A Method for Detecting Thermal Runaway of an Energy Storage Battery Pack PACK

Abstract

The present invention discloses a method for detecting thermal runaway of an energy storage battery pack PACK, which relates to the technical field of battery thermal runaway detection. The method includes collecting mechanical stress change data during battery charge and discharge cycles to establish a stress change data set; real-time monitoring the pressure fluctuation trend inside the single cell, recording the pressure change data, and performing correlation analysis with the stress change data set through data synchronization to form a stress-pressure joint data set; inputting a material aging model and an interface fatigue analysis model to extract the sensitive areas and failure points of interface aging and quantify the gas release risk; based on the quantified gas release risk, constructing an abnormal trend curve in combination with historical data and giving feedback to generate a warning message. The present invention realizes the accurate identification of aging sensitive areas and high-risk single cells.

Description

Technical Field

[0001] The present invention relates to the technical field of battery thermal runaway detection, and particularly to a method for detecting thermal runaway of an energy storage battery pack PACK. Background Art

[0002] In recent years, with the rapid development of the new energy industry, energy storage technology has become increasingly important in power regulation and the utilization of renewable energy. As the core component of an energy storage system, the performance and safety of an energy storage battery pack (PACK) directly affect the operating efficiency and reliability of the entire system. Traditional methods for energy storage battery thermal management and fault monitoring mainly rely on the detection of external parameters such as temperature, voltage, and current.

[0003] However, these methods have limited perception of internal failure symptoms. Especially in the initial stage of thermal runaway, key parameters such as internal stress changes, material aging, and interface failure cannot be monitored and analyzed in a timely and effective manner, resulting in insufficient early warning and intervention capabilities of the system. Existing research shows that the thermal runaway process of energy storage batteries is usually triggered by mechanical stress changes and gas release inside the battery cells. These abnormal changes in internal physical and chemical parameters often precede the changes in traditional external parameters. Therefore, how to obtain key physical and chemical information from inside the battery and conduct accurate analysis has become an important research direction in the field of energy storage battery thermal runaway detection technology. Summary of the Invention

[0004] In view of the problems existing in the above background art, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to improve the detection sensitivity and system response ability to thermal runaway risk by introducing methods of combined stress and pressure analysis, material aging modeling, and trend prediction.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for detecting thermal runaway of an energy storage battery pack PACK, which includes collecting data on mechanical stress changes during battery charge and discharge cycles, and recording the stress peak and minimum values of each charge and discharge cycle in combination with a segmented marking technique to establish a stress change data set; arranging micro pressure sensors inside the battery pack to real-time monitor the pressure fluctuation trend inside the single cell, record the pressure change data, and perform correlation analysis with the stress change data set through data synchronization to form a stress-pressure joint data set; inputting the data in the stress-pressure joint data set into a material aging model and an interface fatigue analysis model, extracting the sensitive areas and failure points of interface aging, and quantifying the gas release risk; based on the quantified gas release risk, identifying the single cells with abnormal stress and pressure change characteristics through a multi-parameter screening algorithm, constructing an abnormal trend curve in combination with historical data, and giving feedback to generate a warning signal and output a control instruction.

[0008] As a preferred solution of the method for detecting thermal runaway of the energy storage battery pack PACK according to the present invention, wherein: the recording of the stress peak and minimum values of each charge and discharge cycle in combination with the segmented marking technique includes: collecting stress data , recording the signal sequences of all single cells at ; performing time segmentation on the stress data , with each segment corresponding to a charge and discharge cycle; finding the maximum and minimum values of all stress data within the segment, and making marks, and storing the segmented stress marks and the time series together in the stress change data set.

[0009] As a preferred solution of the method for detecting thermal runaway of the energy storage battery pack PACK according to the present invention, wherein: the correlation analysis through data synchronization with the stress change data set includes: synchronizing the pressure fluctuation sequence with the stress change data set on the time axis, and the formula for calculating the correlation between the two is as follows:

[0010] ;

[0011] ;

[0012] Wherein, is the stress-pressure correlation value at time , and respectively represent the pressure change rate and the stress change rate, and are respectively the weight coefficients for measuring the influence of pressure change and stress change on the thermal runaway risk, is the pressure value at time , is the pressure change amount, is the maximum stress value within the current period of time, is the minimum stress value within the current period of time, is the stress change amount; if is greater than or equal to the correlation threshold, it is considered that there is a strong correlation between pressure and stress; otherwise, the correlation is weak or non-existent.

[0013] As a preferred solution of the thermal runaway detection method for the energy storage battery pack PACK described in the present invention, wherein: inputting the data in the stress-pressure joint dataset into the material aging model includes: the material aging model simulates the cumulative effect of the degradation of the internal microstructure of the material based on long-term stress loading and environmental pressure changes, and calculates the material aging factor according to the time-axis data in the material aging model parameters. The calculation formula of the aging factor is as follows:

[0014] ;

[0015] wherein, is the material aging factor at time , and are the material constants of the aging model, and are the non-linear exponents; the extraction of the sensitive area of interface aging includes: correlating and matching the material aging factor with the frequency-domain characteristics of the pressure fluctuation sequence, extracting the response relationship of the pressure fluctuation characteristics to material aging, generating a list of aging sensitive areas and marking the corresponding time points.

[0016] As a preferred solution of the thermal runaway detection method for the energy storage battery pack PACK described in the present invention, wherein: the interface fatigue analysis model performs a refined analysis on the stress distribution in the aging sensitive area, and identifying possible failure points includes the following content: using the interface fatigue analysis model to analyze the stress distribution in the aging sensitive area:

[0017] ;

[0018] ;

[0019] wherein, is the average stress of the sensitive area, is the stress fluctuation amplitude of the sensitive area, is the time set of the th sensitive area.

[0020] As a preferred solution of the thermal runaway detection method for the energy storage battery pack PACK described in the present invention, wherein: the quantification of the gas release risk includes: combining the interface fatigue analysis model to perform a refined analysis on the aging sensitive area, and quantifying the gas release risk. The calculation formula is as follows:

[0021] ;

[0022] Among them, is the gas release risk index, and are weight coefficients, reflecting the influence of the mean value and fluctuation on the risk; map the gas release risk of each sensitive area to the risk level, generate an interface failure risk level map, and display the location, risk level and corresponding time points of the sensitive areas.

[0023] As a preferred solution of the thermal runaway detection method for the energy storage battery pack PACK described in the present invention, among them: the correlation matching of the material aging factor and the frequency domain characteristics of the pressure fluctuation sequence to extract the response relationship of the pressure fluctuation characteristics to material aging includes: performing a fast Fourier transform on the pressure fluctuation sequence to obtain the frequency spectrum :

[0024] ;

[0025] Among them, is the frequency domain energy within the specified frequency band , reflecting the main vibration frequency range of the pressure fluctuation; calculate the correlation between the aging factor and the pressure frequency domain energy:

[0026] ;

[0027] Among them, is the correlation coefficient between the material aging factor and the pressure frequency domain energy;

[0028] Mark the time period when the correlation coefficient is higher than the set threshold as the aging sensitive area, and output the aging sensitive area list.

[0029] In a second aspect, the present invention provides a computer device, including a memory and a processor, and the memory stores a computer program, among which: when the computer program instructions are executed by the processor, the steps of the thermal runaway detection method for the energy storage battery pack PACK described in the first aspect of the present invention are implemented.

[0030] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, among which: when the computer program instructions are executed by the processor, the steps of the thermal runaway detection method for the energy storage battery pack PACK described in the first aspect of the present invention are implemented.

[0031] The beneficial effects of the present invention are as follows: Starting from the collection of mechanical stress data during the charge and discharge cycles of the battery, the present invention synchronously analyzes stress, pressure, and their correlations to form a complete data chain, providing multi-dimensional early warning signals. Through joint data modeling and multi-parameter screening algorithms, the accurate identification of aging-sensitive regions and high-risk monomers is achieved. The present invention effectively solves the problems of insufficient early detection and risk quantification of thermal runaway in existing energy storage battery packs, and greatly improves the safety and reliability of the energy storage battery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0033] Figure 1 It is a flowchart of a method for detecting thermal runaway of an energy storage battery pack PACK. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0035] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0036] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0037] Embodiment 1

[0038] Refer to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a method for detecting thermal runaway of an energy storage battery pack PACK, including

[0039] S1: Configure a battery cell stress sensing unit to collect mechanical stress change data during the charge and discharge cycles of the battery, and record the stress peak and minimum values of each charge and discharge cycle in combination with the segmented marking technology to establish a stress change data set.

[0040] S1.1: Arrange flexible stress sensors at the key stress points of the battery cell. Select piezoelectric materials with high sensitivity and anti-electromagnetic interference performance as the sensing layer to accurately measure the micro-stress changes generated during the charge and discharge cycles.

[0041] It should be noted that the stress sensor of the present invention uses a flexible stress sensor, and reflects the stress state by detecting the electrical signal generated by mechanical strain.

[0042] Generally, it is installed at the key stress points of the battery cell, such as near the battery case, tab or the internal material interface, to capture stress changes.

[0043] Among them, the stress sensor needs to be directly attached to the material surface, as close as possible to the stress concentration area.

[0044] S1.2: Convert the output signal of the flexible stress sensor into a digital signal through a high-precision ADC (analog-to-digital converter) to form a stress data stream corresponding to the time series.

[0045] S1.3: Design a stress data marking algorithm based on time segmentation. Segment the stress data stream according to the charge stage and discharge stage, and automatically identify the stress peak and minimum value in each segment of data, and generate segmented stress marks accordingly.

[0046] Preferably, collect stress data , record the signal sequences of all cells at ;

[0047] Perform time segmentation on the stress data to ensure that each segment corresponds to a charge and discharge cycle;

[0048] For all stress data within the segment , directly find the maximum and minimum values and mark them, and store the segmented stress marks and the time series together in the stress change data set.

[0049] It should be noted that the combined data set stores the stress peak and minimum value, which can be used for rapid analysis of the key state in a specific stage.

[0050] S2: Arrange micro pressure sensors inside the battery pack, monitor the pressure fluctuation trend inside the cell in real time, record the pressure change data, and perform correlation analysis with the stress change data set through data synchronization to form a stress-pressure combined data set.

[0051] S2.1: Arrange micro pressure sensors inside the battery cell, collect the pressure signals of the cell during the charge and discharge cycles, and record them as pressure time series data.

[0052] It should be noted that the micro pressure sensor is arranged in the cavity inside the battery cell or the area where gas may accumulate, and is used to monitor the gas pressure fluctuation inside the battery in real time.

[0053] The pressure sensor needs to be installed in an airtight environment to avoid interference signals.

[0054] It should be noted that according to the battery structure design, the installation position should take into account both signal accuracy and battery function integrity.

[0055] S2.2: Perform analog-to-digital conversion on the collected pressure time series data, remove environmental noise and instantaneous spike values through a filter, extract the effective signal of the pressure fluctuation, and obtain the pressure fluctuation sequence.

[0056] S2.3: Through the segment marking technique, divide the pressure fluctuation sequence into a charging pressure subsequence and a discharging pressure subsequence according to the charge and discharge stages, record the subsequence data, and mark the pressure peak value and the lowest value.

[0057] S2.4: Synchronize the pressure fluctuation sequence with the stress change data set obtained in step S1 on the time axis, use the data fitting algorithm to calculate the correlation between the two, and form a stress-pressure joint data set, where each record includes the corresponding relationship of the time stamp, pressure value, and stress value.

[0058] Preferably, the formula for synchronizing the pressure fluctuation sequence with the stress change data set on the time axis and calculating the correlation between the two is as follows:

[0059] ;

[0060] ;

[0061] Where is the stress-pressure correlation value at time , and respectively represent the pressure change rate and the stress change rate, and are the weight coefficients for measuring the influence of pressure change and stress change on the thermal runaway risk respectively, is the pressure value at time , is the pressure change amount, is the maximum stress value within the current time period, is the minimum stress value within the current time period, is the stress change amount.

[0062] Where and According to a large amount of charge and discharge experiment data, adjust the weight coefficients so that the model can more accurately reflect the risk correlation.

[0063] Determine a correlation threshold based on historical experimental data or model calibration. If is greater than or equal to the correlation threshold, it is considered that there is a strong correlation between pressure and stress; otherwise, the correlation is weak or non-existent.

[0064] Furthermore, it should be noted that The synchronism of the pressure change rate and the stress change rate is comprehensively considered. If the change rates of both increase synchronously, it indicates that there is a strong coupling relationship between the two.

[0065] When is greater than the threshold, it indicates that the change patterns of stress and pressure are highly consistent, which may be an early signal of thermal runaway.

[0066] In addition, the core lies in the synchronism of the change rates, rather than simply the absolute values. Even if and are large, it will only increase when the two are synchronous Therefore the physical meaning of

[0067] Furthermore, align the stress change data set and the pressure fluctuation sequence along the time axis, and complement the missing data points by interpolation to ensure the consistency after data synchronization, and finally form a joint data record including time stamps, pressure values and stress values.

[0068] It should be noted that By combining the stress change rate and the pressure change rate, evaluate the synchronism and correlation of their changes during the charge and discharge cycle. When forming the stress-pressure joint data set it can reflect the correlation between stress and pressure changes at each moment, and is used to identify the common sources of abnormal fluctuations during the charge and discharge process. For example, gas accumulation caused by internal decomposition reactions in the battery, local thermal runaway caused by internal short circuits in the battery, resulting in abnormal synchronization of stress and pressure or simultaneous changes in pressure and stress caused by thermal expansion, etc.

[0069] When stress or pressure data is missing at a certain moment, by observing the change trend of adjacent moments, it can be determined whether the interpolation needs to emphasize the characteristics of one side of stress or pressure.

[0070] And a strong correlation indicates that there is a coupling relationship between pressure fluctuations and stress changes, and this relationship is usually manifested as an early signal of thermal runaway:

[0071] The synchronous change of the peak pressure and peak stress may reflect gas release, material aging, or increased internal fatigue. By identifying moments with an unusually high degree of correlation, potential thermal runaway risks can be timely warned.

[0072] It should be noted that stress refers to the internal force per unit area and is directly related to the deformation state of the material; pressure refers to the external force applied per unit area; the change of stress is usually related to the structural strain and material aging inside the battery, while the change of pressure mainly reflects environmental parameter changes such as gas release or electrolyte volatilization, which is a key indicator for early detection of thermal runaway. Through the combined analysis of stress and pressure, the present invention can achieve more comprehensive state monitoring and identification of the source of anomalies.

[0073] S3: Input the data of the stress-pressure combined data set into the material aging model and the interface fatigue analysis model, extract the sensitive areas of interface aging and possible failure points, and quantify the gas release risk.

[0074] S3.1: Input the stress-pressure combined data set into the material aging model, and calculate the material aging factor according to the time-axis data in the material aging model parameters.

[0075] It should be noted that the material aging factor reflects the degree of structural degradation of the key parts of the energy storage battery pack.

[0076] Preferably, the material aging model is based on long-term stress loading and environmental pressure changes, simulating the cumulative effect of the degradation of the internal microstructure of the material. The calculation formula of the aging factor is as follows:

[0077] ;

[0078] where is the material aging factor at time , and are the material constants of the aging model, and are non-linear exponents, reflecting the sensitivity of stress and pressure to the degree of aging.

[0079] By calculating the aging factor of the key parts of the energy storage battery pack at each moment through this material aging model, the time evolution curve of the aging factor is obtained, reflecting the degree of structural degradation.

[0080] S3.2: Correlate and match the material aging factor with the frequency-domain characteristics of the pressure fluctuation sequence, extract the response relationship of the pressure fluctuation characteristics to material aging, generate a list of aging-sensitive areas and mark the corresponding time points.

[0081] Specifically, perform a fast Fourier transform (FFT) on the pressure fluctuation sequence to obtain the frequency spectrum :

[0082] ;

[0083] in, For a specified frequency band The frequency domain energy within reflects the main vibration frequency range of pressure fluctuations;

[0084] Calculate the correlation between the aging factor and the pressure frequency domain energy:

[0085] ;

[0086] in, is the correlation coefficient between the material aging factor and the pressure frequency domain energy;

[0087] The correlation coefficient The time period above the set threshold is marked as an aging sensitive area, and a list of aging sensitive areas is output, including the start and end time points of the sensitive areas, aging factor values, and corresponding pressure fluctuation characteristics.

[0088] S3.3: Combine the interface fatigue analysis model to perform detailed analysis of the aging sensitive area, quantify the gas release risk and output the interface failure risk level map.

[0089] Preferably, the interface fatigue analysis model is used to analyze the stress distribution in the aging sensitive area:

[0090] ;

[0091] ;

[0092] in, is the mean stress value in the sensitive area, is the stress fluctuation amplitude in the sensitive area, For the The time collection of sensitive areas.

[0093] If a sensitive area and At the same time, it is relatively high, indicating that there may be stress concentration in this area, which is a key monitoring location with a higher risk of failure.

[0094] Combined with the interface fatigue analysis model, the aging sensitive area is analyzed in detail to quantify the gas release risk. The calculation formula is as follows:

[0095] ;

[0096] in, is a gas release risk indicator, and is the weight coefficient, which reflects the impact of mean and volatility on risk.

[0097] Map the gas release risk of each sensitive area to a risk level (such as low, medium, high) to generate an interface failure risk level map, showing the location, risk level, and corresponding time points of the sensitive areas.

[0098] S4: Based on the quantified gas release risk, identify monomers with abnormal stress and pressure change characteristics through a multi-parameter screening algorithm, construct an abnormal trend curve in combination with historical data, and give feedback to generate a warning signal and output a control instruction.

[0099] Based on the output gas release risk level and the interface failure risk level map, use a multi-parameter screening algorithm to mark monomers with abnormal stress and pressure change characteristics, and record the time points and corresponding data of each monomer. For example, determine whether the gas release risk exceeds a threshold, or whether it exceeds the set change rate threshold, and whether the stress fluctuation amplitude or pressure fluctuation amplitude is significantly higher than the historical average, etc. If any one of the conditions is met, it is abnormally marked; otherwise, it is considered normal.

[0100] Furthermore, dynamically compare the marked monomer data with the historical data set, calculate the abnormal development trend of the monomer through a trend fitting algorithm, generate an abnormal trend curve, and mark the monomers with a development speed and acceleration exceeding the set threshold as high-risk monomers according to the abnormal development trend.

[0101] Feed back the abnormal trend curve of the high-risk monomer and the multi-parameter screening results to form a comprehensive report including the monomer location, abnormal trend characteristics, and high-risk indicators.

[0102] This embodiment also provides a computer device applicable to the case of the thermal runaway detection method for an energy storage battery pack PACK, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the thermal runaway detection method for the energy storage battery pack PACK proposed in the above embodiment.

[0103] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0104] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method for detecting thermal runaway of the energy storage battery pack PACK as proposed in the above embodiment.

[0105] In summary, the present invention starts from the acquisition of mechanical stress data in the battery charge and discharge cycle, synchronously analyzes stress, pressure, and their correlation to form a complete data chain, provides multi-dimensional early warning signals, and realizes the accurate identification of aging-sensitive areas and high-risk monomers through joint data modeling and multi-parameter screening algorithms. The present invention effectively solves the problems of insufficient early detection and risk quantification of thermal runaway in the existing energy storage battery pack, and greatly improves the safety and reliability of the energy storage battery system.

[0106] Embodiment 2

[0107] Referring to Table 1, this is the second embodiment of the present invention. This embodiment provides a method for detecting thermal runaway of an energy storage battery pack PACK. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0108] In order to verify the effectiveness of the battery thermal runaway prediction and safety detection method proposed by the present invention, 6 battery cells (labeled A, B, C, D, E, and F respectively) were designed and tested in this experiment.

[0109] The experimental objective is to compare the performance differences between the method of the present invention and traditional methods under different conditions, especially the differences in the thermal runaway response, material aging, and gas release risk of battery cells.

[0110] The experiment first used an intelligent sensor array to monitor the temperature, pressure, and stress changes of each battery cell in various working environments in real time, and combined with external gas detection sensors to comprehensively evaluate the gas release risk of each battery cell. The specific process is as follows:

[0111] Each battery cell was placed in a controlled environment separately, with the environmental temperature maintained between 25°C and 40°C and the humidity set at 50%.

[0112] The peak pressure, peak stress, minimum pressure, and minimum stress of each battery were recorded in real time through the intelligent sensor array, and the data acquisition frequency was once every 10 seconds.

[0113] At the same time, infrared thermal imaging technology was used to monitor the surface temperature of the battery in real time to ensure the accuracy of the thermal response data of each battery.

[0114] Whether gas release occurred in each battery cell during the experiment was monitored through gas sensors, and the recorded data was used for subsequent analysis.

[0115] According to the response characteristics of different battery cells, their material aging factors were calculated to evaluate the decline rate and durability of battery performance.

[0116] All data was processed and stored in real time through an embedded system, and finally data analysis and model training were carried out through a cloud platform. The following are some experimental data of this experiment:

[0117] Table 1 Experimental data

[0118]

[0119] As can be seen from the table data, the battery cells showed different characteristics of peak pressure, peak stress, material aging factor, and gas release risk during the experiment. By comparing the performance of different battery cells, the following analysis conclusions can be drawn:

[0120] Judging from the table data, the peak pressure and peak stress of battery cell B and battery cell E are relatively high, which are 900 Pa and 1300 N / m², 920 Pa and 1280 N / m² respectively. This indicates that their structures are more likely to withstand high loads, but at the same time it also means that they may face a greater risk of thermal runaway under high loads. In contrast, the peak pressure and stress of battery cell D are relatively low (875 Pa and 1150 N / m²), and its structure is relatively stable, showing a lower risk of thermal runaway.

[0121] The material aging factor of battery cell E is 0.16, which is significantly higher than that of other battery cells. This data indicates that battery cell E has a faster aging rate, may require more frequent replacement or maintenance, and may experience thermal runaway earlier. In contrast, battery cells A and F have lower material aging factors (0.12 and 0.13), and their performance degradation rates are slower.

[0122] The gas release risk of battery cell E is 7, which is significantly higher than that of other battery cells (e.g., 6 for battery cell B, and 4 for battery cells A and F). This indicates that battery cell E is more likely to experience gas leakage during use, increasing the risk of thermal runaway.

[0123] This data emphasizes the importance of the gas monitoring sensor in the present invention, which can capture the gas release phenomenon in real time, provide early warning for the battery management system, and avoid the occurrence of thermal runaway accidents.

[0124] From the above data comparison, it can be seen that through the real-time monitoring of multiple sensors, the present invention can accurately predict the potential thermal runaway risk of battery cells, improving the safety and reliability of the battery management system.

[0125] In particular, the combination of the intelligent sensor array and gas detection technology enables the present invention to accurately predict the thermal runaway and gas leakage risks of battery cells in real time, thus effectively avoiding the blind spots and delay problems of traditional detection methods.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for detecting thermal runaway of an energy storage battery pack PACK, characterized in that: include: Collect the mechanical stress change data during the battery charge and discharge cycle, and use the segmented marking technology to record the stress peak and minimum values ​​of each charge and discharge cycle to establish a stress change data set; Arrange micro pressure sensors inside the battery pack to monitor the pressure fluctuation trend inside the cell in real time, record the pressure change data, and perform correlation analysis with the stress change data set through data synchronization to form a stress-pressure joint data set; Inputting the data in the stress-pressure joint data set into a material aging model and an interface fatigue analysis model, extracting sensitive areas and failure points of interface aging, and quantifying the risk of gas release; Based on the quantified gas release risk, monomers with abnormal stress and pressure change characteristics are identified through a multi-parameter screening algorithm, and abnormal trend curves are constructed in combination with historical data. Feedback is provided to generate early warning signals and output control instructions. Inputting the data in the stress-pressure joint data set into the material aging model includes: the material aging model simulates the cumulative effect of internal microstructure degradation of the material based on long-term stress loading and environmental pressure changes, and calculates the material aging factor according to the time axis data in the material aging model parameters; extracting the sensitive area of ​​interface aging includes: correlating and matching the material aging factor with the frequency domain characteristics of the pressure fluctuation sequence, extracting the response relationship of the pressure fluctuation characteristics to the material aging, generating a list of aging sensitive areas and marking the corresponding time points; The interface fatigue analysis model performs a detailed analysis on the stress distribution in the aging sensitive area, and identifying possible failure points includes analyzing the stress distribution in the aging sensitive area using the interface fatigue analysis model; The quantification of gas release risk includes: performing detailed analysis on aging sensitive areas to quantify gas release risk; mapping the gas release risk of each sensitive area to a risk level, generating an interface failure risk level map, and displaying the location, risk level, and corresponding time point of the sensitive area; The method of associating and matching the material aging factor with the frequency domain characteristics of the pressure fluctuation sequence and extracting the response relationship of the pressure fluctuation characteristics to the material aging includes: performing a fast Fourier transform on the pressure fluctuation sequence to obtain a spectrum, and calculating the correlation between the aging factor and the pressure frequency domain energy; marking a time period with a correlation coefficient higher than a set threshold as an aging sensitive area, and outputting a list of aging sensitive areas.

2. The method for detecting thermal runaway of an energy storage battery pack PACK according to claim 1, characterized in that: The method of recording the peak and minimum stress values ​​of each charge and discharge cycle by combining the segmented marking technology includes: Collecting stress data , record all monomers in signal sequence; Stress data Divide the time into segments, each segment corresponds to a charge and discharge cycle; For all stress data in the segment Find the maximum and minimum values, mark them, and store the segmented stress marks together with the time series in the stress change dataset.

3. The method for detecting thermal runaway of an energy storage battery pack PACK according to claim 2, characterized in that: The aging factor calculation formula is as follows: ； in, For the moment The material aging factor, and is the material constant of the aging model, and is the nonlinear index, For the moment Pressure value, is the stress variation.

4. The method for detecting thermal runaway of an energy storage battery pack PACK according to claim 3, characterized in that: The method of analyzing the stress distribution in the aging sensitive area by using the interface fatigue analysis model includes: ； ； in, is the mean stress value in the sensitive area, is the stress fluctuation amplitude in the sensitive area, For the The time collection of sensitive areas.

5. The method for detecting thermal runaway of an energy storage battery pack PACK according to claim 4, characterized in that: The aging-sensitive areas are analyzed in detail to quantify the risk of gas release. The calculation formula is as follows: ； in, is a gas release risk indicator, and is the weight coefficient, which reflects the impact of mean and volatility on risk.

6. The method for detecting thermal runaway of an energy storage battery pack PACK according to claim 5, characterized in that: The pressure fluctuation sequence is subjected to fast Fourier transform to obtain a spectrum : ； in, For a specified frequency band The frequency domain energy within reflects the main vibration frequency range of pressure fluctuations; Calculate the correlation between the aging factor and the pressure frequency domain energy: ； in, is the correlation coefficient between material aging factor and pressure frequency domain energy.

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