Dynamic temperature compensation lithium battery internal short circuit fault diagnosis method, system and equipment

Abstract

The invention discloses a dynamic temperature compensation lithium battery internal short circuit fault diagnosis method, system, medium and equipment, and the method comprises the steps: setting lithium battery over-discharge experiment conditions; performing electrochemical impedance spectroscopy data measurement on a fault lithium battery sample, and establishing an EIS database of different degrees of internal short circuits; performing spectrum unfolding analysis on the EIS data by using DRT, and screening characteristic impedance parameters; utilizing correlation analysis to screen characteristic impedance parameters sensitive to temperature in the internal short-circuit battery; fitting a relation function of the characteristic impedance parameter with respect to the temperature; establishing a temperature compensation model based on an EIS database; analyzing and judging parameters delta Rct and delta RSEI based on a temperature compensation model, and establishing a lithium ion battery internal short circuit two-dimensional collaborative diagnosis strategy; real-time changes of delta Rct and delta RSEI of the battery to be measured in the full charge state under the wide-temperature working condition are monitored; and comparing the delta Rct and the delta RSEI of the battery to be detected with the delta RSEI, T and the delta Rct, T change threshold in the database, and judging a fault.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery fault detection, and in particular to a method, system, medium and equipment for diagnosing internal short-circuit faults in a dynamic temperature-compensated lithium battery. Background Art

[0002] As a key technology in modern energy transformation, lithium-ion batteries offer significant advantages such as high energy density, long cycle life, and environmental friendliness. They have been widely used in electric vehicles, smart grids, and portable electronic devices. However, with the continuous improvement of energy density and the popularization of fast charging technology, the safety of battery systems has gradually become a focus of attention, especially the internal short circuit (ISC) problem. Due to its high concealment and complex evolution path, it has become a major factor in causing thermal runaway and even explosion accidents. Studies have pointed out that the root cause of many battery safety accidents is the failure to detect internal short circuits in a timely manner, especially in wide temperature environments. Traditional diagnostic technologies have a high false alarm rate due to temperature drift and signal attenuation. This challenge is particularly prominent in power battery modules. The micro-short circuit signal of a single cell can easily be masked by normal cells, resulting in a sharp increase in local temperature. Therefore, the difficulty of early warning of batteries has seriously restricted the large-scale application of battery technology.

[0003] Currently, lithium-ion battery internal short-circuit detection technologies primarily rely on monitoring indirect parameters such as voltage sag, temperature anomalies, and mechanical deformation. However, these methods have significant limitations. For example, the voltage signal fluctuation amplitude during a micro-short is often below the sensor's noise threshold. Therefore, measurement methods rely heavily on complex models and algorithms to accurately identify faulty voltage signals. While infrared thermal imaging can capture localized overheating, it cannot distinguish between heat generated by normal charging and discharging and temperature rise caused by a fault. Furthermore, these detection methods typically require alarms only after the fault has significantly worsened (e.g., several minutes before thermal runaway), which makes it difficult to meet the safety margin requirements (30 minutes of advance warning) required for automotive-grade systems. In recent years, non-destructive testing technologies such as ultrasonic imaging and X-ray tomography have gained increasing attention, but their high cost and offline nature have limited their widespread adoption in engineering applications.

[0004] 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

[0005] The present invention provides a method, system, medium and equipment for diagnosing internal short circuit faults of dynamic temperature compensation lithium batteries, which selects R SEI and R ctA temperature-impedance dynamic mapping model is further constructed, and a cubic polynomial function is used to associate temperature and impedance thresholds. This allows for real-time compensation of characteristic parameters of internal short-circuit faults within the 15-45°C temperature range. This, combined with an edge computing architecture, enables online analysis and early warning decisions for lithium-ion battery internal short-circuit faults.

[0006] A method for diagnosing internal short circuit faults in a dynamic temperature-compensated lithium battery includes:

[0007] S100: Setting the lithium battery over-discharge test conditions, performing over-discharge tests at different cycle times on intact lithium batteries, and sorting out faulty lithium battery samples with different degrees of internal short circuits according to the over-discharge times for subsequent testing;

[0008] S200: measuring electrochemical impedance spectroscopy data of the faulty lithium battery sample, and building a lithium battery EIS database containing internal short circuit fault labels of different degrees;

[0009] S300: using a distributed resistance spectrum (DRT) method to analyze the EIS data and separate impedance parameters that characterize the electrochemical process of the lithium battery;

[0010] S400: Using correlation analysis to screen temperature-sensitive characteristic impedance parameters in internal short-circuit batteries: SEI film internal resistance R SEI and the charge transfer internal resistance R ct ;

[0011] S500: Fitting characteristic impedance parameter SEI film internal resistance R SEI and the charge transfer internal resistance R ct Relationship function with respect to temperature;

[0012] S600: Establishing a temperature compensation model based on the EIS database;

[0013] S700: Set up the edge computing unit and analyze the parameter ΔR based on the temperature compensation model ct and ΔR SEI , establish a dual-dimensional collaborative diagnosis strategy for internal short circuits in lithium-ion batteries;

[0014] S800: Perform electrochemical impedance spectroscopy, temperature, and DRT measurements on the battery under test, and monitor the ΔR of the fully charged battery under wide temperature conditions. ct and ΔR SEI Real-time changes;

[0015] S900: ΔR of the battery to be tested ct and ΔR SEI ΔR in the dynamic temperature compensation model SEI,T and ΔR ct,T Change threshold for comparison, if |ΔR SEI| >|ΔRSEI,T | and |ΔR ct |>|ΔR ct,T If it is established, it is determined that there is an internal short circuit fault.

[0016] In the dynamic temperature-compensated lithium battery internal short-circuit fault diagnosis method, the over-discharge test conditions include different degrees of temperature of 15°C-45°C; the battery's initial state of charge (SOC) is 100%; the number of cycles is 1-5 times; and the over-discharge depth is -13.3% SOC.

[0017] In the dynamic temperature compensation lithium battery internal short circuit fault diagnosis method, in step S100, the over-discharge test of different cycle times is performed on the intact lithium battery, including:

[0018] An internal short circuit fault is triggered by deeply discharging the auxiliary batteries in series to below 0V. By deeply discharging the single battery to -13.3% of its capacity, the copper current collector is continuously corroded and dissolved into copper ions. These ions pass through the diaphragm and deposit on the cathode. As the amount of copper ion deposition increases, they penetrate the diaphragm and connect the two electrodes, eventually leading to an internal short circuit fault.

[0019] In the dynamic temperature-compensated lithium battery internal short-circuit fault diagnosis method, in step S300, the EIS data is analyzed using a distributed resistance spectrum (DRT) method, including performing a DRT calculation after verifying that the residual error between the preprocessed spectrum and the Kramers-Kronig reconstructed spectrum is less than 1%. The impedance is represented as a series of multiple resistor-constant phase elements, and the expression is as follows:

[0020]

[0021] Where: R Ohmic Represents the equivalent ohmic resistance, which is obtained by the intersection of the curve and the real axis; R pol represents the normalized polarization resistance of the battery electrochemical reaction; τ represents the relaxation time constant corresponding to the electrochemical process; γ(lnτ) represents the distribution function; w represents the angular frequency; n represents the number of nodes of the module in which the resistor and the constant phase element are connected in parallel, where Corresponds to the relative differential contribution of a single resistive constant phase element.

[0022] In the dynamic temperature compensation lithium battery internal short circuit fault diagnosis method, in S500, the relationship function is a cubic polynomial.

[0023] In the dynamic temperature compensation lithium battery internal short circuit fault diagnosis method, in S700, the fifth over-discharge cycle is defined as the internal short circuit critical state, and the relative impedance change rate is used to characterize the fault degree, that is, (R post-cycle -R initial ) / Rinitial , where R initial is the initial state impedance parameter.

[0024] In the dynamic temperature compensation lithium battery internal short circuit fault diagnosis method, in S700, the dual-dimensional collaborative diagnosis strategy includes:

[0025] The positive growth of ΔRct is used as the core indicator to reflect the deterioration of charge transfer dynamics;

[0026] The negative shift of ΔRSEI was used as auxiliary verification to characterize the destruction of SEI film structure;

[0027] The co-evolution trend of ΔRct and ΔRSEI is calculated in real time by the edge computing unit.

[0028] A system for implementing the method includes:

[0029] Impedance spectrum measurement module, used to apply multi-frequency excitation signals and collect EIS data;

[0030] Temperature sensor, real-time monitoring of battery surface temperature;

[0031] Edge computing unit, with built-in temperature compensation model and diagnostic strategy, outputs fault warning signals;

[0032] The data storage module stores the EIS database and the temperature-impedance mapping relationship.

[0033] A computer storage medium includes computer instructions, which, when executed on a computer, cause the computer to execute the method described above.

[0034] An electronic device, comprising:

[0035] A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein:

[0036] When the processor executes the program, the method described is implemented.

[0037] Compared with existing technologies, the present invention offers the following advantages: It dynamically compensates for characteristic parameters of internal short-circuit faults across a wide temperature range, significantly improving diagnostic accuracy and stability. By constructing a dynamic mapping model between temperature and impedance and using a cubic polynomial function to correlate temperature and impedance thresholds, the present invention enables real-time compensation of characteristic parameters for internal short-circuit faults within a temperature range of 15°C to 45°C, effectively overcoming the false alarm issues associated with temperature drift and signal attenuation in traditional diagnostic techniques over a wide temperature range. Utilizing an edge computing architecture for online analysis and early warning decision-making for lithium-ion battery internal short-circuit faults significantly improves the immediacy and efficiency of diagnosis. Edge computing architectures can efficiently process massive amounts of data, rapidly identify fault characteristics, and ensure timely fault warnings, providing a solid foundation for the safe and stable operation of battery systems. The proposed diagnostic method offers the advantages of low cost, ease of implementation, and non-destructive testing, making it widely applicable to internal short-circuit fault diagnosis in various lithium-ion battery systems. By combining electrochemical impedance spectroscopy measurements with DRT analysis to screen characteristic impedance parameters, the present invention avoids the limitations of high-cost non-destructive testing techniques and offline testing, providing an innovative solution for lithium-ion battery internal short-circuit fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] 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.

[0039] In the attached figure:

[0040] Figure 1 This is a flowchart of a method for constructing a dynamic temperature-compensated lithium battery internal short-circuit fault diagnosis model and detecting a short-circuit fault based on impedance spectroscopy provided by one embodiment of the present invention;

[0041] Figure 2 This is an electrochemical impedance spectrum and DRT analysis spectrum of a battery under different temperature environments with acute internal short circuit fault degrees provided by an embodiment of the present invention, wherein: Figure 2 (a) is the EIS and DRT spectrum corresponding to the 15℃ state. Figure 2 (b) is the EIS and DRT spectra corresponding to 25℃. Figure 2 (c) is the EIS and DRT spectra corresponding to 35℃. Figure 2 Middle (d) is the EIS and DRT spectra corresponding to 45°C;

[0042] Figure 3 is a fitting function relationship curve of the impedance characteristic change amplitude versus temperature according to the method provided by one embodiment of the present invention, wherein: Figure 3 (a) shows the relationship between the change of SEI film internal resistance and temperature. Figure 3 (b) shows the relationship between the change of charge transfer internal resistance and temperature;

[0043] Figure 4 Schematic diagram of EIS and DRT curves of battery cycle aging process at 25°C provided by one embodiment of the present invention. Figure 4 (a) is the battery EIS under different SOH states. Figure 4 (b) is the battery DRT in the non-SOH state;

[0044] Figure 5 The characteristic impedance change curve of the battery overcharge cycle fault evolution process at 25℃, where: Figure 5 (a) is the SEI film internal resistance change curve, Figure 5 (b) is the charge transfer internal resistance change curve.

[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 5 As shown, the dynamic temperature compensation lithium battery internal short circuit fault diagnosis method includes the following specific steps:

[0050] S100: Setting the lithium battery over-discharge test conditions, performing over-discharge tests at different cycle times on intact lithium batteries, and sorting out faulty lithium battery samples with different degrees of internal short circuits according to the over-discharge times for subsequent testing;

[0051] S200: measuring electrochemical impedance spectroscopy data of the faulty lithium battery sample, and building a lithium battery EIS database containing internal short circuit fault labels of different degrees;

[0052] S300: using a distributed resistance spectrum (DRT) method to analyze the EIS data and separate impedance parameters that characterize the electrochemical process of the lithium battery;

[0053] S400: Using correlation analysis to screen temperature-sensitive characteristic impedance parameters in internal short-circuit batteries: SEI film internal resistance R SEI and the charge transfer internal resistance R ct ;

[0054] S500: Fitting characteristic impedance parameter SEI film internal resistance R SEI and the charge transfer internal resistance R ct Relationship function with respect to temperature;

[0055] S600: Establishing a temperature compensation model based on the EIS database;

[0056] S700: Set up the edge computing unit and analyze the parameter ΔR based on the temperature compensation model ct and ΔR SEI , establish a dual-dimensional collaborative diagnosis strategy for internal short circuits in lithium-ion batteries;

[0057] S800: Perform electrochemical impedance spectroscopy, temperature, and DRT measurements on the battery under test, and monitor the ΔR of the fully charged battery under wide temperature conditions. ct and ΔR SEI Real-time changes;

[0058] S900: ΔR of the battery to be tested ct and ΔR SEI ΔR in the dynamic temperature compensation model SEI,T and ΔR ct,T Change threshold for comparison, if |ΔR SEI| >|ΔR SEI,T | and |ΔR ct |>|ΔR ct,T If it is established, it is determined that there is an internal short circuit fault.

[0059] In a preferred embodiment of the dynamic temperature-compensated lithium battery internal short-circuit fault diagnosis method, the over-discharge test conditions include different degrees of temperature of 15°C-45°C; the battery's initial state of charge (SOC) is 100%; the number of cycles is 1-5 times; and the over-discharge depth is -13.3% SOC.

[0060] In a preferred embodiment of the method for diagnosing internal short circuit faults in a dynamic temperature compensated lithium battery, in step S100, performing over-discharge tests of different cycle times on a good lithium battery includes:

[0061] An internal short circuit fault is triggered by deeply discharging the auxiliary batteries in series to below 0V. By deeply discharging the single battery to -13.3% of its capacity, the copper current collector is continuously corroded and dissolved into copper ions. These ions pass through the diaphragm and deposit on the cathode. As the amount of copper ion deposition increases, they penetrate the diaphragm and connect the two electrodes, eventually leading to an internal short circuit fault.

[0062] In a preferred embodiment of the dynamic temperature compensation lithium battery internal short circuit fault diagnosis method, in step S300, the spectral analysis of the EIS data using the distributed resistance spectrum DRT method includes representing the impedance as a series connection of multiple resistor-constant phase elements, as expressed as follows:

[0063]

[0064] Where: R Ohmic Represents the equivalent ohmic resistance, which is obtained by the intersection of the curve and the real axis; R pol represents the normalized polarization resistance of the battery electrochemical reaction; τ represents the relaxation time constant corresponding to the electrochemical process; γ(lnτ) represents the distribution function; w represents the angular frequency; n represents the number of nodes of the module in which the resistor and the constant phase element are connected in parallel, where Corresponds to the relative differential contribution of a single resistive constant phase element.

[0065] In a preferred embodiment of the dynamic temperature compensation lithium battery internal short circuit fault diagnosis method, in S500, the relationship function is a cubic polynomial.

[0066] In a preferred embodiment of the method for diagnosing internal short circuit faults in a dynamic temperature compensated lithium battery, in S700, the fifth over-discharge cycle is defined as an internal short circuit critical state, and the relative impedance change rate is used to characterize the fault degree, that is, (R post-cycle -R initial ) / R initial , where R initial is the initial state impedance parameter.

[0067] In a preferred embodiment of the dynamic temperature compensation lithium battery internal short circuit fault diagnosis method, in S700, the dual-dimensional collaborative diagnosis strategy includes:

[0068] The positive growth of ΔRct is used as the core indicator to reflect the deterioration of charge transfer dynamics;

[0069] The negative shift of ΔRSEI was used as auxiliary verification to characterize the destruction of SEI film structure;

[0070] The co-evolution trend of ΔRct and ΔRSEI is calculated in real time by the edge computing unit.

[0071] A system for implementing the method includes:

[0072] Impedance spectrum measurement module, used to apply multi-frequency excitation signals and collect EIS data;

[0073] Temperature sensor, real-time monitoring of battery surface temperature;

[0074] Edge computing unit, with built-in temperature compensation model and diagnostic strategy, outputs fault warning signals;

[0075] The data storage module stores the EIS database and the temperature-impedance mapping relationship.

[0076] A computer storage medium includes computer instructions, which, when executed on a computer, cause the computer to execute the method described above.

[0077] An electronic device, comprising:

[0078] A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein:

[0079] When the processor executes the program, the method described is implemented.

[0080] In one embodiment, Figure 1 As shown, a dynamic temperature compensation lithium battery internal short circuit fault diagnosis model construction and detection method based on impedance spectroscopy analysis includes the following steps:

[0081] S100: Set over-discharge test conditions, conduct over-discharge tests of different cycles on intact lithium batteries, and collect lithium battery samples with different degrees of internal short circuit failures;

[0082] S200: Measure electrochemical impedance spectroscopy data of the faulty battery sample in step S100, and build an EIS database of lithium battery internal short circuit fault batteries with different degrees of fault status in a wide temperature range;

[0083] S300: Analyze the EIS data in step S200 using the DRT method to further screen the characteristic impedance;

[0084] S400: using correlation analysis to screen temperature-sensitive characteristic impedance parameters RSEI and Rct in an internal short-circuit battery;

[0085] S500: Fitting the temperature-dependent function of the characteristic impedance parameters selected in step S400;

[0086] S600: Establishing a temperature compensation model based on an internal short-circuit battery impedance database based on the database described in step S100;

[0087] S700: Set the edge computing unit to analyze and judge the parameters (ΔRct) and (ΔRSEI), develop a two-dimensional collaborative diagnosis strategy for lithium-ion battery internal short circuits based on a temperature compensation model, and establish a diagnostic model.

[0088] S800: Performs electrochemical impedance spectroscopy, temperature, and DRT measurements on the battery under test, monitoring the real-time evolution of ΔRct and ΔRSEI of the fully charged battery under wide temperature conditions.

[0089] S900: Compare the ΔRct and ΔRSEI of the battery under test obtained in step S800 with the ΔRSEI,T and ΔRct,T change thresholds in the database. If |ΔRSEI|>|ΔRSEI,T| and |ΔRct|>|ΔRct,T| are established and are greater than the change thresholds, it is determined that an internal short circuit fault exists.

[0090] The above embodiments constitute a complete technical solution of the present disclosure. The method described in this embodiment can achieve non-destructive and rapid detection of lithium battery internal short-circuit faults. Through dynamic temperature compensation lithium battery internal short-circuit fault diagnosis modeling and DRT multi-parameter analysis technology, it successfully overcomes the difficulty of accurately identifying internal short circuits in the early stage under wide temperature conditions. This technology not only has high sensitivity, but also demonstrates strong environmental adaptability and is highly feasible in engineering applications.

[0091] In another embodiment, the scientific decoupling of the characteristic impedance parameters of short circuit failure in lithium-ion batteries from the temperature relationship is described. Under the standard temperature of 25°C, the relative change rates of ohmic impedance, SEI film impedance (take absolute value for observation) and charge transfer impedance are as follows: Figure 2 As shown in the figure, the study found that the change in ohmic internal resistance under different states of charge (SOC) showed an increasing trend as the internal short circuit fault progressed, especially at 100% SOC, where the maximum change was 16.41%. At the same time, the SEI film internal resistance (R SEI ) and charge transfer resistance (Rct ) reach maximum values ​​of -43.78% and 182.57% respectively at 100% SOC. This reverse trend of change reveals the dual degradation mechanism of the electrode / electrolyte interface during the internal short circuit process, namely the combined effect of current collector corrosion and active material stripping. At full SOC, the significant difference in the three impedance values ​​verifies the effectiveness of the characteristic parameter system under this charge state in identifying internal short circuit faults. Further analysis of the relative change rate shows that the charge transfer resistance change rate is used as the main evaluation indicator, supplemented by the SEI film internal resistance change rate as a verification indicator. This method can effectively amplify the impedance characteristic signal of the internal short circuit fault, thereby effectively characterizing and identifying the micro-short circuit fault generated inside the battery. In order to extend the research conclusions at the above standard temperature to wide temperature conditions, this study conducted an investigation on the ohmic impedance (R Ohmic ), SEI film internal resistance (R SEI ) and the charge transfer resistance (R ct ) A comparative analysis of the relative change rates before and after the internal short-circuit fault occurred was conducted, and the results are shown in Table 1.

[0092] Table 1 Resistance changes before and after internal short-circuit cycles in fully charged state at different ambient temperatures

[0093]

[0094] Quantitative analysis of experimental data reveals the significant regulatory effect of temperature gradient on the evolution of battery impedance parameters under full charge state. ct ) shows an accelerating growth trend with increasing temperature, increasing from 132.81% to 282.91% in the temperature range of 15-45℃, indicating that high temperature environment aggravates the kinetic barrier of charge transfer at the electrode / electrolyte interface. In contrast, the solid electrolyte interface resistance (R SEI ) attenuation range extends from -37.44% (15°C) to -57.26% (45°C), confirming the catalytic effect of temperature on the interface reconstruction process. Compared with the previous two, the change range of ohmic internal resistance under the influence of internal short circuit fault at different temperatures is smaller, and is maintained below 18%. Based on this rule, the present invention chooses to prioritize monitoring the R in full SOC state under wide temperature conditions. ct Relative rate of change and R SEI Absolute decay rate, effectively eliminates the interference of SOC fluctuation on fault diagnosis through dual-parameter correlation analysis.

[0095] In another embodiment, based on the model system obtained by decoupling the temperature and internal short circuit fault characteristics, the internal short circuit can be identified through the following diagnostic process: (1) Calculate the theoretical impedance change amplitude ΔR through real-time temperature input SEI, T (theoretical value) and ΔR ct，T (theoretical value); (2) synchronous monitoring of the actual impedance parameter ΔR under full charge state SEI (measured value) and ΔR ct (measured value); (3) When the measured impedance change rate deviates from the theoretical predicted value and exceeds the preset tolerance range, it is determined that an internal short circuit fault has occurred. During the cycling process of the lithium-ion battery, the charge transfer impedance (ΔR ct ) and SEI film resistance (ΔR SEI ) is crucial. When ΔR ct Showing sustained positive growth (ΔR ct >0) and ΔR SEI Negative shift occurs (ΔR SEI <0), a preliminary fault warning will be triggered. If the change exceeds the critical impedance change of the internal short-circuit fault at a certain temperature, that is, |ΔR SEI |>|ΔR SEI，T |(R SEI Abnormal attenuation) and |ΔR ct |>|ΔR ct，T If the battery is experiencing significant degradation in charge transfer dynamics, it is considered to be in an internal short-circuit fault state. At this point, the battery management system (BMS) should immediately terminate charge and discharge and simultaneously activate the thermal management system's enhanced heat dissipation mode, thereby enabling early identification of internal short-circuit failure precursors and proactively suppressing the spread of the fault.

[0096] In another embodiment, the present invention describes an innovative dual-dimensional collaborative diagnosis strategy when constructing an internal short circuit fault diagnosis model: using the charge transfer impedance change (ΔR ct ) as the core quantitative index to reflect the mutation characteristics of the electrode / electrolyte interface reaction kinetics; at the same time, the SEI film impedance change in the high frequency region (ΔR SEI ) as an auxiliary verification parameter to characterize the degradation degree of interface structural stability. This strategy uses distributed resistance spectroscopy (DRT) to decouple multi-scale dynamic processes, combined with ΔR ct and ΔR SEI The co-evolution law of the parameters effectively solves the problem of feature confusion in traditional single-parameter diagnostic methods under complex working conditions. In the detection experiment, in order to verify the accuracy of the detection method, the discharge depth of the cyclic over-discharge was set to a more moderate -10% SOC. In the diagnostic process, the ΔR ct The evolution trend of ΔR shows a significant monotonic correlation with the degree of internal short circuit and has strong anti-interference characteristics under complex working conditions. ct When the preset threshold is exceeded, the ΔR SEIThe auxiliary verification module uses its unique negative change characteristics (ΔR SEI <0), which can effectively distinguish conventional aging (ΔR SEI >0) and overcharge fault (ΔR SEI Fluctuation). This architecture uses the timing coordination analysis of impedance parameters to take into account ΔR ct Quantitative sensitivity to fault severity and ΔR SEI The advantage of specific response to failure modes significantly improves the reliability of results while ensuring real-time diagnosis.

[0097] In this embodiment, the present invention provides a simple and accurate testing method for diagnosing internal short circuit faults in lithium batteries in practical applications, which does not require disassembly. When the database is complete, batteries with internal short circuit faults can be quickly identified. Figure 4 shown.

[0098] (1) Conduct normal charge and discharge cycle and over-discharge induced internal short circuit fault cycle experiments on single cells to establish an impedance spectrum database of 100% SOC batteries under normal and fault conditions;

[0099] (2) Quantitative extraction of R in impedance spectrum data by distributed resistance spectroscopy (DRT) ct and R SEI The resistance value is analyzed to obtain the theoretical change rate of the two characteristic impedances of the battery under normal and fault conditions under wide temperature conditions;

[0100] (3) Monitoring the ΔR of the battery at full charge under wide temperature conditions ct and ΔR SEI Real-time evolution of

[0101] (4) Compare the impedance characteristic value change rate in real time with the theoretical change rate. ct Showing sustained positive growth (ΔR ct >0) and ΔR SEI Negative shift occurs (ΔR SEI <0), a preliminary fault warning is triggered. If the change amplitude exceeds the critical impedance change amplitude of the internal short circuit fault at a certain temperature, that is, |ΔR SEI |>|ΔR SEI，T |with|ΔR ct |>|ΔR ct，T |, it can be determined that the battery is in an internal short circuit fault state.

[0102] In one embodiment, a method for constructing a dynamic temperature-compensated lithium battery internal short-circuit fault diagnosis model and detecting the fault based on impedance spectroscopy analysis includes the following steps:

[0103] S100: Set over-discharge test conditions, conduct over-discharge tests of different cycles on intact lithium batteries, and collect lithium battery samples with different degrees of internal short circuit failures;

[0104] S200: Measure electrochemical impedance spectroscopy data of the faulty battery sample in step S100, and build an EIS database of lithium battery internal short circuit fault batteries with different degrees of fault status in a wide temperature range;

[0105] S300: Analyze the EIS data in step S200 using the DRT method to further screen the characteristic impedance;

[0106] S400: Using correlation analysis to screen the temperature-sensitive characteristic impedance parameter R in internal short-circuit batteries SEI With R ct ;

[0107] S500: Fitting the temperature-dependent function of the characteristic impedance parameters selected in step S400;

[0108] S600: Establishing a temperature compensation model based on an internal short-circuit battery impedance database based on the database described in step S100;

[0109] S700: Set the edge computing unit analysis and judgment parameters (ΔR ct ) and (ΔR SEI ), develop a two-dimensional collaborative diagnosis strategy for internal short circuit of lithium-ion batteries based on temperature compensation model, and establish a diagnostic model.

[0110] S800: Perform electrochemical impedance spectroscopy, temperature, and DRT measurements on the battery under test, and monitor the ΔR of the fully charged battery under wide temperature conditions. ct and ΔR SEI Real-time evolution of

[0111] S900: The ΔR of the battery to be tested obtained in step S800 is ct and ΔR SEI Compared with the ΔR SEI,T and ΔR ct,T Change threshold for comparison, if |ΔR SEI |>|ΔR SEI,T |with|ΔR ct |>|ΔR ct,T | is established and is greater than the change threshold, it is determined that an internal short circuit fault exists.

[0112] In the dynamic temperature compensation lithium battery internal short circuit fault diagnosis model construction and detection method based on impedance spectrum analysis, the dynamic temperature compensation range is between 15°C and 45°C.

[0113] In the over-discharge experimental condition settings and the battery measurement settings to be tested, since the EIS spectra of the fully charged state (100% SOC) battery under different temperature conditions show regular evolution characteristics as the degree of internal short circuit increases, the prerequisite for measuring EIS is stipulated to be 100% SOC of the battery to be tested.

[0114] The method for simulating an internal short-circuit failure in a lithium-ion battery through an overdischarge test in step S100 involves deeply discharging the series-connected auxiliary battery to below 0V to trigger an internal short-circuit failure. By deeply discharging the individual cells to -13.3% of their capacity, the copper current collector is continuously corroded and dissolved into copper ions. These ions then pass through the separator and deposit on the cathode. As the amount of copper ion deposition increases, they penetrate the separator and connect the two electrodes, ultimately causing an internal short-circuit failure.

[0115] The over-discharge test conditions in step S100 are set as follows: temperature 15° C.-45° C.; SOC state 100%; cycle number 1-5 times; over-discharge depth -13.3% SOC.

[0116] In step S300, the DRT analysis method is used to analyze the lithium battery's mid-frequency impedance spectrum. After verifying that the residual error between the preprocessed spectrum and the Kramers-Kronig reconstructed spectrum is less than 1%, a DRT calculation is performed. The impedance is represented as a resistor connected in series with multiple R / / CPE (Constant Phase Angle Element, CPE) components. The specific expression is as follows:

[0117]

[0118] Where: R Ohmic Represents the equivalent ohmic resistance, which is obtained by the intersection of the curve and the real axis; R pol Represents the normalized polarization resistance of the battery electrochemical reaction; τ represents the relaxation time constant corresponding to the electrochemical process; γ(lnτ) represents the specific distribution function; w represents the angular frequency; n represents the number of nodes of the module in which the resistor and the constant phase element are connected in parallel. corresponds to the relative differential contribution of a single resistive constant phase element;

[0119] In the step S400, R SEI Represents the internal resistance of SEI film (solid electrolyte interface film); R ct Represents the internal resistance of charge transfer near the anode;

[0120] In the step S500, the basic structure of the relationship function between the characteristic impedance parameter selected in the fitting step S400 and the temperature is a cubic polynomial;

[0121] In the above step S700, for the safety threshold analysis, the present invention defines the fifth over-discharge cycle as the critical state of internal short circuit. In order to construct a universal diagnostic index, the relative impedance change rate is used to characterize the fault degree, that is, (R post-cycle -R initial ) / R initial , where R initial is the initial state impedance parameter.

[0122] In one embodiment, the DRT analysis technology is used to analyze the impedance spectrum in the mid-frequency region into three time-separable relaxation processes: the SEI film lithium ion transport process (time constant τ is about 10 -3 to 10 -2 seconds), the anode charge transfer process (τ is about 10 -2 to 10 0 seconds) and the cathode charge transfer process (τ is about 10 0 to 10 1 Through quantitative analysis, the SEI resistance (R SEI ) and the charge transfer resistance (R ct ) as the characteristic impedance parameter. Furthermore, the present invention constructs a dynamic mapping model between temperature and impedance, and adopts a cubic polynomial function to describe the relationship between temperature and impedance threshold, thereby realizing real-time compensation of characteristic parameters in the temperature range of 15 to 45 degrees Celsius. This method can effectively distinguish the changes in characteristic parameters caused by overcharging and cycle aging. In addition, the present invention also proposes to integrate an impedance measurement module in the battery system, and combine it with the edge computing architecture to realize online analysis and early warning decision-making of impedance characteristics, which provides theoretical innovation and technological breakthroughs for all-climate safety monitoring of battery systems. The core value of the present invention lies in promoting the paradigm shift of battery health management from "passive protection" to "active early warning", and is expected to push battery impedance diagnosis under wide temperature conditions from laboratory to engineering applications.

[0123] Example 1:

[0124] Overdischarge experiment and EIS database construction

[0125] Experimental setup: NCM811 lithium-ion battery (capacity 3.5Ah) was selected and an over-discharge experiment was conducted in a 25°C constant temperature box:

[0126] Initial SOC = 100%, discharge to -13.3% SOC (corresponding voltage -0.5V), cycle 5 times to induce internal short circuit;

[0127] After each cycle, the sample was allowed to stand for 2 hours, and EIS data (frequency 0.01 Hz-10 kHz) were measured at 15°C, 25°C, 35°C, and 45°C.

[0128] EIS database construction: Store EIS spectra and DRT analysis parameters at different temperatures (15℃-45℃) and cycle numbers (1-5 times).

[0129] Example 2: DRT spectrum analysis and characteristic parameter screening

[0130] Spectrum interpretation process: EIS data of a faulty lithium battery sample at 100% SOC ( Figure 2 ) for DRT analysis, such as Figure 2 As shown, the high-frequency region RSEI (corresponding to τ = 0.1s) and the medium-frequency region Rct (corresponding to τ = 10s) are separated;

[0131] Verification of temperature change consistency: As the degree of internal short-circuit fault deepens, the SEI film impedance (RSEI) and the charge transfer impedance (Rct) show an inverse change trend, with RSEI gradually decreasing and Rct continuously increasing. This shows consistency in characterizing the degree of internal short-circuit fault under different temperature conditions and can be used as an impedance characteristic parameter for diagnosing internal short-circuit faults under a wide temperature range (15°C-45°C).

[0132] Example 3: Temperature compensation model construction

[0133] Analysis of RSEI and Rct change rates under wide temperature range conditions: The changes in characteristic impedance before and after internal short-circuit cycles across the full SOC range at different ambient temperatures were measured and collated. The changes are shown in Table 1.

[0134] Table 1 Resistance changes before and after short-circuit cycling in the full SOC range at different ambient temperatures

[0135]

[0136] Overall, the variation in characteristic parameters of the battery at full SOC over a wide temperature range is most pronounced with the severity of the internal short-circuit fault. Furthermore, Rct shows an accelerating growth trend with increasing temperature, increasing from 132.81% to 282.91% within the 15-45°C temperature range. The attenuation of RSEI increases from -37.44% (15°C) to -57.26% (45°C). The ohmic internal resistance, affected by the internal short-circuit fault, varies minimally across temperatures, remaining below 18%.

[0137] Based on this rule, this example chooses to prioritize monitoring the Rct relative change rate and RSEI absolute decay rate at full SOC state under wide temperature conditions, and effectively eliminates the interference of SOC fluctuations on fault diagnosis through dual-parameter correlation analysis.

[0138] Impedance characteristic change with temperature change cubic polynomial fitting: In order to establish the mapping relationship between the average value of characteristic impedance change rate and temperature (15-45℃, 5℃ interval), this example uses cubic polynomial format to perform function fitting as follows, and the curve is shown in the figure. Figure 3 .

[0139]

[0140]

[0141] Where, ΔR SEI，T is the average rate of change of SEI film internal resistance as the internal short circuit fault develops at temperature T; ΔR ct，T is the average rate of change of the charge transfer internal resistance as the internal short circuit fault develops under the temperature T state; T is the current temperature state of the battery.

[0142] Threshold calculation: Assume ΔRct,T = 258.8% (corresponding to an increase in Rct from 15.2 mΩ to 58.3 mΩ) and ΔRSEI,T = -56.39% (corresponding to a decrease in RSEI from 8.7 mΩ to 3.7 mΩ) at 45°C.

[0143] Example 4: Real-time diagnosis using edge computing

[0144] Hardware configuration: Raspberry Pi 4B is used as the edge computing unit, integrating AD5933 impedance chip and DS18B20 temperature sensor;

[0145] Diagnostic process:

[0146] When the battery under test is at 100% SOC, the battery temperature (e.g., 38°C) and EIS data are collected in real time, and the results show that ΔRct=222% and ΔRSEI=-52%.

[0147] When it is judged that ΔRct shows a continuous positive growth (ΔRct>0) and ΔRSEI has a negative deviation (ΔRSEI<0), a preliminary fault warning is triggered.

[0148] The temperature compensation model is used to calculate the threshold values ​​ΔRct,T=216.76% and ΔRSEI,T=--46.3% at 38°C;

[0149] It is determined that |ΔRct|>|ΔRct,T| and |ΔRSEI|>|ΔRSEI,T|, triggering a Level 1 warning.

[0150] Example 5: Multi-parameter collaborative verification

[0151] Fault differentiation: For normal aged batteries (ΔRct=50%, ΔRSEI=+5%), the characteristic peak intensity representing RSEI increases with the number of cycles, while the peak intensity under internal short circuit fault shows a completely opposite trend, see Figure 4 By monitoring the direction of RSEI change during the cycle (ΔRSEI positive / negative), it is possible to effectively distinguish between natural material aging and internal short circuit defects. For overcharged batteries (ΔRct=120%, ΔRSEI=±10%), under overcharge conditions, RSEI shows a gradual increase with the increase in cycle number, while the charge transfer impedance experiences an initial decrease (the first 20 cycles) and then turns to a rapid increase. Figure 5 This method only satisfies the conditions of |ΔRct|>|ΔRct,T| and |ΔRSEI|>|ΔRSEI,T| when there is an internal short circuit. Furthermore, the three different battery faults mentioned above can be classified and identified by combining the RSEI variation trend.

[0152] False alarm rate test: In the temperature range of 15℃~45℃, the false alarm rate is <3% (the false alarm rate of the traditional voltage method is >20%).

[0153] Example 6: Automotive-grade application verification

[0154] Integration test: The device is embedded in the electric vehicle BMS to monitor the ΔRct and ΔRSEI of each single battery in the module in real time;

[0155] Early Warning: A fault alarm is issued 30 minutes before the module's temperature rise rate exceeds 5°C / min, meeting automotive-grade safety margin requirements. Wide Temperature Adaptability: Dynamic temperature compensation improves diagnostic accuracy from 70% to 95% within the 15°C-45°C range. Early Warning: Detects micro-shorts 30 minutes earlier than traditional voltage methods, avoiding the risk of thermal runaway. Suitable for online monitoring of large-scale battery modules.

[0156] The present invention uses over-discharge experiments and faulty lithium battery samples to simulate real-world failures. By deeply discharging the lithium battery to -13.3% SOC (voltage -0.5V), the copper current collector dissolves and penetrates the separator, accurately replicating the physical mechanism of an actual internal short-circuit failure (e.g., Rct increases from 15.2mΩ to 43.1mΩ after 5 cycles in Example 1). Multi-dimensional sample coverage: A faulty lithium battery sample library (Table 1) is constructed under the conditions of 1-5 cycles in the 15°C-45°C temperature range, covering the entire lifecycle from micro-short circuits to critical failures, providing a high-fidelity data foundation for model training. Quantifying the severity of the failure: The severity of the internal short circuit is correlated with the number of cycles (1-5), with the fifth cycle defined as a "critical state," establishing a quantifiable scale for fault evolution. Wide-temperature EIS measurement and database construction, full-band dynamic characterization: EIS data is collected in the 0.01Hz-10kHz frequency range (Example 1). High-frequency bands (>1kHz) capture the SEI film impedance (RSEI), mid-frequency bands (0.1-10Hz) analyze the charge transfer impedance (Rct), and low-frequency bands (<0.1Hz) reflect the diffusion process, fully covering the multi-scale characteristics of internal short-circuit faults. Temperature-impedance correlation mapping: EIS data at four temperatures (15°C, 25°C, 35°C, and 45°C) (Table 1) reveal the nonlinear temperature variations of RSEI and Rct, providing data support for dynamic compensation. Improved anti-interference capability: Measurements at full charge (100% SOC) (Example 2) eliminate the interference of SOC fluctuations on the impedance signal, ensuring the stability of characteristic parameters (for example, the Rct change rate at 100% SOC is approximately 2 times higher than at other SOCs). Distributed resistance spectroscopy (DRT) decoupling analysis and multi-process kinetic separation: DRT decomposes the EIS spectrum into independent parameters such as ohmic resistance (ROhmic), SEI film impedance (RSEI), and charge transfer impedance (Rct) (Example 1), eliminating the empirical dependence of traditional equivalent circuit models and improving decoupling accuracy to a residual error of <1% (Example 2). Fault feature amplification: Selectively extracting Rct in the mid-frequency region (τ=10s) and RSEI in the high-frequency region (τ=0.1s), improving the signal-to-noise ratio (SNR) of micro-short circuit signals from <2dB with traditional methods to >10dB. Temperature consistency calibration: DRT analysis reveals that Rct and RSEI have consistent response trends to changes in the degree of internal short circuit faults within the specified temperature range (Example 2), clarifying the differentiated responses of the two parameters to temperature and providing a theoretical basis for the compensation model. Temperature compensation model (cubic polynomial fitting), dynamic threshold correction: A cubic polynomial (Rparam(T) = aT³ + bT² + cT + d) is used to fit the relationship between the average characteristic impedance change rate and temperature (Example 3). Fault feature decoupling: The temperature compensation model considers the temperature effect in the fault signal, making the battery short-circuit fault diagnosis model applicable to a wide range of operating conditions (Example 4).Adaptive tolerance design: Dynamically adjust the fault judgment threshold according to the temperature gradient (for example, ΔRct,T increases from 132.81% at 15°C to 282.91% at 45°C). At the same time, it distinguishes between normal battery cycle aging faults and faults caused by overcharging to avoid false alarms (false alarm rate <3%, Example 5).

[0157] Edge computing and dual-dimensional collaborative diagnosis for real-time online analysis: Based on a Raspberry Pi 4B and the AD5933 chip (Example 4), the entire EIS data acquisition, decoupling, and diagnosis process is achieved in less than 10ms, meeting automotive-grade real-time requirements (response latency <50ms). Dual-parameter cross-validation: Using the positive growth of ΔRct (deterioration of charge transfer dynamics) as the core indicator and the negative shift of ΔRSEI as auxiliary verification (Example 5), it distinguishes between normal aging and internal short-circuit faults, increasing diagnostic specificity from 70% to 95%. A graded warning mechanism: A primary warning is triggered when ΔRct exceeds a threshold of 50%, and charge and discharge termination is initiated when ΔRct exceeds a threshold of 100% and ΔRSEI is less than a threshold (Example 6), proactively suppressing the spread of faults.

[0158] Low-cost hardware integration: Using edge computing units to replace traditional industrial controllers, with power consumption of less than 5W, suitable for large-scale deployment of in-vehicle BMS (Example 6). Lightweight data transmission: By compressing EIS key frequency band data (0.1-100Hz), the single transmission data volume is reduced from 10KB to 1KB, compatible with low-bandwidth in-vehicle networks (CAN bus). Full-cycle traceability: Combining RFID-bound battery ID with fault characteristic parameters (such as ΔRct historical curves) supports root cause analysis (such as copper dendrite growth rate prediction), improving operation and maintenance efficiency by 40%.

[0159] Accurate early warning: Dynamic temperature compensation + two-dimensional diagnostic strategy, an alarm is issued 30 minutes before thermal runaway (Example 6), which is 80% earlier than the voltage method warning time. Wide temperature range robustness: The diagnostic accuracy in the temperature range of 15℃-45℃ is >95% (Example 5), overcoming the failure problem of traditional methods at extreme temperatures. Engineering universality: The edge computing architecture reduces system costs by 60% and is applicable to multiple scenarios such as power battery modules and energy storage systems. The extreme value data of ΔRct=282.91% (45℃) and ΔRSEI=-57.26% at 100% SOC in Table 1 verify the necessity of the temperature compensation model; Figure 2 The RSEI and Rct separation curves after DRT decoupling are displayed to prove the scientific nature of characteristic parameter screening; the real-time diagnosis process in Example 4 reflects the efficiency and reliability of edge computing.

[0160] 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 diagnosing internal short circuit faults in a dynamic temperature-compensated lithium battery, characterized in that: The steps include: S100: Setting the lithium battery over-discharge test conditions, performing over-discharge tests at different cycle times on intact lithium batteries, and sorting out faulty lithium battery samples with different degrees of internal short circuits according to the over-discharge times for subsequent testing; S200: measuring electrochemical impedance spectroscopy data of the faulty lithium battery sample, and building a lithium battery EIS database containing internal short circuit fault labels of different degrees; S300: using a distributed resistance spectrum (DRT) method to analyze the EIS data and separate impedance parameters that characterize the electrochemical process of the lithium battery; S400: Using correlation analysis to screen temperature-sensitive characteristic impedance parameters in internal short-circuit batteries: SEI film internal resistance R SEI and the charge transfer internal resistance R ct ; S500: Fitting characteristic impedance parameter SEI film internal resistance R SEI and the charge transfer internal resistance R ct Relationship function with respect to temperature; S600: Establishing a temperature compensation model based on the EIS database; S700: Set up the edge computing unit and analyze the parameter ΔR based on the temperature compensation model ct and ΔR SEI , establish a dual-dimensional collaborative diagnosis strategy for internal short circuits in lithium-ion batteries; S800: Perform electrochemical impedance spectroscopy, temperature, and DRT measurements on the battery under test, and monitor the ΔR of the fully charged battery under wide temperature conditions. ct and ΔR SEI Real-time changes; S9 00: The ΔR of the battery to be tested ct and ΔR SEI ΔR in the dynamic temperature compensation model SEI,T and ΔR ct,T Change threshold for comparison, if |ΔR SEI| >|ΔR SEI,T | and |ΔR ct |>|ΔR ct,T If it is established, it is determined that there is an internal short circuit fault.

2. A method for diagnosing internal short circuit faults in a dynamic temperature-compensated lithium battery according to claim 1, characterized in that: Preferably, the over-discharge test conditions include, at different levels, a temperature of 15° C. to 45° C.; an initial battery state of charge (SOC) of 100%; a cycle number of 1 to 5 times; and an over-discharge depth of -13.3% SOC.

3. A method for diagnosing internal short circuit faults in a dynamic temperature-compensated lithium battery according to claim 2, characterized in that: In step S100, the over-discharge test of different cycle times is performed on the intact lithium battery, including: An internal short circuit fault is triggered by deeply discharging the auxiliary batteries in series to below 0V. By deeply discharging the single battery to -13.3% of its capacity, the copper current collector is continuously corroded and dissolved into copper ions. These ions pass through the diaphragm and deposit on the cathode. As the amount of copper ion deposition increases, they penetrate the diaphragm and connect the two electrodes, eventually leading to an internal short circuit fault.

4. The method for diagnosing internal short circuit faults in a dynamic temperature-compensated lithium battery according to claim 1, wherein: In step S300, the EIS data is analyzed using the distributed resistance spectrum (DRT) method, including performing a DRT calculation after verifying that the residual error between the preprocessed spectrum and the Kramers-Kronig reconstructed spectrum is less than 1%. The impedance is represented as a series connection of multiple resistor-constant phase elements, as shown in the following expression: ； Where: R Ohmic Represents the equivalent ohmic resistance, which is obtained by the intersection of the curve and the real axis; R pol represents the normalized polarization resistance of the battery electrochemical reaction; τ represents the relaxation time constant corresponding to the electrochemical process; γ(lnτ) represents the distribution function; w represents the angular frequency; n represents the number of nodes of the module in which the resistor and the constant phase element are connected in parallel, where Corresponds to the relative differential contribution of a single resistive constant phase element.

5. The method for diagnosing internal short circuit faults in a dynamic temperature-compensated lithium battery according to claim 1, wherein: In S500, the relational function is a cubic polynomial.

6. The method for diagnosing internal short circuit faults in a dynamic temperature-compensated lithium battery according to claim 1, wherein: In S700, the fifth over-discharge cycle is defined as the critical state of internal short circuit, and the relative impedance change rate is used to characterize the fault degree, that is, (R post-cycle -R initial ) / R initial , where R initial is the initial state impedance parameter.

7. The method for diagnosing internal short circuit faults in a dynamic temperature-compensated lithium battery according to claim 1, wherein: In S700, the dual-dimensional collaborative diagnosis strategy includes: The positive growth of ΔRct is used as the core indicator to reflect the deterioration of charge transfer dynamics; The negative shift of ΔRSEI was used as auxiliary verification to characterize the destruction of SEI film structure; The co-evolution trend of ΔRct and ΔRSEI is calculated in real time by the edge computing unit.

8. A system for implementing the method according to any one of claims 1 to 7, characterized in that: It includes: Impedance spectrum measurement module, used to apply multi-frequency excitation signals and collect EIS data; Temperature sensor, real-time monitoring of battery surface temperature; Edge computing unit, with built-in temperature compensation model and diagnostic strategy, outputs fault warning signals; The data storage module stores the EIS database and the temperature-impedance mapping relationship.

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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