Early internal short circuit identification and early warning method for lithium ion battery based on dynamic impedance

Abstract

The application discloses a lithium ion battery early internal short circuit identification and early warning method based on dynamic impedance, and belongs to the technical field of lithium ion battery safety detection and fault early warning. The method compares static electrochemical impedance spectra of healthy lithium ion batteries and internal short circuit fault samples, and screens internal short circuit sensitive characteristic frequency points. Dynamic excitation signals are applied to the lithium ion batteries to be tested based on the characteristic frequency points, voltage response signals and current response signals are synchronously collected, and dynamic impedance time sequences are obtained. Impedance amplitude deviation, impedance change rate, impedance mutation time and frequency point response mode are extracted from the time sequences, and early internal short circuit identification criteria are established. When dynamic impedance characteristics meet preset abnormal conditions, it is determined that the lithium ion batteries to be tested have early internal short circuit risks and early warning results are output. The application can identify internal short circuit risks before obvious voltage or temperature abnormalities occur, and is suitable for lithium ion battery online safety monitoring.

Description

Technical Field

[0001] This invention belongs to the field of lithium-ion battery safety detection and fault early warning technology, specifically involving a method for early internal short circuit identification and early warning of lithium-ion batteries based on dynamic impedance. Background Technology

[0002] Lithium-ion batteries possess advantages such as high energy density, long cycle life, and fast response speed, and are widely used in grid-side energy storage, electric vehicles, portable devices, and backup power supplies. As battery systems continue to expand in scale, battery safety issues are becoming increasingly prominent. Internal short circuits are a significant cause of battery thermal runaway, potentially resulting from factors such as metallic foreign objects, separator damage, lithium dendrite puncture, localized mechanical damage, or manufacturing defects. Early signs of internal short circuits typically include the formation of localized conductive channels, enhanced internal self-discharge, and changes in localized electrochemical states; however, external voltage and temperature signals often show little change, making early detection difficult.

[0003] Existing methods for identifying internal short circuits in batteries largely rely on externally measurable signals such as abnormal voltage, increased temperature, capacity decay, or consistency deviations. However, voltage signals typically only show a significant decrease after the internal short circuit has developed to a certain extent, and temperature signals are also affected by heat transfer paths, measurement locations, and environmental conditions, exhibiting a lag. Therefore, relying solely on voltage or temperature signals is insufficient for timely detection of early internal short circuit risks.

[0004] Electrochemical impedance spectroscopy (EIS) can reflect information such as ohmic impedance, charge transfer, interfacial reactions, and diffusion processes within a battery, and is highly sensitive to changes in the battery's internal state. While static EIS can characterize impedance responses at different frequency bands, it requires a long testing time and is not suitable for direct online rapid detection. Therefore, it is necessary to combine the characteristics of static EIS and dynamic impedance detection. First, static EIS can be used to screen for characteristic frequency points sensitive to internal short circuits, and then these selected frequency points can be used for online dynamic impedance detection, thereby improving the early identification and warning capabilities for internal short circuits. Summary of the Invention

[0005] The purpose of this invention is to provide an early internal short circuit identification and warning method for lithium-ion batteries based on dynamic impedance, so as to solve the problems of existing lithium-ion battery internal short circuit identification methods that rely on external signals such as voltage and temperature, resulting in response lag and insufficient early identification capability.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for early internal short circuit identification and warning of lithium-ion batteries based on dynamic impedance includes the following steps: S1: Perform static electrochemical impedance spectroscopy (SES) on healthy lithium-ion batteries to obtain impedance spectral data under healthy conditions and establish a healthy battery impedance baseline. The healthy battery impedance baseline may include static electrochemical impedance spectral data of healthy lithium-ion batteries under different states of charge, different temperatures, different cycling stages, or different resting conditions.

[0007] S2: Perform static electrochemical impedance spectroscopy (SES) tests on the internal short-circuit fault samples and compare the impedance spectra of the internal short-circuit fault samples with the impedance baseline of healthy batteries to screen for characteristic frequency points sensitive to internal short circuits. The internal short-circuit fault samples can be internal short-circuit fault battery samples constructed through metallic foreign objects, local conductive channels, membrane damage, external equivalent methods, or other methods. By comparing the impedance amplitude, impedance change trend, and frequency response morphology differences between healthy batteries and internal short-circuit fault samples at different frequency points and different cycling stages, frequency points whose differences meet preset conditions are determined as internal short-circuit sensitive characteristic frequencies.

[0008] Impedance magnitude difference includes absolute offset or relative offset. The relative offset of impedance can be expressed as: R Z ( f i )=(|Z test ( f i )|-|Z base ( f i )|) / |Z base ( f i )| Where RZ(fi) is the relative impedance offset at frequency fi, Ztest(fi) is the impedance of the internal short-circuit fault sample at frequency fi, and Zbase(fi) is the impedance of the healthy battery impedance baseline at frequency fi.

[0009] S3: Based on the selected internal short-circuit sensitive characteristic frequency points, configure a dynamic impedance detection frequency point set, and apply a dynamic excitation signal to the lithium-ion battery under test through a dynamic impedance online detection device. The dynamic excitation signal can be one or more of a single-frequency excitation signal, a multi-frequency composite excitation signal, a swept-frequency excitation signal, or a pulse excitation signal, and the frequency of the dynamic excitation signal corresponds to the internal short-circuit sensitive characteristic frequency points selected in step S2.

[0010] S4: Synchronously acquire the voltage and current response signals of the lithium-ion battery under test, and calculate the dynamic impedance at each internal short-circuit sensitive characteristic frequency point based on the voltage and current response signals, forming a dynamic impedance time series. The dynamic impedance can be calculated according to the following formula: Z (f i , t k )=U( f i , t k ) / I( f i , t k ) Where Z(fi,tk) is the dynamic impedance at frequency fi at time tk, U(fi,tk) is the frequency domain component of the voltage response signal at frequency fi, and I(fi,tk) is the frequency domain component of the current response signal at frequency fi.

[0011] S5: Extract dynamic impedance features from the dynamic impedance time series, wherein the dynamic impedance features include at least one of impedance amplitude, impedance change rate, impedance abrupt change time, and frequency response pattern. The impedance change rate can be expressed as: K Z ( f i , t k )=[|Z( f i , t k )|-|Z( f i , t k-1 )|] / ( t k - t k-1 ) Wherein, KZ(fi,tk) is the impedance change rate at frequency fi at time tk, Z(fi,tk) is the dynamic impedance at the current sampling time, and Z(fi,tk-1) is the dynamic impedance at the previous sampling time. The impedance change time is the sampling time at which the dynamic impedance characteristics first meet the corresponding preset abnormal conditions.

[0012] The frequency response morphology is a response curve composed of impedance amplitudes at multiple internal short-circuit sensitive characteristic frequency points. Anomalies in the frequency response morphology include at least one of the following: changes in curve slope, changes in local peak values, changes in local valley values, overall shift, decreased curve similarity, or an increase in the number of abnormal frequency points.

[0013] S6: Establish an early internal short-circuit identification criterion based on the dynamic impedance characteristics. When the absolute value of the offset of the impedance amplitude of at least one internal short-circuit sensitive frequency point relative to the impedance baseline of a healthy battery exceeds a first preset threshold, the absolute value of the impedance change rate of at least one internal short-circuit sensitive frequency point exceeds a second preset threshold, or the difference between the frequency response pattern formed by multiple internal short-circuit sensitive frequency points and the corresponding pattern of the impedance baseline of a healthy battery exceeds a third preset threshold, the lithium-ion battery under test is determined to have an early internal short-circuit risk. The first, second, and third preset thresholds can be determined based on experimental data, statistical analysis, or historical operating data.

[0014] Furthermore, the voltage and temperature signals of the lithium-ion battery under test are simultaneously acquired, and the occurrence time of the anomaly in the dynamic impedance characteristics is compared with the occurrence times of the anomalies in the voltage and temperature signals. When the dynamic impedance characteristics meet the preset anomaly conditions while the voltage and temperature signals do not meet their respective anomaly conditions, a suspected internal short circuit or early internal short circuit warning result is output; when the anomaly in the dynamic impedance characteristics occurs earlier than the anomaly in the voltage or temperature signals, an early internal short circuit warning result is output; when the dynamic impedance characteristics, voltage signal, and temperature signal all meet their respective anomaly conditions, a high-risk internal short circuit warning result is output.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention utilizes static electrochemical impedance spectroscopy to screen characteristic frequency points sensitive to internal short circuits, avoiding the problem of blind frequency selection in dynamic online detection and improving the detection specificity.

[0016] 2. This invention uses the selected internal short-circuit sensitive characteristic frequency points for online dynamic impedance detection, which can obtain the dynamic impedance characteristics of the lithium-ion battery under test in a short time, making it suitable for online monitoring of battery systems.

[0017] 3. This invention comprehensively utilizes dynamic impedance characteristics such as impedance amplitude, impedance change rate, impedance change time, and frequency response pattern, which can comprehensively reflect the changes in the internal state of the battery before and after an internal short circuit occurs.

[0018] 4. This invention can identify early risks of internal short circuits before obvious abnormalities appear in voltage and temperature signals, thereby improving the early warning time for internal short circuit faults in batteries.

[0019] 5. This invention is applicable to safety monitoring scenarios for energy storage battery systems, power battery systems, and other lithium-ion batteries, and has engineering application value. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method for early internal short circuit identification and warning of lithium-ion batteries based on dynamic impedance according to the present invention. Figure 2 This is a schematic diagram showing the impedance baseline of a healthy battery and the impedance shift of an internal short-circuit fault sample in a static electrochemical impedance spectroscopy. Figure 3 This is a structural diagram of the dynamic impedance detection system used in this invention; Figure 4 This is a comparison chart of dynamic impedance characteristics and the time of occurrence of voltage and temperature signal anomalies. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be noted that the following embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0022] Example 1 This embodiment provides a method for early internal short circuit identification and warning of lithium-ion batteries based on dynamic impedance. The overall process is as follows: Figure 1 As shown.

[0023] First, a healthy lithium-ion battery of the same type as the one being tested is selected as a benchmark sample. Static electrochemical impedance spectroscopy (EIS) tests are performed under preset states of charge, temperature, cycling stages, and resting conditions to obtain impedance spectral data under healthy conditions and establish an impedance baseline for the healthy battery. To reduce the impact of differences in operating conditions on the identification results, corresponding impedance baselines for healthy batteries can be established for different states of charge, temperatures, cycling stages, or resting conditions.

[0024] Then, static electrochemical impedance spectroscopy (SES) tests were performed on samples with internal short-circuit faults. These samples could be obtained through methods such as metal foreign object induction, local conductive channel construction, membrane damage, or external equivalents. The SES spectra of the internal short-circuit fault samples were compared with the impedance baseline of healthy batteries under the same test conditions to analyze the differences in impedance amplitude, impedance variation trends, and frequency response patterns at different frequencies and cycling stages. Figure 2 As shown, as the internal short-circuit fault develops, the impedance spectrum of some frequency bands shifts relative to the impedance baseline of the healthy battery. Frequency points whose differences meet the preset conditions are selected as internal short-circuit sensitive characteristic frequencies.

[0025] For each candidate frequency point, the impedance amplitude difference between the internal short-circuit fault sample and the impedance baseline of the healthy battery is calculated according to the aforementioned impedance relative offset formula. This difference is then combined with the impedance change trend or frequency response pattern difference at different cycling stages for further selection. Preset conditions can be determined based on the statistical distribution of healthy samples and internal short-circuit fault samples.

[0026] Subsequently, a set of dynamic impedance detection frequency points is configured based on the selected internal short-circuit sensitive characteristic frequency points. The dynamic impedance detection system is as follows: Figure 3As shown, the system includes a multiplexer, an excitation current source, a sampling resistor, an instrumentation amplifier, and an analog-to-digital converter. The multiplexer selects the lithium-ion battery or individual battery cell under test; the excitation current source applies a dynamic excitation signal to the lithium-ion battery under test, corresponding to the frequency point sensitive to internal short circuits; the sampling resistor acquires the current response signal; the instrumentation amplifier amplifies the voltage and current response signals; and the analog-to-digital converter converts the analog signals into digital signals.

[0027] During dynamic testing, the online dynamic impedance detection device applies a dynamic excitation signal to the lithium-ion battery under test and simultaneously acquires voltage and current response signals. Frequency domain analysis is performed on the voltage and current response signals, and the dynamic impedance at each sampling time is obtained according to the aforementioned dynamic impedance calculation formula. The amplitude of these impedance signals is then used to form a dynamic impedance time series.

[0028] Impedance amplitude, impedance rate of change, impedance abrupt change time, and frequency response pattern are extracted from the dynamic impedance time series. Impedance amplitude is used to characterize the degree of deviation of the lithium-ion battery under test from the impedance baseline of a healthy battery at the internal short-circuit sensitive characteristic frequency point; impedance rate of change is used to characterize the rate of impedance change over time; impedance abrupt change time is used to determine the moment when the dynamic impedance characteristic first meets the preset abnormal condition; frequency response pattern is used to characterize the change in the impedance amplitude distribution pattern among multiple internal short-circuit sensitive characteristic frequency points.

[0029] When the absolute value of the impedance amplitude offset of at least one internal short-circuit sensitive characteristic frequency point exceeds the first preset threshold, the absolute value of the impedance change rate of at least one internal short-circuit sensitive characteristic frequency point exceeds the second preset threshold, or the difference between the frequency response pattern formed by multiple internal short-circuit sensitive characteristic frequency points and the corresponding pattern of the impedance baseline of a healthy battery exceeds the third preset threshold, the lithium-ion battery under test is determined to have an early internal short-circuit risk, and an early warning result is output.

[0030] Furthermore, the voltage and temperature signals of the lithium-ion battery under test are simultaneously acquired, and the time of abnormal occurrence of dynamic impedance characteristics is compared with the time of abnormal occurrence of voltage and temperature signals. For example... Figure 4 As shown, the dynamic impedance characteristics can change before obvious anomalies appear in the voltage and temperature signals. Therefore, when the dynamic impedance characteristics meet the preset abnormality conditions while the voltage and temperature signals do not meet their respective abnormality conditions, a suspected internal short circuit or early internal short circuit warning result is output; when the anomaly in the dynamic impedance characteristics occurs earlier than the anomaly in the voltage or temperature signals, an early internal short circuit warning result is output; when the dynamic impedance characteristics, voltage signal, and temperature signal all meet their respective abnormality conditions, a high-risk internal short circuit warning result is output.

[0031] Example 2 In another embodiment, the dynamic impedance detection frequency set can be adjusted based on the differences in static electrochemical impedance spectra of different types of lithium-ion batteries. For lithium-ion batteries with different capacities, material systems, or packaging forms, corresponding healthy battery impedance baselines and internal short-circuit sensitive characteristic frequency sets can be established respectively.

[0032] In practical applications, the appropriate healthy battery impedance baseline is first selected based on the type of lithium-ion battery under test and its current operating conditions. Then, the dynamic impedance characteristics of the lithium-ion battery under test are compared with the healthy battery impedance baseline. When the impedance amplitude deviation, impedance change rate, or frequency response pattern abnormally exceeds the corresponding preset threshold, an early internal short circuit warning is output. The preset threshold can be determined based on experimental data, statistical analysis, or historical operating data.

[0033] The above are merely specific embodiments of the present invention. Equivalent substitutions or modifications made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for early internal short circuit identification and warning of lithium-ion batteries based on dynamic impedance, characterized in that, Includes the following steps: S1: Perform static electrochemical impedance spectroscopy on healthy lithium-ion batteries to establish an impedance baseline for healthy batteries; S2: Perform static electrochemical impedance spectroscopy on the internal short-circuit fault sample, and compare the impedance spectrum of the internal short-circuit fault sample with the impedance baseline of the healthy battery to screen the sensitive characteristic frequency points of internal short circuit. S3: Configure a set of dynamic impedance detection frequency points based on the internal short-circuit sensitive characteristic frequency points, and apply a dynamic excitation signal to the lithium-ion battery under test through a dynamic impedance online detection device; S4: Synchronously acquire the voltage response signal and current response signal of the lithium-ion battery under test, calculate the dynamic impedance at each internal short-circuit sensitive characteristic frequency point, and form a dynamic impedance time series. S5: Extract dynamic impedance features from the dynamic impedance time series, wherein the dynamic impedance features include at least one of impedance amplitude, impedance change rate, impedance abrupt change time, and frequency response pattern. S6: Compare the dynamic impedance characteristics with the preset early internal short circuit identification criteria. When the dynamic impedance characteristics meet the preset abnormal conditions, determine that the lithium-ion battery under test has an early internal short circuit risk and output a warning result.

2. The method according to claim 1, characterized in that, In step S1, the healthy battery impedance baseline includes static electrochemical impedance spectroscopy data of healthy lithium-ion batteries under different states of charge, temperatures, cycling stages, or resting conditions.

3. The method according to claim 1, characterized in that, In step S2, the internal short-circuit fault sample is an internal short-circuit fault battery sample constructed through metal foreign objects, local conductive channels, membrane damage, or external equivalent methods.

4. The method according to claim 1, characterized in that, In step S2, the impedance amplitude difference, impedance change trend or frequency response pattern difference of the impedance spectrum of the healthy battery impedance baseline and the internal short circuit fault sample at different frequency points and different cycle stages are compared, and the frequency points whose differences meet the preset conditions are determined as the internal short circuit sensitive characteristic frequency points. The impedance amplitude difference includes an absolute offset or a relative offset, and the relative offset is calculated according to the following formula: R Z ( f i )=(|Z test ( f i )|-|Z base ( f i )|) / |Z base ( f i )| Where RZ(fi) is the relative impedance offset at frequency fi, Ztest(fi) is the impedance of the internal short-circuit fault sample at frequency fi, and Zbase(fi) is the impedance of the healthy battery impedance baseline at frequency fi.

5. The method according to claim 1, characterized in that, In step S3, the dynamic impedance online detection device includes a multiplexer, an excitation current source, a sampling resistor, an instrumentation amplifier, and an analog-to-digital converter; the dynamic excitation signal is one or more of the following: a single-frequency excitation signal, a multi-frequency composite excitation signal, a swept-frequency excitation signal, or a pulse excitation signal generated based on the internal short-circuit sensitive characteristic frequency point.

6. The method according to claim 1, characterized in that, In step S4, the dynamic impedance of frequency fi at time tk is calculated according to the following formula: Z ( f i , t k )=U( f i , t k ) / AND( f i , t k ) Where Z(fi,tk) is the dynamic impedance at frequency fi at time tk, U(fi,tk) is the frequency domain component of the voltage response signal at frequency fi, and I(fi,tk) is the frequency domain component of the current response signal at frequency fi.

7. The method according to claim 1, characterized in that, In step S5, the impedance change rate is calculated according to the following formula: K Z ( f i , t k )=[|Z( f i , t k )|-|Z( f i , t k-1 )|] / ( t k - t k-1 ) Wherein, KZ(fi,tk) is the impedance change rate of frequency fi at time tk, Z(fi,tk) is the dynamic impedance at the current sampling time, and Z(fi,tk-1) is the dynamic impedance at the previous sampling time.

8. The method according to claim 1, characterized in that, In step S5, the frequency response morphology is a response curve composed of impedance amplitudes at multiple internal short-circuit sensitive characteristic frequency points. The abnormal frequency response morphology includes at least one of the following: curve slope change, local peak change, local valley change, overall shift, decrease in curve similarity, or increase in the number of abnormal frequency points.

9. The method according to claim 1, characterized in that, In step S6, the preset abnormal condition includes at least one of the following conditions: The absolute value of the offset of the impedance amplitude at at least one internal short-circuit sensitive characteristic frequency point relative to the impedance baseline of a healthy battery exceeds a first preset threshold. The absolute value of the impedance change rate at at least one internal short-circuit sensitive characteristic frequency point exceeds a second preset threshold. The difference between the frequency response pattern formed by multiple internal short-circuit sensitive characteristic frequency points and the corresponding pattern of the impedance baseline of a healthy battery exceeds the third preset threshold.

10. The method according to claim 9, characterized in that, The voltage and temperature signals of the lithium-ion battery under test are collected simultaneously, and the time of abnormal occurrence of dynamic impedance characteristics is compared with the time of abnormal occurrence of voltage and temperature signals. When the dynamic impedance characteristics meet the preset abnormal conditions, but the voltage signal and temperature signal do not meet their respective abnormal conditions, a suspected internal short circuit or early internal short circuit warning result is output. When the abnormality of the dynamic impedance characteristics occurs earlier than the abnormality of the voltage signal or temperature signal, an early internal short circuit warning result is output. When the dynamic impedance characteristics, voltage signal, and temperature signal all meet their respective abnormal conditions, a high-risk internal short circuit warning result is output.

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