Lithium battery internal short circuit diagnosis method and system based on impedance spectroscopy analysis

Abstract

The invention discloses a lithium battery internal short circuit diagnosis method and system based on impedance spectroscopy analysis, and relates to the technical field of lithium battery short circuit diagnosis, and the method comprises the steps: building a standard impedance spectroscopy library based on the identification identity information of a target battery; performing an electrochemical impedance spectrum test on the target battery to obtain a monitoring impedance spectrum; mapping the monitoring impedance spectrum to the standard impedance spectrum library, and judging a spectrum interval of the monitoring impedance spectrum; calculating the offset of the upper adjacent impedance spectrum and the lower adjacent impedance spectrum; calculating to obtain a virtual impedance spectrum; and fusing the virtual impedance spectrum and the monitoring impedance spectrum, extracting an impedance feature set, and carrying out short circuit judgment to obtain a short circuit diagnosis result. The lithium battery internal short circuit diagnosis method solves the technical problem that in the prior art, lithium battery internal short circuit diagnosis is affected by temperature and SOC, and accurate judgment is difficult, and the technical effects of achieving accurate judgment of lithium battery internal short circuit and improving the accuracy and reliability of lithium battery internal short circuit diagnosis are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery short - circuit diagnosis, and particularly to a method and system for diagnosing internal short - circuit of lithium batteries based on impedance spectrum analysis. Background Art

[0002] Lithium batteries are widely used in modern electronic devices, electric vehicles and other fields, and their safety and reliability are crucial. Internal short - circuit is a serious fault hidden danger during the operation of lithium batteries, which may cause problems such as battery performance degradation, overheating and even fire and explosion. In the prior art, both temperature and SOC (state of charge) have a significant impact on the impedance spectrum of lithium batteries. During the charge - discharge process of the battery, the core temperature is difficult to accurately measure and define, which makes it complex to diagnose the internal short - circuit of lithium batteries based on impedance spectrum analysis. At the same time, traditional diagnostic methods simply rely on setting fixed thresholds to judge short - circuits. Due to the differences in the ohmic resistance of each battery in the battery pack itself, false alarms or missed alarms are likely to occur, resulting in the inability to accurately identify internal short - circuits in the battery and making it difficult to meet the requirements for efficient and accurate diagnosis of lithium batteries.

[0003] There is a technical problem in the prior art that the diagnosis of internal short - circuit of lithium batteries is affected by temperature and SOC and it is difficult to accurately judge. Summary of the Invention

[0004] This application provides a method and system for diagnosing internal short - circuit of lithium batteries based on impedance spectrum analysis to solve the technical problem in the prior art that the diagnosis of internal short - circuit of lithium batteries is affected by temperature and SOC and it is difficult to accurately judge.

[0005] In view of the above problems, this application provides a method and system for diagnosing internal short - circuit of lithium batteries based on impedance spectrum analysis.

[0006] In the first aspect of this application, a method for diagnosing internal short - circuit of lithium batteries based on impedance spectrum analysis is provided. The method includes: Based on the identified identity information of the target battery, a standard impedance spectrum library is constructed, where the standard impedance spectrum library includes standard impedance spectra under various temperature-SOC combinations; combining the standard impedance spectrum library, an electrochemical impedance spectrum test is performed on the target battery to obtain a monitored impedance spectrum; the monitored impedance spectrum is mapped to the standard impedance spectrum library, the spectral interval of the monitored impedance spectrum is discriminated, and the upper adjacent impedance spectrum and the lower adjacent impedance spectrum are correspondingly obtained; the offset of the monitored impedance spectrum relative to the upper adjacent impedance spectrum and the lower adjacent impedance spectrum is calculated, and the relative spectral position is located according to the offset; based on the interpolation method, combining the relative spectral position, the upper adjacent impedance spectrum and the lower adjacent impedance spectrum, a virtual impedance spectrum is calculated and obtained; the virtual impedance spectrum and the monitored impedance spectrum are fused, an impedance feature set is extracted, and short-circuit discrimination is performed according to the impedance feature set to obtain a short-circuit diagnosis result, where the short-circuit diagnosis result includes a short-circuit discrimination result and a short-circuit level mark.

[0007] In the second aspect of the present application, a lithium battery internal short-circuit diagnosis system based on impedance spectrum analysis is provided, and the system includes: A standard impedance spectrum library construction module, configured to construct a standard impedance spectrum library based on the identified identity information of the target battery, where the standard impedance spectrum library includes standard impedance spectra under various temperature-SOC combinations; a monitored impedance spectrum acquisition module, configured to perform an electrochemical impedance spectrum test on the target battery in combination with the standard impedance spectrum library to obtain a monitored impedance spectrum; a monitored impedance spectrum mapping module, configured to map the monitored impedance spectrum to the standard impedance spectrum library, discriminate the spectral interval of the monitored impedance spectrum, and correspondingly obtain the upper adjacent impedance spectrum and the lower adjacent impedance spectrum; an offset calculation module, configured to calculate the offset of the monitored impedance spectrum relative to the upper adjacent impedance spectrum and the lower adjacent impedance spectrum, and locate the relative spectral position according to the offset; a virtual impedance spectrum acquisition module, configured to calculate and obtain a virtual impedance spectrum based on the interpolation method, combining the relative spectral position, the upper adjacent impedance spectrum and the lower adjacent impedance spectrum; a short-circuit diagnosis result acquisition module, configured to fuse the virtual impedance spectrum and the monitored impedance spectrum, extract an impedance feature set, and perform short-circuit discrimination according to the impedance feature set to obtain a short-circuit diagnosis result, where the short-circuit diagnosis result includes a short-circuit discrimination result and a short-circuit level mark.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages: Based on the identified identity information of the target battery, a standard impedance spectrum library is constructed; the target battery is subjected to an electrochemical impedance spectroscopy test to obtain a monitored impedance spectrum; the monitored impedance spectrum is mapped to the standard impedance spectrum library, the spectral interval of the monitored impedance spectrum is discriminated, and the upper adjacent impedance spectrum and the lower adjacent impedance spectrum are correspondingly obtained; the offset of the monitored impedance spectrum relative to the upper adjacent impedance spectrum and the lower adjacent impedance spectrum is calculated, and the relative spectral position is located according to the offset; based on an interpolation method, combining the relative spectral position, the upper adjacent impedance spectrum and the lower adjacent impedance spectrum, a virtual impedance spectrum is calculated and obtained; the virtual impedance spectrum and the monitored impedance spectrum are fused, an impedance feature set is extracted, and short-circuit discrimination is performed according to the impedance feature set to obtain a short-circuit diagnosis result. The technical effects of accurately discriminating the internal short circuit of the lithium battery and improving the accuracy and reliability of the internal short-circuit diagnosis of the lithium battery are achieved. Brief Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0010] Figure 1 It is a schematic flowchart of a method for diagnosing internal short circuit of a lithium battery based on impedance spectrum analysis provided by an embodiment of the present application; Figure 2 It is a schematic structural diagram of a system for diagnosing internal short circuit of a lithium battery based on impedance spectrum analysis provided by an embodiment of the present application.

[0011] Description of reference numerals: Standard impedance spectrum library construction module 10, monitored impedance spectrum acquisition module 20, monitored impedance spectrum mapping module 30, offset calculation module 40, virtual impedance spectrum acquisition module 50, short-circuit diagnosis result acquisition module 60. Detailed Description of the Embodiments

[0012] The present application provides a method and system for diagnosing internal short circuit of a lithium battery based on impedance spectrum analysis, which is used to solve the technical problem that it is difficult to accurately judge the internal short circuit of a lithium battery affected by temperature and SOC in the prior art.

[0013] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present application belong to the scope of protection of the present application.

[0014] Embodiment 1, asFigure 1 As shown, the present application provides a method for diagnosing internal short circuits in lithium batteries based on impedance spectroscopy analysis. The method includes: Step S100: Based on the identification identity information of the target battery, construct a standard impedance spectrum library, where the standard impedance spectrum library includes standard impedance spectra under various temperature-SOC combinations.

[0015] Specifically, starting from the identification identity information carried by the target battery, this information can uniquely identify the battery, ensuring that the subsequent constructed standard impedance spectrum library corresponds to a specific battery. Then, clarify the key parameter ranges required for constructing the standard impedance spectrum library, that is, determine the appropriate temperature range and SOC (State of Charge) range. Through the principle of orthogonal experimental design, within the selected temperature and SOC ranges, plan a series of representative temperature-SOC combination point sets. For each combination point, conduct multiple electrochemical impedance spectroscopy tests on the target battery, and these tests can obtain impedance data of the battery under different states. Subsequently, fuse the multiple test results under the same combination point to remove possible errors and interferences during the test, thereby obtaining more accurate and reliable standard impedance spectrum data. Finally, classify and store these fused standard impedance spectrum data according to different temperature-SOC combinations to construct a standard impedance spectrum library containing standard impedance spectra under various temperature-SOC combinations. This standard impedance spectrum library covers the normal impedance characteristics of the target battery under various temperature and SOC states, provides a key reference benchmark for subsequent monitoring of battery states and diagnosing internal short circuits, etc., and is an important basis for the smooth progress of the entire diagnosis process.

[0016] Step S200: Combine the standard impedance spectrum library to conduct an electrochemical impedance spectroscopy test on the target battery to obtain a monitored impedance spectrum.

[0017] Specifically, by means of the constructed standard impedance spectrum library, a comprehensive and accurate impedance spectrum test is carried out on the target battery to obtain the monitored impedance spectrum. First, the standard impedance spectrum library is deeply analyzed using statistical analysis methods to screen out the confidence frequency interval from a large amount of historical data. The determination of this interval can provide a reliable reference range for subsequent tests. Then, according to this confidence frequency interval, an alternating current excitation signal is applied to the electrodes of the target battery using an electrochemical test device, and the signal frequency covers the range from low frequency to high frequency to simulate the operating state of the battery under different working conditions. While applying the excitation signal, the voltage response data of the target battery are synchronously collected using a high-precision measuring instrument, and these data contain the electrochemical information inside the battery. Subsequently, according to the collected voltage response data and the known excitation signal parameters, the complex impedance data of the battery are calculated, and the complex impedance data can more comprehensively reflect the impedance characteristics inside the battery. Finally, the calculated complex impedance data are sorted and analyzed, and a monitored impedance spectrum is established according to certain rules and models. This monitored impedance spectrum visually presents the current impedance state of the target battery and provides a key basis for subsequent judgment of whether there are abnormal conditions such as internal short circuits in the battery.

[0018] Step S300: Map the monitored impedance spectrum to the standard impedance spectrum library, identify the spectral interval of the monitored impedance spectrum, and correspondingly obtain the upper adjacent impedance spectrum and the lower adjacent impedance spectrum.

[0019] Specifically, first extract the SOC mark from the monitored impedance spectrum, which records the current state of charge information of the battery. Then, using this SOC mark as a clue, conduct a comprehensive traversal in the standard impedance spectrum library to screen out the data corresponding to this SOC mark and construct a standard impedance spectrum sub-library. Then, construct a special impedance spectrum coordinate system that can accurately reflect the characteristics of the impedance spectrum in different dimensions. Map the constructed standard impedance spectrum sub-library into this coordinate system, and divide it into multiple different spectral intervals according to the data distribution. Then, map the monitored impedance spectrum into this impedance spectrum coordinate system as well. By comparing the position of the monitored impedance spectrum in the coordinate system, determine the specific spectral interval it belongs to. Once the belonging spectral interval is determined, extract the upper limit part from the boundary data of this interval as the upper adjacent impedance spectrum and extract the lower limit part as the lower adjacent impedance spectrum. This step provides a key data basis for subsequent in-depth analysis of the differences between the monitored impedance spectrum and the standard impedance spectrum and for calculating the virtual impedance spectrum, and is crucial for accurately judging whether there is an internal short circuit in the lithium battery.

[0020] Step S400: Calculate the offset of the monitored impedance spectrum relative to the upper adjacent impedance spectrum and the lower adjacent impedance spectrum, and locate the relative spectral position according to the offset.

[0021] Specifically, by deeply analyzing the differences between the monitored impedance spectrum and the standard impedance spectrum to more accurately locate the battery state, representative key frequency points in the impedance spectrum are first selected as comparison benchmarks. These key frequency points are determined based on the study of the battery's electrochemical characteristics and a large amount of experimental data, and they can sensitively reflect changes in the internal state of the battery. After determining the reference points, the impedance values of the monitored impedance spectrum are compared one by one with those of the upper adjacent impedance spectrum and the lower adjacent impedance spectrum at each key frequency point, and the offset difference at each key point is calculated. These differences reflect the degree of deviation of the monitored impedance spectrum from the upper and lower boundaries of the standard impedance spectrum at different key frequencies. Then, according to the importance of each key frequency point to the overall state of the battery, corresponding weights are assigned to the differences at each key point, and the overall offset is calculated by weighting to obtain the upper offset of the monitored impedance spectrum relative to the upper adjacent impedance spectrum and the lower offset relative to the lower adjacent impedance spectrum. Finally, the calculated upper offset and lower offset are normalized to convert them into values between 0 and 1. Through these two normalized offsets, the relative position of the monitored impedance spectrum within its spectral interval can be accurately determined, and its specific state within the range of the standard impedance spectrum can be understood, providing an accurate basis for calculating the virtual impedance spectrum and further improving the accuracy of internal short circuit diagnosis of lithium batteries.

[0022] Step S500: Based on the interpolation method, combine the relative spectral position, the upper adjacent impedance spectrum, and the lower adjacent impedance spectrum to calculate and obtain the virtual impedance spectrum.

[0023] Specifically, since it is difficult to accurately measure the actual temperature of the on-site battery, it is difficult to directly obtain the standard impedance spectrum that exactly matches the actual state. Therefore, a specific method needs to be used to construct a more practical reference spectrum, that is, the virtual impedance spectrum. First, use the relative spectral position information of the monitored impedance spectrum relative to the upper adjacent impedance spectrum and the lower adjacent impedance spectrum, and combine the data of the upper adjacent impedance spectrum and the lower adjacent impedance spectrum. Then, select a suitable interpolation algorithm, such as linear interpolation. Taking linear interpolation as an example, according to the relative spectral position, within the interval formed by the upper adjacent and lower adjacent impedance spectra, the impedance values corresponding to each frequency point are calculated according to the linear proportional relationship. Assume that the relative spectral position is x, the impedance value of the upper adjacent impedance spectrum at a certain frequency point is Z upper , and the impedance value of the lower adjacent impedance spectrum at this frequency point is Z lower , then the virtual impedance value Z virtual = (1 - x)Z lower + xZ upperFor each frequency point of the entire impedance spectrum, this calculation method is adopted to construct a complete virtual impedance spectrum. This virtual impedance spectrum can serve as a more accurate reference spectrum to effectively correct the deviations caused by factors such as temperature or SOC estimation errors, providing a reference basis for subsequently extracting reliable impedance features from the fusion of the virtual impedance spectrum and the monitored impedance spectrum and then performing accurate short-circuit discrimination.

[0024] Step S600: Fuse the virtual impedance spectrum and the monitored impedance spectrum, extract an impedance feature set, and perform short-circuit discrimination based on the impedance feature set to obtain a short-circuit diagnosis result, where the short-circuit diagnosis result includes a short-circuit discrimination result and a short-circuit level mark.

[0025] Specifically, fuse the virtual impedance spectrum and the monitored impedance spectrum. This requires first analyzing a large number of sample short-circuit data to determine the key feature index set in depth, and marking the sensitivity for each feature index in the set. The sensitivity reflects its importance for short-circuit discrimination. Based on the key feature index set, extract feature values from the virtual and monitored impedance spectra respectively to generate the first and second impedance feature sets, calculate the difference between the two to form an impedance feature set for short-circuit discrimination. Then carry out short-circuit discrimination work, obtain the historical impedance feature sequence of the battery, combine it with the current impedance feature set, and use the trend analysis method to calculate the impedance feature change trend to obtain the trend development rate. Determine the preset short-circuit discrimination constraint through statistical analysis methods, compare the trend development rate with this constraint. If the trend development rate is greater than or equal to the constraint, it is determined that the battery has a short circuit, and the short-circuit level mark is the ratio of the trend development rate to the short-circuit discrimination constraint. In addition, a short-circuit diagnosis model based on machine learning is used. This model has a virtual impedance spectrum correction channel and a difference analysis mapping channel. Obtain the cycle log of the target battery from the battery management terminal, input the virtual impedance spectrum and the cycle log into the virtual impedance spectrum correction channel to optimize and obtain the reference impedance spectrum. Input the monitored impedance spectrum and the reference impedance spectrum into the difference analysis mapping channel to obtain the reference impedance feature set, compare it with the previous impedance feature set to obtain the short-circuit discrimination result. If it is determined that there is a short circuit, map the reference impedance feature set to the short-circuit level mark, and finally comprehensively and accurately obtain the short-circuit diagnosis result, providing strong support for the safety management of lithium batteries.

[0026] In a possible implementation manner, step S100 further includes: Step S110: Determine the temperature range and the SOC range, and define a temperature-SOC combination point set according to the principle of orthogonal experimental design.

[0027] Step S120: Perform a preset number of electrochemical impedance spectrum tests on the target battery at each combination point, and fuse the test results to obtain standard impedance spectrum data.

[0028] Step S130: Classify and store the obtained standard impedance spectrum data according to the temperature-SOC combination to construct a standard impedance spectrum library.

[0029] Specifically, the actual application scenarios and performance characteristics of the lithium battery need to be comprehensively considered to determine the temperature range and SOC (state of charge) range required for the research. For example, if the lithium battery is mainly used in electric vehicles, considering the vehicle's operation at different ambient temperatures and the change of SOC during the battery charging and discharging process, the temperature range may be set from -30°C to 50°C, and the SOC range may be set from 5% to 95%. After determining the range, the principle of orthogonal experimental design is used. This principle can obtain relatively comprehensive and representative data with fewer experimental times. Through the orthogonal table, a series of typical temperature values and SOC values are selected for combination within the established temperature and SOC ranges. For example, select temperature points of -30°C, 0°C, 25°C, and 50°C from the temperature range, and select SOC points of 5%, 30%, 60%, and 95% from the SOC range. Combine these temperature points and SOC points with each other to form a temperature-SOC combination point set. These combination points cover various states that the battery may encounter, laying a foundation for obtaining comprehensive and valuable impedance spectrum data in the follow-up.

[0030] Perform a preset number of electrochemical impedance spectroscopy tests on the target battery at each combination point and fuse the test results to obtain the standard impedance spectrum data. For each determined temperature-SOC combination point, use an electrochemical test device to perform a preset number of (such as 5 times) electrochemical impedance spectroscopy tests on the target battery. Each time during the test, by applying an alternating current excitation signal with different frequencies to the battery, measure the voltage response of the battery, and then obtain the corresponding impedance data. Since there may be certain errors during the test process, in order to obtain more accurate and reliable data, fuse the multiple test results at the same combination point. The average value method can be used to synthesize the data of multiple tests to obtain the standard impedance spectrum data that can more accurately reflect the true impedance characteristics of the battery at this combination point.

[0031] Classify and store the obtained standard impedance spectrum data according to the temperature-SOC combination to construct a standard impedance spectrum library. Classify and organize the standard impedance spectrum data obtained by fusion processing at each combination point according to its corresponding temperature-SOC combination, and establish a database. Using temperature and SOC as indexes, accurately store the standard impedance spectrum data of each combination point in the corresponding position to construct a complete standard impedance spectrum library. This standard impedance spectrum library contains the standard impedance spectra of the target battery under different temperature and SOC combinations, providing an important reference benchmark for the subsequent real-time monitoring and accurate diagnosis of the internal short-circuit condition of the target battery.

[0032] In a possible implementation manner, step S200 further includes: Step S210: Based on the statistical analysis method, conduct statistical analysis in the standard impedance spectrum library to determine the confidence frequency interval.

[0033] Step S220: According to the confidence frequency interval, apply an alternating current excitation signal covering low frequency to high frequency to the target battery electrode, measure the voltage response of the target battery, and obtain the complex impedance data of the battery.

[0034] Step S230: Establish the monitored impedance spectrum according to the complex impedance data.

[0035] Specifically, since a large amount of impedance spectrum data under different temperature-SOC combinations is stored in the standard impedance spectrum library, in order to make the subsequent tests on the target battery more accurate and reliable, it is necessary to use the statistical analysis method to process these data and then determine the confidence frequency interval. First, extract the impedance data at each frequency from the standard impedance spectrum library, and use statistical principles to calculate statistical quantities such as the mean, variance, and frequency distribution of these data. Taking the calculation of the mean as an example, by summing all the impedance data at the same frequency and then dividing by the number of data, the average level of the impedance at that frequency is obtained. Then, according to the preset confidence level, such as the commonly used 95% confidence level, combined with the distribution of the data, use the normal distribution or other appropriate distribution models to determine the confidence frequency interval. If the data approximately follows the normal distribution, according to the characteristics of the normal distribution, use the mean and standard deviation to calculate the frequency boundary values at a specific confidence level, so as to determine a frequency range, which is the confidence frequency interval. The data within this interval can better represent the impedance characteristics of the battery in the normal state, providing a scientific and reasonable frequency reference range for applying the alternating current excitation signal to the target battery later, and helping to improve the accuracy of monitoring the state of the target battery.

[0036] According to the determined confidence frequency interval, apply an alternating current excitation signal covering low frequency to high frequency to the target battery electrode, measure the voltage response of the target battery, and obtain the complex impedance data of the battery. Using an electrochemical test device, apply an alternating current excitation signal to the target battery electrode according to the established confidence frequency interval, and the signal frequency gradually changes from low frequency to high frequency, such as from 0.01 Hz to 10 kHz. While applying the alternating current excitation signal, use a high-precision voltage measuring instrument to synchronously measure the voltage response of the target battery. Since the battery will exhibit different electrochemical characteristics under alternating current excitation at different frequencies, its voltage response will also vary. According to Ohm's law and complex number operation rules, combine the measured voltage response data with the known parameters of the alternating current excitation signal to calculate the complex impedance data of the battery at different frequencies. These complex impedance data contain comprehensive information such as the battery resistance, capacitance, and inductance, reflecting the electrochemical state inside the battery.

[0037] Based on the acquired complex impedance data, a monitoring impedance spectrum is established. The complex impedance data calculated at different frequencies are sorted and analyzed, and arranged in ascending order of frequency. With frequency as the abscissa and the real and imaginary parts of the complex impedance as the ordinates respectively, these data points are plotted in a coordinate system, and through curve fitting, a curve that can accurately reflect the current state of the target battery is constructed, and this curve is the monitoring impedance spectrum. The monitoring impedance spectrum visually shows the variation of the impedance characteristics of the target battery with frequency under the current test conditions, providing crucial data basis for subsequent judgment of whether there are abnormal conditions such as internal short circuits in the battery.

[0038] In a possible implementation manner, step S300 further includes: Step S310: Extract the SOC marker of the monitoring impedance spectrum.

[0039] Step S320: Based on the SOC marker, traverse the standard impedance spectrum library to construct a standard impedance spectrum sub-library.

[0040] Step S330: Construct an impedance spectrum coordinate system, map the standard impedance spectrum sub-library to the impedance spectrum coordinate system, and obtain a plurality of spectral intervals.

[0041] Step S340: Map the monitoring impedance spectrum to the impedance spectrum coordinate system, and according to the spectral interval it belongs to, extract the upper limit of the interval as the upper adjacent impedance spectrum, and extract the lower limit of the interval as the lower adjacent impedance spectrum.

[0042] Specifically, since the monitoring impedance spectrum is a reflection of the current state of the target battery, and the SOC (state of charge) marker contains the current battery charge information, which is crucial for subsequent analysis. First, according to the data storage format of the monitoring impedance spectrum and relevant technical specifications, determine the storage location or coding method of the SOC marker in the data. If the monitoring impedance spectrum is stored in a specific file format, such as CSV format, there are clear column headers indicating the column where the SOC data is located; if it is in binary data format, the SOC marker data segment needs to be located according to the pre-set parsing rules. After finding the SOC marker data, use a data reading algorithm to accurately extract the stored value. If the SOC marker is stored in a certain coding form, a decoding operation is also required to convert it into an intuitive state of charge value that can be used for subsequent processing. This extracted SOC marker represents the state of charge of the target battery when the monitoring impedance spectrum is obtained, and is the key basis for subsequent construction of the standard impedance spectrum sub-library and further analysis of the relationship between the monitoring impedance spectrum and the standard impedance spectrum. It can ensure that subsequent analysis focuses on the standard data matching the current state of charge of the battery, improving the accuracy and pertinence of the analysis.

[0043] Based on the extracted SOC markers, traverse the standard impedance spectrum library to construct a standard impedance spectrum sub-library. The standard impedance spectrum library contains a large amount of standard impedance spectrum data under different temperature-SOC combinations. Using the SOC marker as an index, search through the standard impedance spectrum library one by one to find the data corresponding to this SOC marker. Filter out all the standard impedance spectrum data that match this SOC marker and re-integrate them to construct a dedicated standard impedance spectrum sub-library, providing a more targeted data set for subsequent precise analysis.

[0044] Construct an impedance spectrum coordinate system and map the standard impedance spectrum sub-library to this coordinate system to obtain multiple spectral intervals. To analyze the impedance spectrum data more intuitively and effectively, construct a suitable impedance spectrum coordinate system. The coordinate axes of this coordinate system are set according to the key characteristic parameters of the impedance spectrum. For example, the frequency is used as the horizontal axis, and the real and imaginary parts of the impedance are used as the vertical axes. Map all the data in the constructed standard impedance spectrum sub-library to this coordinate system according to their characteristics in dimensions such as frequency, real part of impedance, and imaginary part of impedance. According to the distribution of the data in the coordinate system, use the clustering analysis method to divide the entire data region into multiple different spectral intervals. These spectral intervals are an effective way to classify the data of the standard impedance spectrum sub-library, helping to analyze the position and characteristics of the monitored impedance spectrum within the standard spectrum range more meticulously.

[0045] Map the monitored impedance spectrum to the impedance spectrum coordinate system. According to the spectral interval it belongs to, extract the upper limit of the interval as the upper adjacent impedance spectrum and extract the lower limit of the interval as the lower adjacent impedance spectrum. Map the previously obtained monitored impedance spectrum to the constructed impedance spectrum coordinate system according to the same rules. By comparing the position of the monitored impedance spectrum in the coordinate system with the ranges of each spectral interval, determine the specific spectral interval it belongs to. After finding this spectral interval, extract the upper limit part from the boundary data of the interval. This part of the data constitutes the upper adjacent impedance spectrum; extract the lower limit part to form the lower adjacent impedance spectrum. The upper adjacent impedance spectrum and the lower adjacent impedance spectrum respectively represent the two boundary states of the spectral interval where the monitored impedance spectrum is located, providing a key reference basis for accurately calculating the offset of the monitored impedance spectrum and judging the battery state later, and helping to diagnose more accurately whether there are problems such as internal short circuits in lithium batteries.

[0046] In a possible implementation manner, step S400 further includes: Step S410: Select representative key frequency points in the impedance spectrum as comparison benchmarks.

[0047] Step S420: Compare the impedance values of the monitored impedance spectrum, the upper adjacent impedance spectrum, and the lower adjacent impedance spectrum at each key frequency point, and calculate the offset difference at each key point.

[0048] Step S430: Weight the differences at each key point, calculate the overall offset, and output the upper offset and the lower offset.

[0049] Step S440: Normalize the upper offset and the lower offset to determine the relative position of the monitored impedance spectrum within its spectral range.

[0050] Specifically, select representative key frequency points in the impedance spectrum as the comparison reference. Since the impedance spectrum contains numerous frequency point data, not all frequency points have the same sensitivity to analyzing changes in the internal state of the battery. Through in-depth research on the electrochemical characteristics of lithium batteries, combined with a large amount of experimental data and theoretical analysis, those frequency points that can sensitively reflect changes in the internal state of the battery are selected as key frequency points. For example, in the impedance spectrum of a lithium battery, some frequency points in the low-frequency band are sensitive to the charge transfer process inside the battery, and some frequency points in the high-frequency band are closely related to the ohmic resistance characteristics of the battery. Selecting these representative key frequency points lays the foundation for accurately comparing the differences between different impedance spectra subsequently.

[0051] Compare the impedance values of the monitored impedance spectrum with those of the upper adjacent impedance spectrum and the lower adjacent impedance spectrum at each key frequency point, and calculate the offset difference at each key point. Compare the impedance values of the monitored impedance spectrum at each selected key frequency point with the impedance values of the upper adjacent impedance spectrum and the lower adjacent impedance spectrum at the same key frequency point respectively. For each key frequency point, calculate the impedance difference between the monitored impedance spectrum and the upper adjacent impedance spectrum, and the impedance difference between the monitored impedance spectrum and the lower adjacent impedance spectrum through subtraction operations. These differences can intuitively reflect the deviation degree of the monitored impedance spectrum from the upper and lower boundaries of the standard impedance spectrum at each key frequency point, providing detailed data support for evaluating the overall offset of the monitored impedance spectrum subsequently.

[0052] Weight the differences at each key point, calculate the overall offset, and output the upper offset and the lower offset. Considering that different key frequency points have different degrees of influence on the overall state of the battery, assign corresponding weights to the differences at each key point. The determination of the weights is based on the understanding of the physical and chemical processes inside the battery and the analysis of a large amount of experimental data. Multiply the differences at each key frequency point by the corresponding weights, and then sum up all the product results to obtain the overall offset of the monitored impedance spectrum relative to the upper adjacent impedance spectrum, that is, the upper offset, and the overall offset relative to the lower adjacent impedance spectrum, that is, the lower offset. These two offsets comprehensively reflect the deviation of the monitored impedance spectrum from the upper and lower boundaries of the standard impedance spectrum at all key frequency points, and can more comprehensively measure the difference between the monitored impedance spectrum and the standard impedance spectrum.

[0053] Normalize the upper offset and the lower offset to determine the relative position of the monitored impedance spectrum within its spectral range. Since the numerical values of the upper offset and the lower offset may be affected by factors such as measurement units and data magnitudes, in order to accurately compare them on a unified scale, it is necessary to normalize these two offsets. Through the normalization algorithm, the upper offset and the lower offset are transformed into values between 0 and 1. Based on the normalized upper offset and lower offset, the relative position of the monitored impedance spectrum within its spectral range can be determined. For example, if the normalized upper offset is 0.3 and the lower offset is 0.2, this indicates that the monitored impedance spectrum is closer to the adjacent lower impedance spectrum and is relatively close to the lower limit within the spectral range. The determination of this relative position is of great significance for further analyzing the state of the battery and judging whether there are abnormal conditions such as internal short circuits in the subsequent process, and can provide a key basis for the health assessment and fault diagnosis of lithium batteries.

[0054] In a possible implementation manner, step S500 further includes: Step S510: Calculate the coefficient of variation between the virtual impedance spectrum and the monitored impedance spectrum.

[0055] Step S520: If the coefficient of variation meets the preset threshold, output the virtual impedance spectrum.

[0056] Step S530: If the coefficient of variation does not meet the preset threshold, perform iterative update of the virtual impedance spectrum based on the over-limit direction of the coefficient of variation, and recalculate the corresponding coefficient of variation until the preset threshold is met.

[0057] Specifically, calculate the coefficient of variation between the virtual impedance spectrum and the monitored impedance spectrum. The coefficient of variation is a relative index for measuring the degree of data dispersion and is used here to evaluate the difference between the virtual impedance spectrum and the monitored impedance spectrum. Respectively obtain the impedance value data sequences of the virtual impedance spectrum and the monitored impedance spectrum at each frequency point. According to the calculation formula of the coefficient of variation, first calculate the standard deviation of the two groups of data, and then divide it by their respective means to obtain the coefficient of variation between the virtual impedance spectrum and the monitored impedance spectrum. This coefficient of variation reflects the relative degree of dispersion between the two. The smaller the value, the higher the similarity between the virtual impedance spectrum and the monitored impedance spectrum, and the more representative the virtual impedance spectrum is of the actual state of the current target battery.

[0058] If the coefficient of variation meets the preset threshold, output the virtual impedance spectrum. The preset threshold is determined based on a large number of experiments and data analyses and is used to judge whether the virtual impedance spectrum is accurate enough. When the calculated coefficient of variation is less than or equal to the preset threshold, it means that the difference between the virtual impedance spectrum and the monitored impedance spectrum is within an acceptable range, and the virtual impedance spectrum can better reflect the actual situation of the target battery. At this time, the virtual impedance spectrum can be directly output as a reliable basis for subsequent feature extraction and short circuit discrimination.

[0059] If the coefficient of variation does not meet the preset threshold, the virtual impedance spectrum is iteratively updated based on the over-limit direction of the coefficient of variation, and the corresponding coefficient of variation is recalculated until the preset threshold is met. If the coefficient of variation is greater than the preset threshold, it means that there is a large difference between the virtual impedance spectrum and the monitored impedance spectrum, and the virtual impedance spectrum needs to be optimized. According to the over-limit direction of the coefficient of variation, that is, whether the virtual impedance spectrum is too discrete or too small relative to the monitored impedance spectrum, corresponding adjustment strategies are adopted. For example, if the virtual impedance spectrum is too discrete, the parameters for interpolating and calculating the virtual impedance spectrum need to be fine-tuned to make it closer to the monitored impedance spectrum. After adjustment, the virtual impedance spectrum is recalculated, and the coefficient of variation between the new virtual impedance spectrum and the monitored impedance spectrum is calculated again. This iterative update and calculation of the coefficient of variation are continuously repeated until the coefficient of variation meets the preset threshold. In this way, it is ensured that the finally obtained virtual impedance spectrum can accurately reflect the state of the target battery, providing strong support for the subsequent accurate diagnosis of internal short circuits in lithium batteries.

[0060] In a possible implementation manner, step S600 further includes: Step S610: Analyze and determine a key feature index set through sample short-circuit data, where each feature index included in the key feature index set is marked with a sensitivity.

[0061] Step S620: Based on the key feature index set, extract eigenvalue features from the virtual impedance spectrum and the monitored impedance spectrum respectively to obtain a first impedance feature set and a second impedance feature set.

[0062] Step S630: Calculate the impedance feature difference information between the first impedance feature set and the second impedance feature set, and output it as the impedance feature set.

[0063] Specifically, in order to accurately diagnose the internal short - circuit situation of lithium - ion batteries, it is necessary to start with a large number of sample short - circuit data. These sample short - circuit data cover various conditions of lithium - ion batteries under different operating conditions and different short - circuit degrees. By applying data - mining, statistical analysis methods, and electrochemical expertise, a comprehensive and in - depth analysis of this rich data is carried out. From numerous possible characteristic indicators, those that are closely related to the short - circuit situation of lithium - ion batteries and can significantly reflect short - circuit characteristics are selected to determine the key characteristic indicator set. For example, the impedance change trend within a specific frequency range, the real and imaginary parts of the impedance at certain key frequency points, etc. may be included. At the same time, in order to reflect the differences in the importance and sensitivity of each characteristic indicator in reflecting the short - circuit problem, a sensitivity is marked for each characteristic indicator in the key characteristic indicator set. The determination of sensitivity is based on observing the response of the indicator to changes in the short - circuit degree in a large amount of sample data. If an indicator shows an obvious change when the short - circuit degree changes slightly, it indicates that it is more sensitive to the short - circuit situation and has a higher sensitivity; on the contrary, if the change of the indicator is not obvious or lags, its sensitivity is lower. After marking the sensitivity in this way, these key characteristic indicators can be analyzed and utilized more targeted in the subsequent diagnosis process, improving the accuracy and reliability of short - circuit diagnosis.

[0064] Guided by the determined key characteristic indicator set, eigenvalue extraction is performed on the virtual impedance spectrum and the monitored impedance spectrum respectively to obtain the first impedance characteristic set and the second impedance characteristic set. For each indicator in the key characteristic indicator set, the corresponding eigenvalue is found on the virtual impedance spectrum and the monitored impedance spectrum. For example, if the key characteristic indicator set includes the real part of the impedance at frequency f1, the real - part value of the impedance at frequency f1 is extracted from the virtual impedance spectrum and the monitored impedance spectrum. In this way, the eigenvalues corresponding to all indicators in the key characteristic indicator set are extracted in turn, forming the first impedance characteristic set (from the virtual impedance spectrum) and the second impedance characteristic set (from the monitored impedance spectrum) respectively. These two characteristic sets represent the battery impedance characteristic information obtained based on virtual and actual monitoring respectively.

[0065] The first impedance feature set and the second impedance feature set are made to correspond one by one in the same order of feature indicators. For each pair of corresponding eigenvalue, the difference between them is quantified. For example, the absolute value of their difference is calculated to intuitively reflect the degree of deviation in value; or the ratio of the two is calculated to measure the relative change. If a certain feature indicator is the real part of the impedance at a specific frequency, the eigenvalues of the real part of the impedance at this frequency in the first and second impedance feature sets are respectively extracted, and the absolute value of the difference between the two is calculated as the difference information of this feature indicator. Such calculations are performed on all corresponding eigenvalues, and the series of difference information obtained is integrated in the order of feature indicators to form a new impedance feature set. This new impedance feature set prominently shows the differences in the impedance characteristics of the battery between the virtual state and the actual monitoring state, can more effectively reflect the actual condition of the battery, provides core data support for subsequent short - circuit discrimination based on this impedance feature set, and helps to improve the accuracy of internal short - circuit diagnosis of lithium batteries.

[0066] In a possible implementation manner, step S600 further includes: Step S640: Obtain a historical impedance feature sequence, and in combination with the impedance feature set and the historical impedance feature sequence, use a trend analysis method to calculate the change trend of the impedance feature and obtain a trend development rate.

[0067] Step S650: Determine whether the trend development rate meets a preset short - circuit discrimination constraint, where the short - circuit discrimination constraint is obtained based on a statistical analysis method.

[0068] Step S660: If the trend development rate is greater than or equal to the short - circuit discrimination constraint, the short - circuit discrimination result is that there is a short - circuit, and the short - circuit level mark is equal to the ratio of the trend development rate to the short - circuit discrimination constraint.

[0069] Specifically, in order to more accurately determine whether an internal short circuit has occurred in a lithium battery, it is necessary to obtain the historical impedance characteristic sequence of the battery, which is a record set of impedance characteristics at different time points during the past use of the battery and contains information on the impedance changes of the battery under various operating conditions. Then, the currently obtained impedance characteristic set is combined with the historical impedance characteristic sequence. In practical applications, there are differences in the ohmic resistances of the batteries in a battery pack, and an internal short circuit will increase the ohmic resistance of the battery. However, relying solely on setting a fixed threshold to judge a short circuit is not accurate. For a battery with a relatively high ohmic resistance itself, false alarms may occur when it is not short-circuited according to the conventional threshold setting; while for a battery with a relatively low ohmic resistance, the same threshold may lead to missed alarms when a short circuit occurs. Therefore, a trend analysis method is used to deeply process these data. Common trend analysis methods such as linear regression are used to find a straight line that best reflects the data change trend by fitting the data points of the historical impedance characteristic sequence and the current impedance characteristic set. The slope of this straight line is the trend development rate. The trend development rate intuitively reflects the speed and direction of the change of the battery impedance characteristics over time. By analyzing this change trend rather than simply relying on a fixed threshold, it is possible to more reliably determine whether an internal short circuit has occurred during the cyclic use of the battery, providing a key basis for subsequent short circuit diagnosis.

[0070] Determine whether the trend development rate meets the preset short circuit discrimination constraint, where the short circuit discrimination constraint is obtained based on statistical analysis methods. Based on the historical data and experimental results of a large number of batteries, statistical analysis methods such as hypothesis testing and probability distribution analysis are used to determine a reasonable short circuit discrimination constraint value. This constraint value is a measurement standard used to distinguish the boundary between the normal operation of the battery and the occurrence of an internal short circuit. The calculated trend development rate is compared with the preset short circuit discrimination constraint to determine whether the trend development rate exceeds the normal range.

[0071] If the trend development rate is greater than or equal to the short circuit discrimination constraint, it indicates that the battery state is abnormal, the short circuit discrimination result is that there is a short circuit, and the short circuit level mark is equal to the ratio of the trend development rate to the short circuit discrimination constraint. This means that the greater the trend development rate and the higher the degree of exceeding the short circuit discrimination constraint, the higher the severity of the battery short circuit and the higher the short circuit level. For example, if the trend development rate is twice the short circuit discrimination constraint, then the short circuit level mark is 2; if the trend development rate is only slightly greater than the short circuit discrimination constraint, the short circuit level mark is close to 1, indicating that the short circuit degree is relatively light. In this way, not only can it accurately determine whether there is an internal short circuit in the battery, but also the severity of the short circuit can be quantitatively marked, providing key information for battery maintenance and management, which helps to take corresponding measures in a timely manner to ensure the safe and stable operation of the lithium battery.

[0072] In a possible implementation manner, step S600 further includes: Step S670: Establish a short - circuit diagnosis model based on machine learning. The short - circuit diagnosis model includes a virtual impedance spectrum correction channel and a differential analysis mapping channel. Interact with the battery management terminal to obtain the cycle log of the target battery. Using the virtual impedance spectrum and the cycle log as inputs, perform impedance spectrum correction through the virtual impedance spectrum correction channel to obtain a reference impedance spectrum. The differential analysis mapping channel takes the monitored impedance spectrum and the reference impedance spectrum as inputs for differential analysis to obtain a reference impedance feature set, and compares the reference impedance feature set with the impedance feature set to obtain the short - circuit discrimination result. If the short - circuit discrimination result indicates a short circuit, map the reference impedance feature set to the short - circuit level marker.

[0073] Specifically, to more accurately diagnose the internal short - circuit condition of a lithium - ion battery, a short - circuit diagnosis model based on machine learning needs to be established. This model has two key channels: a virtual impedance spectrum correction channel and a differential analysis mapping channel. The virtual impedance spectrum correction channel is used to optimize the previously calculated virtual impedance spectrum. It uses machine - learning algorithms to learn the complex relationship between the virtual impedance spectrum and the actual state of the battery, and then corrects the virtual impedance spectrum so that it can more accurately reflect the current real state of the battery. For example, a neural - network algorithm can be used. Taking the virtual impedance spectrum data as input, through training the network, the network can learn the characteristics that the virtual impedance spectrum should have under different working conditions, and thus output a more accurate virtual impedance spectrum. The differential analysis mapping channel focuses on comparative analysis. It takes the monitored impedance spectrum and the corrected reference impedance spectrum (output by the virtual impedance spectrum correction channel) as inputs, and uses a neural - network algorithm to deeply analyze the differences between them, and extracts a reference impedance feature set from them. By comparing the reference impedance feature set with the impedance feature set obtained by fusing the virtual and monitored impedance spectra before, this channel can determine whether the battery has a short circuit, providing key decision - making support for the entire short - circuit diagnosis.

[0074] The battery management terminal, as the core control unit of the battery system, stores detailed operation data of the target battery. Among them, the cycle log records the key information during multiple charge - discharge cycles of the battery. To obtain this data, the diagnosis system will establish a communication connection with the battery management terminal and use a specific communication protocol, such as the Controller Area Network (CAN) protocol or other protocols suitable for the battery management system, to ensure the accuracy and stability of data transmission. By sending a request command, the diagnosis system requests the cycle log of the target battery from the battery management terminal. After receiving the request, the battery management terminal retrieves and extracts the corresponding cycle log data from its internal storage unit. These data cover rich information such as the start and end times of each charge - discharge of the battery, the magnitude of the charge - discharge current, the voltage change situation, the battery temperature, and the change process of the State of Charge (SOC).

[0075] After obtaining the virtual impedance spectrum and the cycle log, they are transmitted as input data to the virtual impedance spectrum correction channel. This channel uses a pre-trained machine learning model internally, such as a neural network or other algorithm models suitable for processing such data, to adjust the input virtual impedance spectrum. The model will optimize and correct the virtual impedance spectrum based on information such as the number of charge and discharge cycles of the battery recorded in the cycle log, the current and voltage changes during each charge and discharge, and the temperature conditions, combined with the characteristics of the virtual impedance spectrum itself, and finally output a reference impedance spectrum that can better reflect the current actual state of the battery.

[0076] The differential analysis mapping channel starts to work, taking the monitored impedance spectrum and the reference impedance spectrum as inputs. Through the algorithm of the neural network, this channel conducts a detailed differential analysis of these two impedance spectra, and extracts a set of reference impedance characteristics that can represent the current state characteristics of the battery from the differences between the two. This set of reference impedance characteristics contains the difference information between the actual monitored state of the battery and the corrected ideal state. Subsequently, the set of reference impedance characteristics is compared with the set of impedance characteristics obtained by fusing the virtual impedance spectrum and the monitored impedance spectrum before. By comparing aspects such as the similarity degree and feature differences between the two, and using preset discrimination rules, the short-circuit discrimination result is obtained to determine whether there is an internal short-circuit situation in the battery.

[0077] When it is determined through the previous analysis process that the battery has a short circuit, it is necessary to further clarify the severity of the short circuit. The set of reference impedance characteristics is obtained by the differential analysis mapping channel for the monitored impedance spectrum and the reference impedance spectrum, and it contains the key information reflecting the difference between the current state and the normal state of the battery. According to the preset mapping rules, the numerical values, change trends, etc. of each characteristic index in the set of reference impedance characteristics are transformed. For example, if the impedance values at certain specific frequencies in the set of reference impedance characteristics deviate from the normal range more, or the change trends of certain characteristic indexes are more obvious, according to the established rules, the numerical value of the short-circuit level mark obtained by mapping is larger, indicating a more serious short-circuit situation; on the contrary, the smaller the numerical value of the short-circuit level mark, the relatively lighter the short-circuit degree. In this way, the abstract set of reference impedance characteristics is transformed into an intuitive short-circuit level mark, providing a clear and quantitative basis for subsequent decisions such as battery maintenance and replacement.

[0078] Embodiment 2, based on the same inventive concept as the method for diagnosing internal short circuit of a lithium battery based on impedance spectrum analysis in the foregoing embodiment, as Figure 2 shown, the present application provides a system for diagnosing internal short circuit of a lithium battery based on impedance spectrum analysis. The system in the embodiment of the present application and the method embodiment are based on the same inventive concept. Among them, the system includes: A standard impedance spectrum library construction module 10, configured to construct a standard impedance spectrum library based on the identification identity information of the target battery, wherein the standard impedance spectrum library includes standard impedance spectra under each temperature-SOC combination.

[0079] The monitoring impedance spectrum acquisition module 20 is configured to perform an electrochemical impedance spectrum test on a target battery in combination with the standard impedance spectrum library to obtain a monitoring impedance spectrum.

[0080] The monitoring impedance spectrum mapping module 30 is configured to map the monitoring impedance spectrum to the standard impedance spectrum library, determine the spectral interval of the monitoring impedance spectrum, and correspondingly obtain the upper adjacent impedance spectrum and the lower adjacent impedance spectrum.

[0081] The offset calculation module 40 is configured to calculate the offsets of the monitoring impedance spectrum relative to the upper adjacent impedance spectrum and the lower adjacent impedance spectrum, and locate the relative spectral position according to the offsets.

[0082] The virtual impedance spectrum acquisition module 50 is configured to calculate and obtain a virtual impedance spectrum based on an interpolation method in combination with the relative spectral position, the upper adjacent impedance spectrum, and the lower adjacent impedance spectrum.

[0083] The short - circuit diagnosis result acquisition module 60 is configured to fuse the virtual impedance spectrum and the monitoring impedance spectrum, extract an impedance feature set, and perform short - circuit discrimination according to the impedance feature set to obtain a short - circuit diagnosis result, where the short - circuit diagnosis result includes a short - circuit discrimination result and a short - circuit level mark.

[0084] Furthermore, the system is also used to implement the following functions: Determine the temperature range and the SOC range, and define a temperature - SOC combination point set according to the principle of orthogonal experimental design; perform a preset number of electrochemical impedance spectrum tests on the target battery at each combination point, and fuse the test results to obtain standard impedance spectrum data; classify and store the obtained standard impedance spectrum data according to the temperature - SOC combination to construct a standard impedance spectrum library.

[0085] Furthermore, the system is also used to implement the following functions: Based on a statistical analysis method, perform statistical analysis in the standard impedance spectrum library to determine a confidence frequency interval; according to the confidence frequency interval, apply an alternating current excitation signal covering low frequency to high frequency to the electrodes of the target battery, and measure the voltage response of the target battery to obtain the complex impedance data of the battery; establish the monitoring impedance spectrum according to the complex impedance data.

[0086] Furthermore, the system is also used to implement the following functions: Extract the SOC marker of the monitored impedance spectrum; based on the SOC marker, traverse the standard impedance spectrum library to construct a standard impedance spectrum sub-library; construct an impedance spectrum coordinate system, and map the standard impedance spectrum sub-library to the impedance spectrum coordinate system to obtain a complex number of spectral intervals; map the monitored impedance spectrum to the impedance spectrum coordinate system, and according to the belonging spectral interval, extract the upper limit of the interval as the upper adjacent impedance spectrum, and extract the lower limit of the interval as the lower adjacent impedance spectrum.

[0087] Further, the system is also used to implement the following functions: Select representative key frequency points in the impedance spectrum as comparison benchmarks; compare the impedance values of the monitored impedance spectrum with the upper adjacent impedance spectrum and the lower adjacent impedance spectrum at each key frequency point, calculate the offset difference of each key point; weight the differences of each key point, calculate the overall offset, and output the upper offset and the lower offset; normalize the upper offset and the lower offset to determine the relative position of the monitored impedance spectrum in the belonging spectral interval.

[0088] Further, the system is also used to implement the following functions: Calculate the coefficient of variation of the virtual impedance spectrum and the monitored impedance spectrum; if the coefficient of variation meets the preset threshold, output the virtual impedance spectrum; if the coefficient of variation does not meet the preset threshold, perform iterative update of the virtual impedance spectrum based on the overrun direction of the coefficient of variation, and recalculate the corresponding coefficient of variation until it meets the preset threshold.

[0089] Further, the system is also used to implement the following functions: Analyze and determine a key feature index set through sample short-circuit data, where each feature index included in the key feature index set is marked with sensitivity; based on the key feature index set, perform eigenvalue extraction on the virtual impedance spectrum and the monitored impedance spectrum respectively to obtain a first impedance feature set and a second impedance feature set; calculate the impedance feature difference information between the first impedance feature set and the second impedance feature set, and output it as the impedance feature set.

[0090] Further, the system is also used to implement the following functions: Obtain a historical impedance feature sequence, and combine the impedance feature set with the historical impedance feature sequence, and use a trend analysis method to calculate the change trend of the impedance feature to obtain a trend development rate; determine whether the trend development rate meets a preset short-circuit discrimination constraint, where the short-circuit discrimination constraint is obtained based on a statistical analysis method; if the trend development rate is greater than or equal to the short-circuit discrimination constraint, the short-circuit discrimination result is that there is a short circuit, and the short-circuit level mark is equal to the ratio of the trend development rate to the short-circuit discrimination constraint.

[0091] Further, the system is also used to implement the following functions: Build a short-circuit diagnosis model based on machine learning, where the short-circuit diagnosis model includes a virtual impedance spectrum correction channel and a difference analysis mapping channel; an interactive battery management terminal obtains the cycle log of the target battery; using the virtual impedance spectrum and the cycle log as inputs, perform impedance spectrum correction through the virtual impedance spectrum correction channel to obtain a reference impedance spectrum; the difference analysis mapping channel takes the monitored impedance spectrum and the reference impedance spectrum as inputs for difference analysis to obtain a reference impedance feature set, and compares the reference impedance feature set with the impedance feature set to obtain the short-circuit discrimination result; if the short-circuit discrimination result is that there is a short circuit, map the reference impedance feature set as the short-circuit level mark.

[0092] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0094] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A lithium battery internal short circuit diagnosis method based on impedance spectrum analysis, characterized in that: The method comprises: Based on the identification information of the target battery, a standard impedance spectrum library is constructed, wherein the standard impedance spectrum library includes standard impedance spectra under various temperature-SOC combinations; In combination with the standard impedance spectrum library, an electrochemical impedance spectrum test is performed on the target battery to obtain a monitoring impedance spectrum; Mapping the monitoring impedance spectrum to the standard impedance spectrum library, identifying the spectrum interval of the monitoring impedance spectrum, and correspondingly obtaining the upper and lower impedance spectra; Calculating the offset of the monitored impedance spectrum relative to the upper impedance spectrum and the lower impedance spectrum, and locating the relative spectrum position according to the offset; Based on the interpolation method, the virtual impedance spectrum is calculated and obtained by combining the relative spectrum position, the upper impedance spectrum and the lower impedance spectrum; The virtual impedance spectrum and the monitoring impedance spectrum are integrated to extract an impedance feature set, and short circuit discrimination is performed according to the impedance feature set to obtain a short circuit diagnosis result, wherein the short circuit diagnosis result includes a short circuit discrimination result and a short circuit level mark.

2. The lithium battery internal short circuit diagnosis method based on impedance spectrum analysis according to claim 1, characterized in that: Based on the identification information of the target battery, a standard impedance spectrum library is constructed, wherein the standard impedance spectrum library includes standard impedance spectra under various temperature-SOC combinations, including: Determine the temperature range and SOC range, and define the temperature-SOC combination point set based on the orthogonal experimental design principle; Performing a preset number of electrochemical impedance spectroscopy tests on the target battery at each combination point, and integrating the test results to obtain standard impedance spectroscopy data; The acquired standard impedance spectrum data are classified and stored according to the temperature-SOC combination to construct a standard impedance spectrum library.

3. The lithium battery internal short circuit diagnosis method based on impedance spectrum analysis according to claim 2, characterized in that: Combined with the standard impedance spectrum library, an electrochemical impedance spectrum test is performed on the target battery to obtain a monitoring impedance spectrum, including: Based on the statistical analysis method, statistical analysis is performed in the standard impedance spectrum library to determine the confidence frequency interval; According to the confidence frequency interval, applying an AC excitation signal covering low frequency to high frequency to the target battery electrode, and measuring the voltage response of the target battery to obtain complex impedance data of the battery; The monitoring impedance spectrum is established according to the complex impedance data.

4. The lithium battery internal short circuit diagnosis method based on impedance spectrum analysis according to claim 3, characterized in that: Mapping the monitoring impedance spectrum to the standard impedance spectrum library, identifying the spectrum interval of the monitoring impedance spectrum, and correspondingly obtaining the upper and lower impedance spectra, including: Extracting the SOC marker of the monitoring impedance spectrum; Based on the SOC tag, traverse the standard impedance spectrum library to build a standard impedance spectrum sub-library; Constructing an impedance spectrum coordinate system, and mapping the standard impedance spectrum sub-library to the impedance spectrum coordinate system to obtain a plurality of spectrum intervals; The monitoring impedance spectrum is mapped to the impedance spectrum coordinate system, and according to the spectrum interval to which it belongs, the upper limit of the interval is extracted as the upper impedance spectrum, and the lower limit of the interval is extracted as the lower impedance spectrum.

5. The lithium battery internal short circuit diagnosis method based on impedance spectrum analysis according to claim 4, characterized in that: Calculating the offset of the monitoring impedance spectrum relative to the upper impedance spectrum and the lower impedance spectrum, and locating the relative spectrum position according to the offset, including: Select representative key frequency points in the impedance spectrum as comparison benchmarks; Comparing the impedance values ​​of the monitoring impedance spectrum with those of the upper and lower impedance spectra at each key frequency point, and calculating the offset difference of each key point; Weight the difference of each key point, calculate the overall offset, and output the upper offset and lower offset; The upper offset and the lower offset are normalized to determine the relative position of the monitoring impedance spectrum in the corresponding spectrum interval.

6. The lithium battery internal short circuit diagnosis method based on impedance spectrum analysis according to claim 5, characterized in that: Based on the interpolation method, the virtual impedance spectrum is calculated and obtained by combining the relative spectrum position, the upper impedance spectrum and the lower impedance spectrum, and further comprising: Calculating the coefficient of variation of the virtual impedance spectrum and the monitoring impedance spectrum; If the coefficient of variation meets a preset threshold, outputting the virtual impedance spectrum; If the coefficient of variation does not meet the preset threshold, the virtual impedance spectrum is iteratively updated based on the excess direction of the coefficient of variation, and the corresponding coefficient of variation is recalculated until the preset threshold is met.

7. The lithium battery internal short circuit diagnosis method based on impedance spectrum analysis according to claim 6, characterized in that: The virtual impedance spectrum and the monitoring impedance spectrum are integrated to extract an impedance feature set, and the method further includes: By analyzing the sample short-circuit data, a key characteristic indicator set is determined, wherein each characteristic indicator included in the key characteristic indicator set is marked with sensitivity; Based on the key characteristic indicator set, respectively extracting characteristic values ​​of the virtual impedance spectrum and the monitoring impedance spectrum to obtain a first impedance characteristic set and a second impedance characteristic set; Impedance feature difference information between the first impedance feature set and the second impedance feature set is calculated, and the result is output as the impedance feature set.

8. The lithium battery internal short circuit diagnosis method based on impedance spectrum analysis according to claim 7, characterized in that: Performing short circuit discrimination according to the impedance feature set to obtain a short circuit diagnosis result includes: Obtaining a historical impedance feature sequence, and combining the impedance feature set with the historical impedance feature sequence, using a trend analysis method to calculate a change trend of the impedance feature, and obtaining a trend development rate; Determining whether the trend development rate satisfies a preset short circuit determination constraint, wherein the short circuit determination constraint is obtained based on a statistical analysis method; If the trend development rate is greater than or equal to the short circuit determination constraint, the short circuit determination result is that a short circuit exists, and the short circuit level mark is equal to the ratio of the trend development rate to the short circuit determination constraint.

9. The lithium battery internal short circuit diagnosis method based on impedance spectrum analysis according to claim 7, characterized in that: The method further comprises: Establishing a short-circuit diagnosis model based on machine learning, wherein the short-circuit diagnosis model includes a virtual impedance spectrum correction channel and a difference analysis mapping channel; Interact with the battery management terminal to obtain the cycle log of the target battery; Taking the virtual impedance spectrum and the cycle log as input, performing impedance spectrum correction through the virtual impedance spectrum correction channel to obtain a reference impedance spectrum; The difference analysis mapping channel performs difference analysis with the monitoring impedance spectrum and the reference impedance spectrum as input, obtains a reference impedance feature set, and compares the reference impedance feature set with the impedance feature set to obtain the short circuit discrimination result; If the short circuit determination result is that a short circuit exists, the reference impedance feature set is mapped to the short circuit level mark.

10. A lithium battery internal short circuit diagnosis system based on impedance spectrum analysis, characterized in that: The system comprises: A standard impedance spectrum library construction module is used to construct a standard impedance spectrum library based on the identification information of the target battery, wherein the standard impedance spectrum library includes standard impedance spectra under various temperature-SOC combinations; A monitoring impedance spectrum acquisition module is used to perform an electrochemical impedance spectrum test on a target battery in combination with the standard impedance spectrum library to obtain a monitoring impedance spectrum; A monitoring impedance spectrum mapping module is used to map the monitoring impedance spectrum to the standard impedance spectrum library, identify the spectrum interval of the monitoring impedance spectrum, and obtain the upper impedance spectrum and the lower impedance spectrum accordingly; An offset calculation module, used for calculating the offset of the monitoring impedance spectrum relative to the upper impedance spectrum and the lower impedance spectrum, and locating the relative spectrum position according to the offset; A virtual impedance spectrum acquisition module, used for calculating and acquiring a virtual impedance spectrum based on an interpolation method and combining the relative spectrum position, the upper impedance spectrum and the lower impedance spectrum; The short-circuit diagnosis result acquisition module is used to fuse the virtual impedance spectrum with the monitoring impedance spectrum, extract the impedance feature set, and perform short-circuit discrimination based on the impedance feature set to obtain a short-circuit diagnosis result, wherein the short-circuit diagnosis result includes a short-circuit discrimination result and a short-circuit level mark.

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