Lithium battery single body internal short circuit fault detection method, system, medium and equipment

Abstract

The invention discloses a lithium battery monomer internal short circuit fault detection method, system, medium and equipment, and the method comprises the steps: carrying out an over-discharge induced internal short circuit fault circulation experiment for a normal monomer battery, and obtaining different internal short circuit fault monomer samples; establishing an impedance spectrum database of the battery in a normal state and a fault state under a wide-temperature working condition and in a full charge state, and calculating charge transfer internal resistance and SEI internal resistance values in impedance spectrum data through the area of a relaxation time distribution curve; the method comprises the following steps of: establishing a database based on temperature distribution by taking the resistance value change amplitude of the single battery transited from a normal state to a fault state in different temperature states as a threshold value fitting function, and performing impedance spectrum measurement and temperature measurement on the single battery to be measured after each charge-discharge cycle is finished; performing relaxation time distribution quantification to obtain charge transfer internal resistance and SEI internal resistance values; and if the resistance value change amplitudes of the charge transfer internal resistor and the SEI internal resistor both exceed a threshold value, determining that an internal short circuit fault exists in the single battery.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery status detection, and in particular to a method, system, medium and equipment for detecting short circuit faults within a lithium battery cell. Background Art

[0002] As one of the most advanced and widely used rechargeable batteries today, lithium batteries have become the primary power source for electric vehicles, portable devices, and energy storage. Their high energy density, long cycle life, and environmental friendliness make them highly sought after. In practical applications, lithium batteries are often used as modules, key components in electric vehicles and energy storage power plants. These modules consist of multiple lithium battery cells connected in series and parallel to provide the required voltage and current output. As the number of series-connected cells in high-voltage systems such as electric vehicles increases, batteries are increasingly susceptible to overdischarge-induced internal short-circuit failures. Internal short-circuit failures caused by overdischarge are generally in their early stages of development and are less obvious than those caused by thermal and mechanical abuse, making them difficult to detect. As specific energy increases and separator thickness decreases, the thermal stability of lithium-ion batteries decreases. In the later stages of internal short-circuit failure, abnormal voltage and temperature distributions develop within the battery. This abnormal distribution can cause a sharp decline in battery performance and potentially lead to serious safety issues such as overheating, explosion, and fire. Therefore, early detection and diagnosis of short circuit failures in single cells is crucial to ensuring the safety and reliability of the battery system.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0004] The present invention provides a method, system, medium and equipment for detecting internal short-circuit faults in lithium battery cells. By extracting the amplitude of characteristic impedance changes, the internal short-circuit fault state of the lithium battery under different ambient temperatures can be tracked and detected after each cycle.

[0005] A method for detecting short circuit faults in lithium battery cells includes:

[0006] S1: Conduct an over-discharge-induced internal short-circuit failure cycle experiment on normal single cells to obtain a single cell sample with internal short-circuit failure at a discharge depth of -15% capacity.

[0007] S2: Conduct electrochemical impedance spectroscopy (EIS) tests on single-cell battery samples at different temperatures to establish an impedance spectrum database for fully charged batteries in normal and faulty conditions under a wide operating temperature range of 15°C to 45°C.

[0008] S3: Calculate the charge transfer resistance and solid electrolyte interface (SEI) resistance in the impedance spectrum data using the area of ​​the relaxation time distribution curve.

[0009] S4: Calculate the solid electrolyte interface membrane internal resistance and charge transfer internal resistance of the single cell under normal conditions and under fault conditions at different temperatures, and calculate their corresponding change amplitudes α and β as the impedance change thresholds for evaluating internal short circuits in the battery. Construct fitting functions between the solid electrolyte interface membrane internal resistance change amplitude threshold, the charge transfer internal resistance change amplitude threshold, and temperature, respectively, and establish a database of the solid electrolyte interface membrane internal resistance change amplitude threshold and the charge transfer internal resistance change amplitude threshold based on temperature distribution;

[0010] S5: After each charge-discharge cycle, impedance spectrum and temperature measurements are performed on the single cell to be tested. The charge transfer internal resistance and solid electrolyte interface membrane internal resistance are calculated by calculating the area of ​​the relaxation time distribution curve. The corresponding temperature and the change in charge transfer internal resistance and solid electrolyte interface membrane internal resistance compared to the normal state of the single cell are recorded.

[0011] S6: Substitute the temperature data into the fitting function between the solid electrolyte interface membrane internal resistance change amplitude threshold, the charge transfer internal resistance change amplitude threshold and the temperature to obtain the charge transfer internal resistance change amplitude threshold and the solid electrolyte interface membrane internal resistance change amplitude threshold, and compare them with the charge transfer internal resistance and solid electrolyte interface membrane internal resistance change amplitude of the single cell obtained by the test. If the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance change amplitude both exceed the corresponding thresholds, it is determined that an internal short circuit fault exists in the single cell.

[0012] In the method for detecting internal short-circuit faults in lithium battery cells, in step S1, the overdischarge-induced internal short-circuit fault cycle experiment includes triggering the internal short-circuit fault by overdischarging the auxiliary battery in series to below 0V, and overdischarging the single battery to a -15% capacity state so that the copper current collector is continuously corroded and dissolved into copper ions, which pass through the diaphragm and are deposited on the cathode. As the deposited copper ions continue to increase, they penetrate the diaphragm and connect the two electrodes, thereby triggering an internal short-circuit fault.

[0013] In the lithium battery cell internal short circuit fault detection method, in step S2, the electrochemical impedance spectroscopy injects an AC sinusoidal signal into the battery to be tested, obtains its response signal, and calculates the impedance data of the battery. When the sinusoidal current signal is injected, the battery electrochemical impedance calculation formula is as follows:

[0014]

[0015] Where Z is the total impedance of the battery, U is the output voltage signal, I is the input current signal, |U| is the output voltage amplitude, |I| is the input current amplitude, |Z| is the modulus of the system impedance, ω is the angular frequency of the signal, and t is time. is the phase angle of the signal.

[0016] In the method for detecting internal short circuit faults in lithium battery cells, in step S3, calculating the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance by using the area of ​​the relaxation time distribution curve includes converting the frequency domain impedance data into a time domain relaxation time distribution by performing relaxation time distribution conversion on the electrochemical impedance spectrum data, and obtaining the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance by calculating the area enclosed by the characteristic peak of the relaxation time distribution and the curve.

[0017] Among them, the relaxation time distribution function uses the equivalent circuit method to convert the electrochemical impedance spectroscopy data into an expression of multiple differential resistances and capacitances in parallel. The formula is as follows:

[0018]

[0019] Where Z(ω) is the total impedance of the battery; n is the number of parallel structures of differential resistance and capacitance; dR i is the ith differential resistance; j is the imaginary unit; ω is the angular frequency; C i is the i-th capacitor; dτ i is the discrete relaxation time differential expression, which is:

[0020] Converting the above differential expression into integral form gives the relaxation time distribution function, which is as follows:

[0021]

[0022] Where Z(ω) is the total impedance of the battery, R0 is the ohmic impedance and is not affected by frequency; γ(τ) is the relaxation time distribution function; τ is the relaxation time; j is the imaginary unit; and ω is the angular frequency.

[0023] In the method for detecting short circuit faults in lithium battery cells, in step S4, the resistance change amplitude of the single cell from the normal state to the fault state under different temperature conditions includes: the internal resistance of the solid electrolyte interface membrane of the single cell under normal cycle state is R SEI0 , the internal resistance of the solid electrolyte interface film of the single cell in the fault state is R SEI , the internal resistance variation of the solid electrolyte interface film α is

[0024] ,

[0025] Under normal cycle conditions, the internal resistance of charge transfer of a single cell is R ct0, the charge transfer internal resistance of the single battery in the fault state is R ct , the charge transfer internal resistance variation β is

[0026] .

[0027] In the lithium battery cell internal short circuit fault detection method, the fitting function is a cubic function.

[0028] In the method for detecting short-circuit faults in lithium battery cells, the single cells are lithium batteries.

[0029] In the method for detecting short-circuit faults in lithium battery cells, the lithium battery cells are in a fully charged state during the measurement process.

[0030] A system for implementing the method includes:

[0031] A measuring unit, which is used to perform short-circuit induced cycle tests on single lithium batteries and electrochemical impedance spectroscopy tests under different temperature environments;

[0032] A data unit is used to calculate the relaxation time distribution curve area of ​​the electrochemical impedance spectroscopy data of batteries at different temperatures, extract the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance change amplitude to establish a database;

[0033] A model unit, which uses the impedance characteristics at different temperatures established by the data unit as a threshold and establishes a database based on the temperature distribution through a fitting function;

[0034] The detection unit is used to perform electrochemical impedance spectroscopy testing on the single battery to be tested after each cycle, and substitute the quantitatively extracted data into the established internal short circuit fault diagnosis model to obtain the fault status.

[0035] A computer storage medium includes computer instructions, which, when executed on a computer, cause the computer to execute the method described above.

[0036] An electronic device, comprising:

[0037] A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein:

[0038] When the processor executes the program, the method described is implemented.

[0039] Compared with existing technologies, the present invention offers the following advantages: Through temperature-impedance coupling modeling and DRT multi-parameter analysis, it addresses the difficulty of accurately identifying internal short circuits early in a wide temperature range, combining high sensitivity, strong environmental adaptability, and engineering feasibility. By constructing a database of impedance spectra of normal and faulty batteries at different temperatures and combining it with a threshold fitting function for the temperature distribution, dynamic temperature compensation of the detection standard is achieved. Existing methods often misjudge due to impedance characteristic drift caused by temperature changes. By establishing a temperature-impedance correlation model and dynamically binding the threshold to the temperature parameter, this method significantly improves detection reliability under different operating conditions. Compared to traditional methods that rely solely on total impedance or a single parameter, this method simultaneously monitors the dual changes in the solid electrolyte interface membrane internal resistance and charge transfer resistance through quantification of the relaxation time distribution curve area, leveraging the differences in their short-circuit sensitivities to form a multi-dimensional criterion. It can detect resistance anomalies at an early stage of internal short circuits, providing earlier warning than voltage / temperature monitoring, significantly improving the timeliness of safety and control measures. An embedded process for real-time impedance spectroscopy measurement after charge and discharge cycles, combined with parameter extraction from the impedance spectrum, enables diagnosis without disassembling the battery or interrupting operation. Traditional internal short-circuit detection often relies on destructive disassembly or complex BMS algorithm inference. However, our Ahu uses standardized EIS testing and automated threshold comparison to achieve non-invasive rapid screening. It only takes a few minutes to complete the full battery status assessment, significantly reducing operation and maintenance costs and adapting to large-scale application needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Various other advantages and benefits of the present invention will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are intended only to illustrate preferred embodiments and are not to be construed as limiting the present invention. It should be understood that the drawings described below are merely examples of the present invention, and that those skilled in the art will be able to derive other drawings from these drawings without inventive effort. Throughout the drawings, identical reference numerals are used to denote identical components.

[0041] In the attached figure:

[0042] Figure 1 This is a flow chart of a method for detecting short-circuit faults in lithium battery cells under wide temperature conditions, provided by one embodiment of the present disclosure;

[0043] Figure 2 This is a diagram showing the analysis process of the electrochemical impedance spectrum of a lithium-ion battery changing with temperature, provided by another embodiment of the present disclosure, wherein: Figure 2 (a) is a diagram showing the analysis process of the normal battery impedance spectrum changing with temperature. Figure 2 (b) is a diagram showing the analysis process of the impedance spectrum of the battery with internal short circuit fault changing with temperature;

[0044] Figure 3 : is a fitting function relationship curve effect diagram of the impedance characteristic change amplitude with temperature provided by another embodiment of the present disclosure, wherein: Figure 3 (a) is the effect diagram of the fitting function relationship curve of the change amplitude of the solid electrolyte interface film internal resistance with temperature. Figure 3 Middle (b) is the effect diagram of the fitting function relationship curve of the change amplitude of charge transfer internal resistance with temperature.

[0045] The present invention will be further explained below with reference to the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0046] Specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although specific embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0047] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the present invention, but the description is based on the general principles of the specification and is not intended to limit the scope of the invention. The scope of protection of the present invention shall be as defined in the attached claims.

[0048] To facilitate understanding of the embodiments of the present invention, further explanation will be given below using specific embodiments as examples in conjunction with the accompanying drawings, and the accompanying drawings do not constitute a limitation on the embodiments of the present invention.

[0049] like Figures 1 to 3 As shown, the method for detecting short circuit faults in lithium battery cells includes the following steps:

[0050] S1: Conduct an over-discharge-induced internal short-circuit failure cycle experiment on normal single cells to obtain a single cell sample with internal short-circuit failure at a discharge depth of -15% capacity.

[0051] S2: Conduct electrochemical impedance spectroscopy (EIS) tests on single-cell battery samples at different temperatures to establish an impedance spectrum database for fully charged batteries in normal and faulty conditions under a wide operating temperature range of 15°C to 45°C.

[0052] S3: Calculate the charge transfer resistance and solid electrolyte interface (SEI) resistance in the impedance spectrum data using the area of ​​the relaxation time distribution curve.

[0053] S4: Calculate the solid electrolyte interface membrane internal resistance and charge transfer internal resistance of the single cell under normal conditions and under fault conditions at different temperatures, and calculate their corresponding change amplitudes α and β as the impedance change thresholds for evaluating internal short circuits in the battery. Construct fitting functions between the solid electrolyte interface membrane internal resistance change amplitude threshold, the charge transfer internal resistance change amplitude threshold, and temperature, respectively, and establish a database of the solid electrolyte interface membrane internal resistance change amplitude threshold and the charge transfer internal resistance change amplitude threshold based on temperature distribution;

[0054] S5: After each charge-discharge cycle, impedance spectrum and temperature measurements are performed on the single cell to be tested. The charge transfer internal resistance and solid electrolyte interface membrane internal resistance are calculated by calculating the area of ​​the relaxation time distribution curve. The corresponding temperature and the change in charge transfer internal resistance and solid electrolyte interface membrane internal resistance compared to the normal state of the single cell are recorded.

[0055] S6: Substitute the temperature data into the fitting function between the solid electrolyte interface membrane internal resistance change amplitude threshold, the charge transfer internal resistance change amplitude threshold and the temperature to obtain the charge transfer internal resistance change amplitude threshold and the solid electrolyte interface membrane internal resistance change amplitude threshold, and compare them with the charge transfer internal resistance and solid electrolyte interface membrane internal resistance change amplitude of the single cell obtained by the test. If the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance change amplitude both exceed the corresponding thresholds, it is determined that an internal short circuit fault exists in the single cell.

[0056] In a preferred embodiment of the method for detecting internal short-circuit faults in lithium battery cells, in step S1, the over-discharge-induced internal short-circuit fault cycle experiment includes triggering the internal short-circuit fault by over-discharging the auxiliary battery in series to below 0V, and over-discharging the single battery to a -15% capacity state so that the copper current collector is continuously corroded and dissolved into copper ions, which pass through the diaphragm and are deposited on the cathode. As the deposited copper ions continue to increase, they penetrate the diaphragm and connect the two electrodes, thereby triggering an internal short-circuit fault.

[0057] In a preferred embodiment of the method for detecting short circuit faults in lithium battery cells, in step S2, electrochemical impedance spectroscopy is a fast and non-destructive battery impedance testing technology. A small-amplitude AC sinusoidal signal is injected into the battery to be tested, and its response signal is obtained to calculate the battery impedance data. Taking the injection of a sinusoidal current signal as an example, the battery electrochemical impedance calculation formula is as follows:

[0058]

[0059] Where Z is the total impedance of the battery, U is the output voltage signal, I is the input current signal, |U| is the output voltage amplitude, |I| is the input current amplitude, |Z| is the modulus of the system impedance, ω is the angular frequency of the signal, and t is time. is the phase angle of the signal.

[0060] In a preferred embodiment of the method for detecting internal short circuit faults in lithium battery cells, in step S3, calculating the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance by the area of ​​the relaxation time distribution curve includes performing relaxation time distribution conversion on the electrochemical impedance spectroscopy data, converting the frequency domain impedance data into a time domain relaxation time distribution, and obtaining the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance by calculating the area enclosed by the characteristic peak of the relaxation time distribution and the curve.

[0061] Among them, the relaxation time distribution function uses the equivalent circuit method to convert the electrochemical impedance spectroscopy data into an expression of multiple differential resistances and capacitances in parallel. The formula is as follows:

[0062]

[0063] Where Z(ω) is the total impedance of the battery; n is the number of parallel structures of differential resistance and capacitance; dR i is the ith differential resistance; j is the imaginary unit; ω is the angular frequency; C i is the i-th capacitor; dτ i is the discrete relaxation time differential expression, which is:

[0064] Converting the above differential expression into integral form gives the relaxation time distribution function, which is as follows:

[0065]

[0066] Where Z(ω) is the total impedance of the battery, R0 is the ohmic impedance and is not affected by frequency; γ(τ) is the relaxation time distribution function; τ is the relaxation time; j is the imaginary unit; and ω is the angular frequency.

[0067] In a preferred embodiment of the method for detecting short circuit faults in a lithium battery cell, in step S4, the resistance change amplitude of the cell transitioning from a normal state to a fault state under different temperature conditions includes: the internal resistance of the solid electrolyte interface membrane of the cell under normal cycle condition is R SEI0 , the internal resistance of the solid electrolyte interface film of the single cell in the fault state is R SEI , the internal resistance variation of the solid electrolyte interface film α is

[0068] ,

[0069] Under normal cycle conditions, the internal resistance of charge transfer of a single cell is R ct0 , the charge transfer internal resistance of the single battery in the fault state is R ct , the charge transfer internal resistance variation β is

[0070] .

[0071] In a preferred embodiment of the method for detecting internal short circuit faults in lithium battery cells, the fitting function is a cubic function.

[0072] In a preferred embodiment of the method for detecting short-circuit faults in lithium battery cells, the cell is a lithium battery.

[0073] In a preferred embodiment of the method for detecting short circuit faults in a lithium battery cell, the lithium battery cell is in a fully charged state during the measurement process.

[0074] A system for implementing the method includes:

[0075] A measuring unit, which is used to perform short-circuit induced cycle tests on single lithium batteries and electrochemical impedance spectroscopy tests under different temperature environments;

[0076] A data unit is used to calculate the relaxation time distribution curve area of ​​the electrochemical impedance spectroscopy data of batteries at different temperatures, extract the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance change amplitude to establish a database;

[0077] A model unit, which uses the impedance characteristics at different temperatures established by the data unit as a threshold and establishes a database based on the temperature distribution through a fitting function;

[0078] The detection unit is used to perform electrochemical impedance spectroscopy testing on the single battery to be tested after each cycle, and substitute the quantitatively extracted data into the established internal short circuit fault diagnosis model to obtain the fault status.

[0079] A computer storage medium includes computer instructions, which, when executed on a computer, cause the computer to execute the method described above.

[0080] An electronic device, comprising:

[0081] A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein:

[0082] When the processor executes the program, the method described is implemented.

[0083] In one embodiment, Figure 1 As shown, a method for detecting short circuit faults in lithium battery cells under wide temperature conditions includes the following steps:

[0084] S1: Conduct an over-discharge-induced internal short-circuit failure cycle experiment on normal single cells to obtain a single cell sample with internal short-circuit failure at a discharge depth of -15% capacity.

[0085] S2: Conduct electrochemical impedance spectroscopy (EIS) tests on single-cell battery samples at different temperatures to establish an impedance spectrum database for fully charged batteries in normal and faulty conditions under a wide operating temperature range of 15°C to 45°C.

[0086] S3: Calculate the charge transfer resistance and solid electrolyte interface (SEI) resistance in the impedance spectrum data using the area of ​​the relaxation time distribution curve.

[0087] S4: Calculate the solid electrolyte interface membrane internal resistance and charge transfer internal resistance of the single cell under normal conditions and under fault conditions at different temperatures, and calculate their corresponding change amplitudes α and β as the impedance change thresholds for evaluating internal short circuits in the battery. Construct fitting functions between the solid electrolyte interface membrane internal resistance change amplitude threshold, the charge transfer internal resistance change amplitude threshold, and temperature, respectively, and establish a database of the solid electrolyte interface membrane internal resistance change amplitude threshold and the charge transfer internal resistance change amplitude threshold based on temperature distribution;

[0088] S5: After each charge-discharge cycle, impedance spectrum and temperature measurements are performed on the single cell to be tested. The charge transfer internal resistance and solid electrolyte interface membrane internal resistance are calculated by calculating the area of ​​the relaxation time distribution curve. The corresponding temperature and the change in charge transfer internal resistance and solid electrolyte interface membrane internal resistance compared to the normal state of the single cell are recorded.

[0089] S6: Substitute the temperature data into the fitting function between the solid electrolyte interface membrane internal resistance change amplitude threshold, the charge transfer internal resistance change amplitude threshold and the temperature to obtain the charge transfer internal resistance change amplitude threshold and the solid electrolyte interface membrane internal resistance change amplitude threshold, and compare them with the charge transfer internal resistance and solid electrolyte interface membrane internal resistance change amplitude of the single cell obtained by the test. If the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance change amplitude both exceed the corresponding thresholds, it is determined that an internal short circuit fault exists in the single cell.

[0090] The above embodiments constitute a complete technical solution of the present disclosure. The method described in this embodiment can perform non-destructive and rapid testing of lithium batteries. Through temperature-impedance coupling modeling and DRT multi-parameter analysis technology, it solves the difficulty of accurately identifying internal short circuits at an early stage over a wide temperature range. It combines high sensitivity, strong environmental adaptability, and engineering feasibility.

[0091] In another embodiment, the electrochemical impedance spectroscopy is a fast and non-destructive battery impedance testing technology. By injecting a small-amplitude AC sinusoidal signal into the battery to be tested and obtaining its response signal, the battery impedance data is calculated. Taking the injection of a sinusoidal current signal as an example, the battery electrochemical impedance calculation formula is as follows:

[0092]

[0093] Where Z is the total impedance of the battery, U is the output voltage signal, I is the input current signal, |U| is the output voltage amplitude, |I| is the input current amplitude, |Z| is the modulus of the system impedance, ω is the angular frequency of the signal, and t is time. is the phase angle of the signal.

[0094] In another embodiment, the calculation of the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance using the area of ​​the relaxation time distribution curve includes converting the frequency domain impedance data into the distribution of time domain relaxation time by performing relaxation time distribution conversion on the electrochemical impedance spectrum data, and obtaining the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance by calculating the area enclosed by the characteristic peak of the relaxation time distribution and the curve.

[0095] Among them, the relaxation time distribution function uses the equivalent circuit method to convert the electrochemical impedance spectroscopy data into an expression of multiple differential resistances and capacitances in parallel. The formula is as follows:

[0096]

[0097] Where Z(ω) is the total impedance of the battery; n is the number of parallel structures of differential resistance and capacitance; dR i is the ith differential resistance; j is the imaginary unit; ω is the angular frequency; C i is the i-th capacitor; dτ i is the discrete relaxation time differential expression, which is:

[0098] Converting the above differential expression into integral form gives the relaxation time distribution function, which is as follows:

[0099]

[0100] Where Z(ω) is the total impedance of the battery, R0 is the ohmic impedance and is not affected by frequency; γ(τ) is the relaxation time distribution function; τ is the relaxation time; j is the imaginary unit; and ω is the angular frequency.

[0101] In another embodiment, the impedance spectrum comparison analysis of the battery at different temperature conditions before and after the internal short circuit failure occurs is as follows: Figure 3As shown, under different internal short-circuit fault levels, the battery's electrochemical impedance spectrum shows a consistent trend of shrinking toward the lower left as temperature rises, with no significant change in overall shape. Furthermore, based on the impedance spectrum curves under different temperature states and internal short-circuit fault levels, it can be seen that the magnitude of the impedance spectrum shrinkage with temperature is nearly consistent across different internal short-circuit fault levels. Therefore, by considering the impact of temperature on impedance characteristics, a temperature-impedance correlation model can be established in combination with experimental data to enable internal short-circuit fault diagnosis under different ambient temperatures.

[0102] In another embodiment, the function fitting process using the resistance change amplitude of the single battery at different temperature states from the normal state to the fault state as the threshold is as follows. The average value of the characteristic impedance change amplitude of the lithium battery at 15°C, 20°C, 25°C, 30°C, 35°C, 40°C and 45°C is selected, and the mapping relationship between the change amplitude as the internal short circuit fault develops and the temperature is analyzed to obtain the corresponding fitting function image as shown below. Figure 3 The fitting function of the relationship between the change amplitude of the solid electrolyte interface film internal resistance and the charge transfer internal resistance affected by the internal short circuit fault and the temperature change is shown in the following formula.

[0103] (1)

[0104] (2)

[0105] Where, ΔR SEI，T is the variation of the internal resistance of the solid electrolyte interface film as the internal short circuit fault develops at temperature T; ΔR ct，T is the change range of the charge transfer internal resistance with the development of the internal short circuit fault under the temperature T state; T is the current temperature state of the battery. The fitting function correlation coefficient R of the change range of the solid electrolyte interface film internal resistance is 2 The correlation coefficient of the fitting function of the charge transfer internal resistance variation range is R 2 The value reaches 0.9989, and the residual sum of squares is only 16.2140. Therefore, the variation in the characteristic impedance of a fully charged lithium battery under different temperature conditions can be characterized by a cubic equation with two temperatures as independent variables. Based on this, the theoretical variation in the characteristic impedance when an internal short circuit occurs can be obtained by substituting known temperature conditions. The actual variation in the charge transfer internal resistance and solid electrolyte interface membrane internal resistance of a fully charged lithium battery during cycling can be monitored in real time. By comparing the two, internal short circuit faults in individual cells can be identified.

[0106] In another embodiment, the present disclosure further provides a method for detecting short circuit faults in lithium battery cells under wide temperature conditions, including:

[0107] Measuring unit, used for single lithium battery internal short circuit induced cycle test and electrochemical impedance spectroscopy test under different temperature environments;

[0108] The data unit is used to calculate the relaxation time distribution curve area of ​​the electrochemical impedance spectroscopy data of batteries at different temperatures, extract the charge transfer internal resistance and the change amplitude of the solid electrolyte interface membrane internal resistance to establish a database;

[0109] The model unit uses the impedance characteristics at different temperatures established by the data unit as thresholds and the fitting function to establish a database based on temperature distribution;

[0110] The detection unit is used to perform electrochemical impedance spectroscopy testing on the single battery to be tested after each cycle, and substitute the quantified extracted data into the established internal short circuit fault diagnosis model to obtain the fault status.

[0111] In one embodiment, the method steps include: conducting an over-discharge-induced internal short circuit fault cycle experiment on normal single cells to obtain samples of cells with different internal short circuit faults; conducting electrochemical impedance spectroscopy tests on the single cell samples under different temperature environments to establish an impedance spectrum database of batteries in normal state, fault state and fully charged state under wide temperature conditions; calculating the charge transfer internal resistance and solid electrolyte interface membrane internal resistance in the impedance spectrum data by using the area of ​​the relaxation time distribution curve for analysis; using the resistance change amplitude of the single cell when transitioning from normal state to fault state under different temperature conditions as a threshold fitting function to establish a database based on temperature distribution; performing impedance spectrum measurement and temperature measurement on the single cell to be tested after each charge and discharge cycle, and quantifying the relaxation time distribution to obtain the lower charge transfer internal resistance and solid electrolyte interface membrane internal resistance; comparing the resistance change amplitude of the single cell with the threshold value at the temperature, and if the change amplitude of the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance both exceed the threshold value, it is determined that an internal short circuit fault exists in the single cell. Based on the differences in impedance characteristics between normal and short-circuit fault battery cells, the present invention proposes a method for detecting short-circuit faults in lithium battery cells under wide temperature conditions. By tracking and detecting the faults during the cycle, the short-circuit fault phenomenon in the single cell battery can be detected quickly and economically.

[0112] This method uses a discharge-induced internal short-circuit failure cycle experiment to over-discharge normal batteries to below 0V, even reaching a -15% capacity state. The method exploits the corrosion and dissolution of the copper current collector into copper ions, which then deposit at the cathode and penetrate the separator, inducing an internal short circuit. This method controllably simulates the mechanism of a real internal short-circuit failure, providing a foundation for the subsequent establishment of fault samples and a database. This ensures the representativeness and repeatability of experimental samples, facilitating the construction of a standardized fault detection model. Electrochemical impedance spectroscopy (EIS) testing is performed on normal and faulty battery samples over a wide temperature range of 15°C to 45°C. A database of impedance spectra at different temperatures and states at the fully charged state (SOC = 1%) is established. This method captures the dynamic characteristics of the battery's internal electrochemical processes under different ambient temperatures, constructs comprehensive, multi-dimensional reference data, improves the environmental adaptability and robustness of the detection system, and provides raw frequency domain data support for subsequent analysis based on DRT (distributed relaxation time). Quantitative analysis of the distribution of relaxation time (DRT) mathematically inverts EIS data to extract the time-domain relaxation process; identifies the peak areas corresponding to the solid electrolyte interface film internal resistance (R_SEI) and the charge transfer internal resistance (R_ct); and uses the change in the curve envelope area to reflect the magnitude of the resistance change. This allows for a clear interpretation of the physical meaning of complex EIS data, converting frequency-domain data into intuitive electrochemical reaction parameters; and improves detection sensitivity, enabling the capture of early, subtle signs of internal short circuits.

[0113] This system provides quantitative key indicators for establishing threshold judgment criteria. A temperature-based impedance change threshold fitting function extracts the R_SEI and R_ct variations from normal to faulty states at different temperatures. A cubic polynomial function is used to fit the impedance change trend with temperature. The fitting results demonstrate a high correlation coefficient (R² > 0.99) and a low residual sum of squares. A mathematical mapping relationship is established between temperature and impedance change, enabling theoretical threshold prediction at any temperature point. This addresses the high false positive rate of traditional fixed-threshold detection in wide temperature environments and improves the applicability and accuracy of the detection system in various usage scenarios. An online detection mechanism performs EIS measurements and temperature acquisition after each charge and discharge cycle. Real-time R_SEI and R_ct are quantified using DRT, and the actual impedance change is compared with the theoretical threshold at the current temperature. This system enables online, non-destructive, and periodic monitoring of battery health, enabling early detection of potential internal short circuit risks and preventing safety incidents such as thermal runaway. It also supports intelligent early warning and maintenance decision-making for the battery management system (BMS). The dual-parameter joint judgment mechanism (R_SEI + R_ct) simultaneously monitors the solid electrolyte interface film internal resistance and charge transfer internal resistance. An internal short circuit is only determined when the change in both parameters exceeds the threshold at the corresponding temperature. This avoids the risk of misjudging a single parameter and improves diagnostic accuracy. It enhances the reliability of the detection logic, ensuring that the alarm is triggered only when an internal short circuit actually occurs. This conforms to the coupled characteristics of battery electrochemical behavior, embodying scientific and engineering practicality. The construction of an integrated hardware and software detection system includes measurement units, data units, model units, and detection units. Computer programs and electronic equipment are integrated to realize automated detection processes. This promotes the transition of detection technology from the laboratory to engineering applications, supports the health status management of large-scale battery packs, and provides technical support for the safe operation of power batteries, energy storage systems, and other fields.

[0114] Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields. The above-mentioned specific embodiments are merely illustrative and instructive, and are not restrictive. A person skilled in the art, guided by this specification and without departing from the scope of protection of the claims of the present invention, may also devise various forms, all of which fall within the scope of protection of the present invention.

Claims

1. A method for detecting short circuit faults in lithium battery cells, characterized in that: The steps include: S1: Conduct an over-discharge-induced internal short-circuit failure cycle experiment on normal single cells to obtain a single cell sample with internal short-circuit failure at a discharge depth of -15% capacity. S2: Conduct electrochemical impedance spectroscopy (EIS) tests on single-cell battery samples at different temperatures to establish an impedance spectrum database for fully charged batteries in normal and faulty conditions under a wide operating temperature range of 15°C to 45°C. S3: Calculate the charge transfer resistance and solid electrolyte interface film resistance in the impedance spectrum data by using the relaxation time distribution curve area; S4: Calculate the solid electrolyte interface membrane internal resistance and charge transfer internal resistance of the single cell under normal conditions and under fault conditions at different temperatures, and calculate their corresponding change amplitudes α and β as the impedance change thresholds for evaluating internal short circuits in the battery. Construct fitting functions between the solid electrolyte interface membrane internal resistance change amplitude threshold, the charge transfer internal resistance change amplitude threshold, and temperature, respectively, and establish a database of the solid electrolyte interface membrane internal resistance change amplitude threshold and the charge transfer internal resistance change amplitude threshold based on temperature distribution; S5: After each charge-discharge cycle, impedance spectrum and temperature measurements are performed on the single cell to be tested. The charge transfer internal resistance and solid electrolyte interface membrane internal resistance are calculated by calculating the area of ​​the relaxation time distribution curve. The corresponding temperature and the change in charge transfer internal resistance and solid electrolyte interface membrane internal resistance compared to the normal state of the single cell are recorded. S6: Substitute the temperature data into the fitting function between the solid electrolyte interface membrane internal resistance change amplitude threshold, the charge transfer internal resistance change amplitude threshold and the temperature to obtain the charge transfer internal resistance change amplitude threshold and the solid electrolyte interface membrane internal resistance change amplitude threshold, and compare them with the charge transfer internal resistance and solid electrolyte interface membrane internal resistance change amplitude of the single cell obtained by the test. If the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance change amplitude both exceed the corresponding thresholds, it is determined that an internal short circuit fault exists in the single cell.

2. The method for detecting internal short circuit faults in a lithium battery cell according to claim 1, wherein: Preferably, in step S1, the over-discharge-induced internal short-circuit fault cycle experiment includes triggering the internal short-circuit fault by over-discharging the auxiliary battery in series to below 0V, and over-discharging the single battery to -15% capacity so that the copper current collector is continuously corroded and dissolved into copper ions, which pass through the diaphragm and are deposited on the cathode. As the deposited copper ions continue to increase, they penetrate the diaphragm and connect the two electrodes, thereby causing an internal short-circuit fault.

3. The method for detecting internal short circuit faults in a lithium battery cell according to claim 1, wherein: In step S2, the electrochemical impedance spectroscopy calculates the impedance data of the battery by injecting an AC sinusoidal signal into the battery to be tested and obtaining its response signal. When the sinusoidal current signal is injected, the battery electrochemical impedance calculation formula is as follows: ， Where Z is the total impedance of the battery, U is the output voltage signal, I is the input current signal, |U| is the output voltage amplitude, |I| is the input current amplitude, |Z| is the modulus of the system impedance, ω is the angular frequency of the signal, and t is time. is the phase angle of the signal.

4. The method for detecting internal short circuit faults in a lithium battery cell according to claim 1, wherein: In step S3, calculating the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance by using the relaxation time distribution curve area includes converting the frequency domain impedance data into a time domain relaxation time distribution by performing relaxation time distribution conversion on the electrochemical impedance spectroscopy data, and obtaining the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance by calculating the area enclosed by the relaxation time distribution characteristic peak and the curve; Among them, the relaxation time distribution function uses the equivalent circuit method to convert the electrochemical impedance spectroscopy data into an expression of multiple differential resistances and capacitances in parallel. The formula is as follows: ， Where Z(ω) is the total impedance of the battery; n is the number of parallel structures of differential resistance and capacitance; dR i is the ith differential resistance; j is the imaginary unit; ω is the angular frequency; C i is the i-th capacitor; dτ i is the discrete relaxation time differential expression, which is: , Converting the above differential expression into integral form gives the relaxation time distribution function, which is as follows: ， Where Z(ω) is the total impedance of the battery, R0 is the ohmic impedance and is not affected by frequency; γ(τ) is the relaxation time distribution function; τ is the relaxation time; j is the imaginary unit; and ω is the angular frequency.

5. The method for detecting internal short circuit faults in a lithium battery cell according to claim 3, wherein: In step S4, the resistance change range of the single battery under different temperature conditions from the normal state to the fault state includes: Under normal cycle conditions, the internal resistance of the solid electrolyte interface film of a single cell is R SEI0 , the internal resistance of the solid electrolyte interface film of the single cell in the fault state is R SEI , the internal resistance variation of the solid electrolyte interface film α is , Under normal cycle conditions, the internal resistance of charge transfer of a single cell is R ct0 , the charge transfer internal resistance of the single battery in the fault state is R ct , the charge transfer internal resistance variation β is 。 6. The method for detecting internal short circuit faults in a lithium battery cell according to claim 1, wherein: The fitting function is a cubic function.

7. The method for detecting internal short circuit faults in a lithium battery cell according to claim 1, wherein: The single cell is a lithium battery.

8. A system for implementing the method according to any one of claims 1 to 7, characterized in that: It includes: A measuring unit, which is used to perform short-circuit induced cycle tests on single lithium batteries and electrochemical impedance spectroscopy tests under different temperature environments; A data unit is used to calculate the relaxation time distribution curve area of ​​the electrochemical impedance spectroscopy data of batteries at different temperatures, extract the charge transfer internal resistance and the solid electrolyte interface membrane internal resistance change amplitude to establish a database; A model unit, which uses the impedance characteristics at different temperatures established by the data unit as a threshold and establishes a database based on the temperature distribution through a fitting function; The detection unit is used to perform electrochemical impedance spectroscopy testing on the single battery to be tested after each cycle, and substitute the quantified extracted data into the established internal short circuit fault diagnosis model to obtain the fault status.

9. A computer storage medium, characterized in that The storage medium includes computer instructions, which, when executed on a computer, enable the computer to perform the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device comprises: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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