203, identifying the combination of test pressure values ​​of different pressure detection nodes but belonging to the same time node as a test pressure value group, and sorting the test pressure value group based on the time progress to obtain a test pressure value group sequence; In step S204, a pressure reference line is vertically set for the pressure detection node on the three-dimensional model of the lithium battery. The length of the pressure reference line is used to express the high and low test pressure values. Based on the test pressure value group sequence, the length of the pressure reference line of different pressure detection nodes is driven to change, and a test lithium battery pressure change model is obtained.

4. A lithium battery deformation detection method according to claim 3, characterized in that: The method for obtaining the pressure change model for testing a lithium battery also includes: Step S2041, configuring the pressure reference line of each pressure detection node on the lithium battery three-dimensional model based on the test pressure value group to form a test lithium battery pressure change frame corresponding to the test pressure value group; Step S2042, determining a change sequence of a test lithium battery pressure change frame based on the test pressure value group sequence, and determining a change rate of the test lithium battery pressure change frame based on the time interval between the test pressure value groups in the test pressure value group sequence.

5. A lithium battery deformation detection method according to claim 1, characterized in that: The method for judging the matching degree of the pressure change model of the tested lithium battery also includes: Step S4045, analyzing the maximum value among all time length differences, recording it as the maximum time length difference, analyzing the maximum value among all time interval differences, recording it as the maximum time interval difference; Step S4046, setting a maximum time length difference comparison array t[t1, t2, t3, ..., tn] for the maximum time length difference t0, wherein t1 is the first preset maximum time length difference, t2 is the second preset maximum time length difference, t3 is the third preset maximum time length difference, tn is the nth preset maximum time length difference, t1<t2<t3<...<tn, and setting a maximum time interval difference comparison array T[T1, T2, T3, ..., Tn] for the maximum time interval difference T0, T1 is the first preset maximum time interval difference, T2 is the second preset maximum time interval difference, T3 is the third preset maximum time interval difference, Tn is the nth preset maximum time interval difference, T1<T2<T3<...<Tn; If t0<t1, then the matching degree P=P0*K1, where P0 is the initial matching degree and K1 is the first preset time length difference correction coefficient; If t1≤t0<t2, then the matching degree P=P0*K2, where K2 is the second preset time length difference correction coefficient; If t2≤t0<t3, then the matching degree P=P0*K3, where K3 is the third preset time length difference correction coefficient; ...； If tn-1≤t0<tn, then the matching degree P=P0*Kn, where Kn is the correction coefficient of the difference in the nth preset time length; If T0<T1, then the matching degree P=P0*K*R1, where R1 is the correction coefficient of the first preset time interval difference; If T1≤T0<T2, then the matching degree P=P0*K*R2, where R2 is the second preset time interval difference correction coefficient; If T2≤T0<T3, then the matching degree P=P0*K*R3, where R3 is the third preset time interval difference correction coefficient; ...； If Tn-1≤T0<Tn, then the matching degree P=P0*K*Rn, where Rn is the correction coefficient of the difference amount of the nth preset time interval.

6. A lithium battery deformation detection system, characterized in that: The method for detecting deformation of a lithium battery according to any one of claims 1 to 5 comprises: The first module is used to perform an application deformation test on the lithium battery, perform a state detection on each test lithium battery subjected to the application deformation test, obtain a state change model, and compare the state change model with a preset safety standard to screen out the test lithium batteries that do not meet the preset safety standard and record them as abnormal deformation test lithium batteries; The second module is used to collect the test pressure data of the outer periphery of the abnormal deformation test lithium battery, analyze the test pressure data, construct a test lithium battery pressure change model, and record the combination of several test lithium battery pressure change models as a test lithium battery pressure change model set; The third module is used to collect real-time pressure data of the periphery of the lithium battery in real time during the detection of the applied lithium battery, analyze the real-time pressure data, and construct a real-time pressure change curve for the pressure detection nodes of the periphery of the lithium battery; The fourth module is used to set a pressure threshold for the real-time pressure change curve of each pressure detection node. If the real-time pressure change curve of a pressure detection node exceeds the pressure threshold, a feature analysis is performed on the real-time pressure change curve to determine the characteristic factor. If there is a test lithium battery pressure change model that matches the characteristic factor in the test lithium battery pressure change model set, the state change model corresponding to the matching test lithium battery pressure change model is identified as the prediction reference model of the lithium battery.

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