A solid-state battery thermal runaway early warning method and system based on electric-magnetic dual-mode fusion

Abstract

The present application relates to the technical field of battery management, and more particularly to a solid-state battery thermal runaway early warning method and system based on electric-magnetic dual-mode fusion. The method comprises injecting a preset frequency sinusoidal current disturbance signal into a to-be-tested solid-state battery and synchronously collecting the voltage response signal of the battery; performing time-frequency transformation on the voltage response signal to obtain a time-frequency domain representation; calculating an energy concentration index and a time continuity index based on the time-frequency domain representation; determining whether the first early warning condition is met based on the energy concentration index and the time continuity index, and triggering the first stage early warning; after triggering the first stage early warning, starting non-contact monitoring of the external magnetic field of the solid-state battery; and determining whether the second early warning condition is met based on the monitored magnetic field data, and triggering the second stage early warning. The present application can realize early, rapid and non-destructive early warning of thermal runaway.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, in particular to a solid-state battery thermal runaway early warning method and system based on electric-magnetic dual-mode fusion. BACKGROUND

[0002] With the rapid development of electric vehicles, higher requirements are put forward for the energy density and safety of power batteries. Solid-state batteries are considered an important direction for the next generation of power batteries due to their high energy density and high safety, but their thermal runaway behavior has not been fully understood. Existing thermal runaway early warning methods are mostly for traditional lithium-ion batteries, using contactless electrochemical impedance spectroscopy (EIS) and other electrical methods, which have problems such as information redundancy, complex calculation, unstable impedance at high temperatures, etc.

[0003] In addition, existing methods are difficult to perform non-destructive testing when the battery is close to failure, and lack adaptability to the non-uniformity of interface impedance at high temperatures for solid-state batteries.

[0004] Therefore, there is an urgent need for a method that can solve the problems of information redundancy, low computational efficiency, high-temperature failure, and poor reliability of existing early warning methods in the application of solid-state batteries, and achieve early, rapid, and non-destructive early warning of thermal runaway. SUMMARY

[0005] In view of the above problems, the present disclosure provides a solid-state battery thermal runaway early warning method and system based on electric-magnetic dual-mode fusion, which overcomes the above problems or at least partially solves the above problems, and aims to solve the problems of information redundancy, low computational efficiency, high-temperature failure, and poor reliability of existing early warning methods in the application of solid-state batteries, and achieve early, rapid, and non-destructive early warning of thermal runaway.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] The first aspect of the present application provides a solid-state battery thermal runaway early warning method based on electric-magnetic dual-mode fusion, comprising:

[0008] injecting a preset frequency sinusoidal current disturbance signal into the solid-state battery to be tested, and synchronously collecting the voltage response signal of the battery; performing time-frequency transformation on the voltage response signal to obtain a time-frequency domain representation;

[0009] Based on the time-frequency domain representation, calculate the energy concentration index and the time continuity index;

[0010] Based on the energy concentration index and the time continuity index, determine whether the first early warning condition is met, and trigger the first stage early warning if the first early warning condition is met;

[0011] After triggering the first stage early warning, start non-contact monitoring of the external magnetic field of the solid-state battery;

[0012] Based on the monitored magnetic field data, it is judged that the second early warning condition is met, and the second stage early warning is triggered.

[0013] Further,

[0014] The first early warning condition is that the energy concentration index is greater than the first threshold value and the time continuity index is less than the second threshold value.

[0015] Further,

[0016] The second early warning condition is that a preset magnetic field anomaly mutation event occurs; the preset magnetic field anomaly mutation event is that the polarity of the magnetic field is reversed.

[0017] Further,

[0018] The time-frequency transformation of the voltage response signal is performed to obtain a time-frequency domain representation, including:

[0019] The time-domain voltage response is converted into a time-frequency domain signal based on the generalized S transform, and the discrete form of the generalized S transform is:

[0020] ;

[0021] Wherein, n, k, m are time, frequency and time index respectively; is a discrete voltage response signal, the length of which is N, and the sampling frequency is ; is a sliding Gaussian window; is the Gaussian window width, which is inversely proportional to the frequency ; ; is the discrete frequency (Hz);

[0022] The time-frequency domain is obtained by logarithmic scaling of .

[0023] Further,

[0024] The calculation of the energy concentration index ECI includes:

[0025] Based on the time-frequency domain, the amplitude profile of the frequency dimension is calculated, which is represented as:

[0026] ;

[0027] Wherein, is the discrete frequency index within the target frequency band, is the time index, is the number of time points;

[0028] The normalized fourth moment of the amplitude profile is calculated as the energy concentration index ECI, denoted as:

[0029] ;

[0030] wherein, is the number of frequency points; is the mean value of ; is the square of the variance.

[0031] Further,

[0032] The time continuity index TCI is calculated, comprising:

[0033] Based on the time-frequency domain, the amplitude profile in the time dimension is calculated ,

[0034] denoted as: ;

[0035] wherein, is the discrete frequency index within the target frequency band, is the time index, is the number of time points;

[0036] The coefficient of variation of the amplitude profile is calculated , denoted as ;

[0037] wherein, is the mean value of ;

[0038] The TCI is obtained as .

[0039] Further, after triggering the first-stage warning, the non-contact monitoring of the external magnetic field of the solid-state battery is started, comprising:

[0040] After triggering the first-stage warning, the magnetization intensity of the solid-state battery is monitored , denoted as:

[0041] wherein, is the volume, is the magnetic moment of a single metal atom.

[0042] Further,

[0043] The magnetization intensity changes the external magnetic field of the battery, and the change amount can be denoted as:

[0044] ;

[0045] wherein, is a geometrical constant related to the sensor position and the battery structure, is the mass susceptibility of the metal deposit, is the accumulated mass of the transition metal reduction product.

[0046] A second aspect of the technical solution of the present application provides a solid-state battery thermal runaway early warning system based on electric-magnetic dual-mode fusion, comprising:

[0047] a signal excitation and collection module for injecting a preset frequency sinusoidal current disturbance signal into the solid-state battery to be measured and synchronously collecting the voltage response signal of the battery;

[0048] a signal processing module for performing time-frequency transformation on the voltage response signal to obtain a time-frequency domain representation; based on the time-frequency domain representation, calculating an energy concentration index and a time continuity index;

[0049] a magnetic field monitoring module for starting non-contact monitoring of the external magnetic field of the solid-state battery after triggering the first stage early warning;

[0050] an early warning module for judging that the first early warning condition is met based on the energy concentration index and the time continuity index, and triggering the first stage early warning;

[0051] for judging that the second early warning condition is met based on the monitored magnetic field data, and triggering the second stage early warning.

[0052] A third aspect of the technical solution of the present application provides a computer readable storage medium having instructions stored thereon, which when executed by one or more processors, cause the processors to execute the solid-state battery thermal runaway early warning method based on electric-magnetic dual-mode fusion as described in the first aspect.

[0053] The technical solution proposed in the present application can bring the following beneficial effects:

[0054] 1. The present application adopts full-stage non-invasive early warning, fuses electrical and magnetic monitoring, realizes full-process coverage from interface slow degradation to severe explosion precursor, and adopts non-contact monitoring in the late stage to realize non-invasive diagnosis.

[0055] 2. The present application meets the online rapid calculation, the present application directly analyzes the 1kHz single frequency response signal, avoids the full frequency band scanning and complex calculation of the traditional EIS, and shortens the online time consumption to 0.007 seconds.

[0056] 3. The application establishes two new indexes, energy concentration index (ECI) and time continuity index (TCI), to evaluate the performance of the early warning response signal online, and quantifies the interface impedance unevenness and state fluctuation of the solid-state battery, so as to realize the quantitative early warning of thermal runaway in the early stage.

[0057] The above description is only a summary of the technical solutions of the present disclosure, in order to enable more clear understanding of the technical means of the present disclosure, and in order to enable the above and other purposes, characteristics and advantages of the present disclosure to be more obvious and easy to understand, the following specific embodiments of the present disclosure are described in detail. BRIEF DESCRIPTION OF DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are intended to only illustrate preferred embodiments and are not considered limiting of the present disclosure. Moreover, like reference numerals designate like parts throughout the several views in the drawings. In the drawings:

[0059] Figure 1 is a step flow chart of a solid-state battery thermal runaway early warning method based on electric-magnetic dual-mode fusion provided by the embodiments of the present specification;

[0060] Figure 2 is a three-dimensional time-frequency graph of a solid-state battery under normal conditions provided by the embodiments of the present specification;

[0061] Figure 3 is a frequency distribution graph of a solid-state battery under normal conditions provided by the embodiments of the present specification;

[0062] Figure 4 is a time evolution graph of a solid-state battery under normal conditions provided by the embodiments of the present specification;

[0063] Figure 5 is an active barcode of a solid-state battery under normal conditions provided by the embodiments of the present specification;

[0064] Figure 6 is a three-dimensional time-frequency graph when the battery temperature is higher than 60 degrees Celsius provided by the embodiments of the present specification;

[0065] Figure 7 is a full spectrum graph when the battery temperature is higher than 60 degrees Celsius provided by the embodiments of the present specification;

[0066] Figure 8 is a frequency distribution graph when the battery temperature is higher than 60 degrees Celsius provided by the embodiments of the present specification;

[0067] Figure 9 is a time evolution graph when the battery temperature is higher than 60 degrees Celsius provided by the embodiments of the present specification;

[0068] Figure 10 is an activity barcode provided by the embodiment of the present specification when the battery temperature is higher than 60 degrees Celsius;

[0069] Figure 11 is a threshold effect diagram of the first stage early warning under different SOC provided by the embodiment of the present specification;

[0070] Figure 12 is a second stage magnetic field mutation curve diagram provided by the embodiment of the present specification;

[0071] Figure 13 is a structural diagram of a solid-state battery thermal runaway early warning system based on electric-magnetic dual-mode fusion provided by the embodiment of the present specification;

[0072] Figure 14 is an online disturbance-response signal measurement hardware schematic diagram. DETAILED DESCRIPTION

[0073] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art. The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0074] The present application provides a solid-state battery thermal runaway early warning method and system based on electric-magnetic dual-mode fusion.

[0075] As shown in Figure 1 , a step flow chart of a solid-state battery thermal runaway early warning method based on electric-magnetic dual-mode fusion;

[0076] In a first aspect of the technical solutions of the present application, a solid-state battery thermal runaway early warning method based on electric-magnetic dual-mode fusion is provided, comprising the following steps:

[0077] S101, injecting a preset frequency sinusoidal current disturbance signal into the solid-state battery to be measured, and synchronously collecting the voltage response signal of the battery;

[0078] S102, performing time-frequency transformation on the voltage response signal to obtain a time-frequency domain representation;

[0079] S103, calculating an energy concentration index and a time continuity index based on the time-frequency domain representation;

[0080] S104, determining whether the first early warning condition is met based on the energy concentration index and the time continuity index, and triggering the first stage early warning if the first early warning condition is met;

[0081] S105. After triggering the first stage warning, non-contact monitoring of the external magnetic field of the solid-state battery is initiated.

[0082] S106. Based on the monitored magnetic field data, if the second warning condition is met, the second stage warning is triggered.

[0083] Based on step S101, firstly as follows: Figure 14 As shown, the solid-state battery is connected to a differential amplifier, which is used to measure the response signal. The solid-state battery is also connected to a differential amplifier and a precision resistor in parallel for measuring the actual disturbance signal. The signal is then sent to the host computer via a data acquisition unit for data acquisition and calculation. The disturbance signal is injected via a waveform generator.

[0084] To achieve real-time evaluation of the battery system response under a 1kHz disturbance, a disturbance-response measurement platform based on a four-wire Kelvin connection was built. First, a waveform generator produces a 5V sinusoidal disturbance signal with a frequency of 1kHz. Then, a voltage-controlled current source (VCCS) provides load driving capability and injects the sinusoidal disturbance into the battery under test. Simultaneously, a high-precision differential amplifier acquires the resulting voltage response. A 500 mΩ precision resistor connected in series with the battery is used to accurately measure the actual disturbance current. The disturbance and response signals measured by the platform meet the Kramers-Kronig causality and linearity requirements. Finally, the response signal acquired by the data acquisition card (DAQ) is sent to the host computer for further processing.

[0085] This invention employs a four-wire Kelvin configuration to eliminate the influence of wire resistance on measurement accuracy. Its core function is to inject a 1kHz sinusoidal disturbance signal in real time and acquire the battery voltage response. The specific hardware and parameters used are as follows:

[0086] Disturbance signal generation unit: Waveform generator (model FY6300-20M) generates a 5V, 1kHz sinusoidal current disturbance signal with high frequency accuracy. 20ppm ensures the stability of the disturbance signal.

[0087] Signal injection unit: Voltage-controlled current source (VCCS, model KW-BPVCCS1000) injects disturbance signals into the solid-state battery, with an output current range of... 1A, meeting battery safety testing requirements.

[0088] Signal acquisition unit: A high-precision differential amplifier acquires the battery's voltage response signal. Suppress common-mode interference; 500 m series connection Precision resistor (model BWL-EE, 3W, temperature range 55~175℃) for real-time measurement of actual disturbance current. ;

[0089] The data acquisition card (DAQ, model ART-USB3132A) collects the voltage and current signals at a sampling frequency of 5000 Hz and a resolution of 16 bits, ensuring signal fidelity.

[0090] The data processing unit: the host computer receives the DAQ data, performs time-frequency analysis and index calculation, and processes the time consumption online ≤0.007 seconds.

[0091] The hardware module of the application needs to meet the Kramers-Kronig causality and linear requirements, that is, the phase difference and amplitude relationship between the disturbance signal and the response signal meet the linear system characteristics, ensuring the effectiveness of subsequent signal analysis.

[0092] The application designs a two-stage early warning, the first stage through electrical monitoring, the second stage through magnetic field monitoring; through the technical scheme of "electrical monitoring-magnetic field monitoring" dual-mode fusion, the early abnormality of thermal runaway and the explosion precursor are identified respectively;

[0093] Among them, the first stage realizes early warning of thermal runaway by the process of "disturbance injection-response collection-time-frequency analysis-index extraction";

[0094] The application designs an excitation-response signal direct analysis method ERSD (Emphasizes-response subdues-disturbing), which directly analyzes the voltage response signal of the battery under 1 kHz sinusoidal current disturbance by bypassing the traditional electrochemical impedance spectrum calculation.

[0095] A waveform generator is used to generate a 1 kHz sinusoidal voltage signal, which is converted into a current disturbance by a voltage-controlled current source (VCCS) ( The amplitude of the disturbance current is calculated by a precision resistor voltage divider), which is injected into the battery through a four-wire Kelvin connection, and the voltage response signal of the battery is collected synchronously .

[0096] Based on the S102 step, time-frequency analysis and generalized S transform are performed, specifically including:

[0097] The time-frequency transform of the voltage response signal is performed to obtain a time-frequency domain representation, including:

[0098] The time-domain voltage response is converted into a time-frequency domain signal based on the generalized S transform,

[0099] The time and frequency dimensions are retained, and the transformation formula is as follows:

[0100] ;

[0101] wherein, is the analysis window time center, is the frequency, is the frequency-dependent Gaussian window function used to highlight the fault-sensitive features and suppress the disturbance signal redundant information.

[0102] In practical calculation, the discrete form of the generalized S-transform is:

[0103] ;

[0104] wherein, , are the time, frequency and time sample indices, respectively; is the discrete voltage response signal with length N and sampling frequency ; is the sliding Gaussian window, is the Gaussian window width, which is inversely proportional to the frequency ; is the discrete frequency (Hz);

[0105] The time-frequency domain is obtained by logarithmic scaling of , highlighting the fault-induced features.

[0106] Based on the step S103,

[0107] the calculation energy concentration index ECI is calculated, which represents the sharpness of the quantitative frequency domain energy distribution and reflects the solid-state battery interface impedance uniformity.

[0108] First, based on the time-frequency domain, the amplitude profile of the frequency dimension is calculated, which is represented as:

[0109] ;

[0110] wherein, is the discrete frequency index within the target frequency band, is the time index, is the number of time points;

[0111] Then, the normalized fourth moment of the amplitude profile is calculated as the energy concentration index ECI, which is represented as:

[0112] ;

[0113] wherein, is the number of frequency points; is the mean value of ;

[0114] is the square of the variance.

[0115] Further,

[0116] The time continuity index TCI is calculated, which represents the stability of quantifying the energy of the time domain signal, reflecting the degree of fluctuation of the battery state.

[0117] First, based on the time-frequency domain, the amplitude profile in the time dimension is calculated ,

[0118] is expressed as: ;

[0119] wherein, is the discrete frequency index in the target frequency band, is the time index, is the number of time points;

[0120] Then, the coefficient of variation of the amplitude profile is calculated , expressed as ;

[0121] wherein, is the mean value of the amplitude profile; is the standard deviation;

[0122] Finally, TCI is defined as the normalized inverse of, so that TCI is ; the closer the TCI value is to 1, the more stable the time domain signal is; the decrease of the TCI value indicates that the fluctuation of the internal state of the battery is intensified, for example, the change of the interfacial impedance caused by the expansion of the electrolyte.

[0123] Based on the S104 step, a first warning condition is set, wherein the first warning condition is that the energy concentration index is greater than a first threshold value and the time continuity index is less than a second threshold value;

[0124] Exemplarily, the warning threshold values are set as: , the ECI is about 2.65 under normal conditions, , and the TCI is about 0.94 under normal conditions. When and are satisfied, the first stage warning is triggered, and at the same time the disturbance signal injection of the waveform generator and the VCCS is stopped to avoid additional burden on the battery.

[0125] Based on the S105 and S106 steps,

[0126] The non-contact monitoring of the external magnetic field of the solid-state battery is started after the first-stage early warning is triggered, to capture the pre-explosion signal that may be caused by the end of thermal runaway. When the first-stage early warning is triggered, the transition metal of the positive electrode is reduced to generate a ferromagnetic metal deposit with a permanent magnetic moment, and the collective magnetic moment of the ferromagnetic metal deposit forms a macroscopic magnetization , thereby changing the magnetic field distribution around the battery, including:

[0127] The magnetization of the solid-state battery is monitored after the first-stage early warning is triggered , which is represented as:

[0128] , wherein, is the volume, is the magnetic moment of a single metal atom.

[0129] The magnetization changes the external magnetic field of the battery, and the change amount may be represented as:

[0130] ;

[0131] , wherein, is a geometric constant related to the sensor position and the battery structure, is the mass susceptibility of the metal deposit, is the cumulative mass of the transition metal reduction product.

[0132] As the thermal runaway intensifies, the metal deposit continuously accumulates, and the external magnetic field strength continuously increases. When the battery experiences thermal runaway and approaches explosion, a strong fault current is generated inside, causing a sudden change in the polarity of the magnetic field. When the magnetic field polarity reversal is detected, the second-stage early warning can be triggered.

[0133] The second early warning condition is the occurrence of a preset magnetic field anomaly mutation event; the preset magnetic field anomaly mutation event is the polarity reversal of the magnetic field.

[0134] The present application combines contact electrical monitoring (early stage) and non-contact magnetic field monitoring (late stage), avoids the safety risk of contact measurement in the critical state, covers the whole stage of thermal runaway, and realizes non-invasive diagnosis.

[0135] In addition, the present application directly analyzes the 1kHz response signal, avoids the complex impedance calculation and full-band scanning of traditional EIS, and the online calculation time is only 0.007 seconds, which is about 700 times faster than the fastest existing fast EIS (5 seconds), meeting the real-time monitoring requirements of vehicle-mounted devices.

[0136] The ECI and TCI indexes respectively associate interface impedance distribution unevenness (caused by solid-state electrolyte expansion) and battery state sustained fluctuation (caused by interface reaction aggravation), and magnetic field mutation association transition metal deposition (characteristic at the end of thermal runaway), each index can correspond to a clear physical and chemical mechanism, and has strong explainability.

[0137] The SSB test bench with online ERSD and magnetic field monitoring is adopted in the application, two types of experiments are designed to verify the effectiveness of the two-stage early warning:

[0138] 1. The heating plate is used to pulse heat the battery, so that the temperature exceeds the normal working range, and the effectiveness of the first stage early warning method is verified under different states of charge (SOC);

[0139] 2. The battery is continuously heated until thermal runaway and fire explosion occur, so as to verify the overall effectiveness of the two-stage early warning strategy proposed in the application.

[0140] All experiments are carried out in an explosion-proof box to ensure safety, and are connected with ERSD module, magnetic field monitoring module and temperature or voltage monitoring equipment, and the exhaust fan is turned on during the experiment to timely exhaust harmful gas; once the first stage early warning is triggered, the waveform generator is immediately turned off, and the disturbance signal is stopped to ensure that the battery is not damaged; then the second stage is entered, and the magnetic field monitoring is continuously carried out.

[0141] The magnetic field sensor is fixed above the clamp and has no physical contact with the battery, and will not interfere with the battery even at the critical stage before explosion. By comparing the first stage early warning triggering time and the second stage magnetic field mutation time, the time markers of the two-stage early warning can be obtained, and the early warning advance amount of each stage is calculated based on the industry standard thermal runaway judgment criterion.

[0142] As shown in Figure 2 , a 1 kHz disturbance is applied to the solid-state battery at normal temperature, Figure 2 wherein the x-axis coordinate is frequency, the y-axis coordinate is time, and the z-axis coordinate is amplitude; as shown in Figure 3 , Figure 3 wherein the horizontal coordinate is time, and the vertical coordinate is average amplitude, and the results show that the ECI is stable at about 2.65; as shown in Figure 4 , Figure 4 wherein the horizontal coordinate is time, and the vertical coordinate is average amplitude, and the results show that the TCI is stable at about 0.94; as shown in Figure 5 , Figure 5 wherein the horizontal coordinate is time, and the vertical coordinate is activity, and the black stripes represent that the activity is higher than the average value, and the results show that the activity barcode shows that the active proportion of the signal is 51.8%, and no early warning is triggered.

[0143] Since high temperature will cause the solid-state electrolyte to expand, thereby changing the contact pressure of the electrode / electrolyte interface, the interface impedance is unevenly distributed. This non-uniformity can cause fluctuations in the 1 kHz perturbation response along the time dimension, so the fluctuations in the response are intuitively shown in FIGS. Figure 6 、 Figure 7 Figure 6 and Figure 7 , where the x-axis coordinate is the frequency, the y-axis coordinate is the time, and the z-axis coordinate is the amplitude; Figure 6 , where the abscissa is the time and the ordinate is the frequency; Figure 7 , where the abscissa is the time and the ordinate is the average amplitude;

[0144] As shown in FIGS. Figure 8 、 Figure 9 , the index calculation further verifies that the ECI is 3.33 when the anomaly occurs, which is higher than the normal value of 2.65; the TCI is 0.89 when the anomaly occurs, which is lower than the normal value of 0.94; where, Figure 8 , the abscissa is the time and the ordinate is the average amplitude; Figure 9 , the abscissa is the time and the ordinate is the average amplitude.

[0145] As shown in FIGS. Figure 10 , the abscissa is the time and the ordinate is the activity; Figure 10 , the abscissa is the time and the ordinate is the activity. Figure 10 provides an intuitive way to observe the time distribution of signal activity. The black bar chart represents the time period when the signal strength is higher than the average level, and the white area represents the time period when the signal strength is lower than the average level. The column spacing ratio result shows that the battery signal under abnormal high temperature conditions is only 43%, which is lower than the normal value of 51.8%.

[0146] The setting of "ECI>3 and TCI<0.9" is the first stage warning threshold, while according to the industry standard, the conditions that need to be met to judge thermal runaway are that the temperature exceeds 60℃, the temperature rise rate exceeds 1℃ / s or the voltage drop reaches 25% of the initial value. In contrast, the first stage warning relies on the fluctuations in the 1 kHz perturbation response index caused by the thermal expansion and contraction of the solid electrolyte, providing a 9-minute warning (traditional judgment requires heating to 130℃ before the voltage drop occurs, while the method of the present application triggers a warning at 62℃).

[0147] Figure 11 is a threshold effect diagram for the first stage warning under different SOC, which shows the effectiveness verification results of the stage one warning method under different SOC (state of charge); where, Figure 11 , the abscissa is the ECI value and the ordinate is the TCI value.

[0148] ​To adapt to the characteristics of different states of charge (SOC, 0%-100%) of the battery, the threshold adaptability of ECI and TCI is calibrated through experiments, and the ECI and TCI values of the solid-state battery under different SOC are measured under normal and abnormal states. The results show that the early warning thresholds under different SOC are applicable, , When the SOC is low (0%-20%), the TCI decreases to 0.86, but still meets the judgment condition of "abnormal state TCI<0.9", and when the SOC is high (80%-100%), the ECI increases to 3.5, the early warning trigger is more sensitive, and there is no false alarm; therefore, the threshold does not need to be adjusted to realize the early warning coverage in the whole SOC range.

[0149] After the first early warning is completed, the magnetic field monitoring is continuously maintained until the magnetic field polarity is reversed due to the current anomaly, triggering the second early warning.

[0150] Figure 12 The second stage magnetic field mutation curve is shown in FIG. 4, Figure 12 wherein the abscissa is time and the ordinate is the magnetic field intensity. About 20 seconds before the thermal runaway explosion, the external magnetic field monitored by the fluxgate sensor undergoes rapid polarity reversal and dramatic mutation, at which time the second stage early warning is triggered. It is found in the experiment that within the next 20 seconds, the temperature rises rapidly, the voltage drops rapidly, and fire and explosion occur.

[0151] The sharp reversal of the magnetic field polarity indicates that the main physical mechanism changes from static magnetization to dynamic electromagnetic induction. This mechanism is a strong local internal current generated rapidly before the battery breaks, which represents a direct measurement of the fault current itself and marks the transition from the chemical degradation process to the explosive electrical and mechanical failure event.

[0152] The second aspect of the technical scheme of the present application provides a solid-state battery thermal runaway early warning system 1300 based on electric-magnetic dual-mode fusion, comprising:

[0153] A signal excitation and collection module 1301 is used to inject a preset frequency sinusoidal current disturbance signal into the solid-state battery to be measured, and synchronously collect the voltage response signal of the battery;

[0154] A signal processing module 1302 is used to perform time-frequency transformation on the voltage response signal to obtain a time-frequency domain representation; based on the time-frequency domain representation, an energy concentration index and a time continuity index are calculated;

[0155] A magnetic field monitoring module 1303 is used to start non-contact monitoring of the external magnetic field of the solid-state battery after triggering the first stage early warning;

[0156] The early warning module 1304 is configured to determine that the first early warning condition is met based on the energy concentration index and the time continuity index, and trigger the first-stage early warning.

[0157] The early warning module 1304 is configured to determine that the first early warning condition is met based on the energy concentration index and the time continuity index, and trigger the first-stage early warning.

[0158] The third aspect of the technical scheme of the present application provides a computer readable storage medium, which stores instructions, and when the instructions are executed by one or more processors, the processors execute the solid-state battery thermal runaway early warning method based on the electro-magnetic dual modal fusion as described in the first aspect.

[0159] The present embodiment can divide the method into functional modules according to the above method examples, for example, each functional module can be corresponding, or two or more functions can be integrated in one processing module, and the integrated module can be realized in the form of hardware. It should be noted that the division of modules in the present embodiment is illustrative, and is only a logical functional division. In actual implementation, another division mode can be used.

[0160] In the case of dividing each functional module according to each function, the signal excitation and collection module, the signal processing module, the magnetic field monitoring module, the early warning module, etc. can be included. It should be noted that all related contents of each step involved in the above method embodiments can be referred to the function description of the corresponding functional module, which will not be described here.

[0161] The present embodiment also provides a computer readable storage medium, which (including but not limited to disk memory, CD-ROM, optical memory, etc.) stores computer program codes, and when the computer program codes are run on a computer, the computer executes the above related method steps to realize the solid-state battery thermal runaway early warning method and system based on the electro-magnetic dual modal fusion provided in the above embodiment.

[0162] The present embodiment also provides a computer program product, which, when run on a computer, causes the computer to execute the above related steps to realize the solid-state battery thermal runaway early warning method and system based on the electro-magnetic dual modal fusion provided in the above embodiment. The beneficial effects of the above embodiment can be referred to the beneficial effects of the corresponding method provided above, which will not be described here.

[0163] Through the description of the above embodiments, those skilled in the art can understand that, for the convenience and brevity of description, only the above division of functional modules is taken as an example. In actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0164] In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the division of the apparatus embodiments is only a logical function division, and there can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another apparatus, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different apparatuses can be indirect couplings or communication connections through some interfaces, apparatuses or units, and can be in electrical, mechanical or other forms.

[0165] It should be noted that, in the description of the disclosure, the relative terms such as "upper", "lower", "front", "back", "left" and "right" are based on the orientation or position relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and do not indicate or imply that the indicated position or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the disclosure.

[0166] The above is only an embodiment of the disclosure, and is not intended to limit the disclosure. Those skilled in the art can make various modifications and changes to the disclosure. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the disclosure shall be included in the scope of the claims of the disclosure.

Claims

1. A method for early warning of thermal runaway in solid-state batteries based on electro-magnetic dual-mode fusion, characterized in that, include: A sinusoidal current disturbance signal of a preset frequency is injected into the solid-state battery under test, and the voltage response signal of the battery is acquired simultaneously. The voltage response signal is subjected to time-frequency transformation to obtain a time-frequency domain representation; Based on the time-frequency domain representation, the energy concentration index and the time continuity index are calculated; If the energy concentration index and time continuity index are used to determine whether the first warning condition is met, then the first stage warning is triggered. After triggering the first-stage warning, non-contact monitoring of the external magnetic field of the solid-state battery is initiated. Based on the monitored magnetic field data, if the second warning condition is met, the second stage warning is triggered. The step of performing time-frequency transformation on the voltage response signal to obtain a time-frequency domain representation includes: Based on the generalized S-transform, the time-domain voltage response is converted into a time-frequency domain signal. The discrete form of the generalized S-transform is as follows: ； Where n, k, and m are time, frequency, and time index, respectively; The signal is a discrete voltage response with a length of N and a sampling frequency of . ; A sliding Gaussian window; The width of the Gaussian window is related to the frequency. Inversely proportional, ; Discrete frequency; right Logarithmic scaling is performed to obtain the time-frequency domain. ; The calculation of the Energy Concentration Index (ECI) includes: Based on the time-frequency domain, the amplitude profile in the frequency dimension is calculated. , represented as: ； in, This refers to the discrete frequency index within the target frequency band. For time indexing, For time points; The normalized fourth moment of the amplitude profile is calculated as the energy concentration index (ECI), expressed as: ； in, Number of frequency points; for The mean; It is the square of the variance; Calculating the Time Continuity Index (TCI) includes: Based on the time-frequency domain, the amplitude profile in the time dimension is calculated. , Represented as: ; in, This refers to the discrete frequency index within the target frequency band. For time indexing, For time points; Calculate the coefficient of variation of the amplitude profile. , represented as ; in, The mean; Standard deviation; TCI was obtained .

2. The method for early warning of thermal runaway in solid-state batteries based on electro-magnetic dual-mode fusion according to claim 1, characterized in that, The first warning condition is that the energy concentration index is greater than a first threshold and the time continuity index is less than a second threshold.

3. The method for early warning of thermal runaway in solid-state batteries based on electro-magnetic dual-mode fusion according to claim 1, characterized in that, The second warning condition is the occurrence of a preset abnormal magnetic field event; the preset abnormal magnetic field event is a reversal of magnetic field polarity.

4. The method for early warning of thermal runaway in solid-state batteries based on electro-magnetic dual-mode fusion according to claim 1, characterized in that, After triggering the first-stage warning, the non-contact monitoring of the external magnetic field of the solid-state battery is initiated, including: After triggering the first-stage warning, monitor the magnetization of the solid-state battery. , represented as: ,in, For volume, denoted as , where is the magnetic moment of a single metal atom.

5. The method for early warning of thermal runaway in solid-state batteries based on electro-magnetic dual-mode fusion according to claim 4, characterized in that, The magnetization The change in the external magnetic field of the battery It can be represented as: ； in, These are geometric constants related to the sensor location and battery structure. The mass magnetic susceptibility of the metal deposit. This represents the cumulative mass of the transition metal reduction products.

6. A solid-state battery thermal runaway early warning system based on electro-magnetic dual-mode fusion, characterized in that, The solid-state battery thermal runaway early warning system is used to implement the solid-state battery thermal runaway early warning method based on electro-magnetic dual-mode fusion as described in any one of claims 1-5; The solid-state battery thermal runaway early warning system includes: The signal excitation and acquisition module is used to inject a sinusoidal current disturbance signal of a preset frequency into the solid-state battery under test and simultaneously acquire the voltage response signal of the battery. The signal processing module is used to perform time-frequency transformation on the voltage response signal to obtain a time-frequency domain representation; and to calculate the energy concentration index and the time continuity index based on the time-frequency domain representation. The magnetic field monitoring module is used to initiate non-contact monitoring of the external magnetic field of the solid-state battery after the first-stage warning is triggered. The early warning module is used to determine whether the first early warning condition is met based on the energy concentration index and the time continuity index, and to trigger the first stage of early warning. Based on the monitored magnetic field data, it is used to determine whether the second warning condition is met and to trigger the second-stage warning.

7. A computer-readable storage medium, characterized in that, It stores instructions that, when executed by one or more processors, cause the processors to perform the solid-state battery thermal runaway early warning method based on electro-magnetic dual-mode fusion as described in any one of claims 1-5.

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