Method for detecting PACK thermal runaway of energy storage battery pack

Abstract

The invention discloses an energy storage battery pack PACK thermal runaway detection method, and relates to the technical field of battery thermal runaway detection. Comprising the following steps: collecting mechanical stress change data in battery charge-discharge circulation, and establishing a stress change data set; monitoring the pressure fluctuation trend in the single body in real time, recording pressure change data, and performing correlation analysis with the stress change data set through data synchronization to form a stress-pressure combined data set; inputting a material aging model and an interface fatigue analysis model, extracting a sensitive area and a failure point of interface aging, and quantifying a gas release risk; and on the basis of the quantized gas release risk, an abnormal trend curve is constructed in combination with historical data, feedback is performed, and early warning information is generated. According to the invention, accurate identification of the aging sensitive area and the high-risk monomer is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of battery thermal runaway detection, and in particular 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 renewable energy utilization. As the core component of the energy storage system, the performance and safety of the energy storage battery pack (PACK) directly affect the operating efficiency and reliability of the entire system. Traditional energy storage battery thermal management and fault monitoring methods mainly rely on the detection of external parameters such as temperature, voltage, and current.

[0003] However, these methods have limited perception of internal failure signs, especially in the early stages 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 studies have shown that the thermal runaway process of energy storage batteries is usually triggered by mechanical stress changes and gas release inside the battery cells. Abnormal changes in these internal physical and chemical parameters often precede changes in traditional external parameters. Therefore, how to obtain key physical and chemical information from the inside of the battery and perform precise analysis has become an important research direction in thermal runaway detection technology for energy storage batteries. Summary of the invention

[0004] In view of the problems existing in the above background technology, 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 responsiveness of thermal runaway risks by introducing joint analysis of stress and pressure, material aging modeling and trend prediction methods.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for detecting thermal runaway of an energy storage battery pack PACK, which includes collecting mechanical stress change data in a battery charge and discharge cycle, and recording the stress peak and minimum values ​​of each charge and discharge cycle in combination with a segmented marking technology to establish a stress change data set; arranging a micro pressure sensor inside the battery pack to monitor the pressure fluctuation trend inside the cell in real time, 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 the data in the stress-pressure joint data set into a material aging model and an interface fatigue analysis model to extract sensitive areas and failure points of interface aging, and quantify the risk of gas release; based on the quantified gas release risk, identifying 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 providing feedback to generate an early warning signal and output a control instruction.

[0007] As a preferred solution of the thermal runaway detection method for the energy storage battery pack PACK of the present invention, wherein: the recording of the stress peak and minimum value 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; 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.

[0008] As a preferred solution of the thermal runaway detection method of the energy storage battery pack PACK of the present invention, the correlation analysis through data synchronization and stress change data set includes: synchronizing the pressure fluctuation sequence with the stress change data set on the time axis, and calculating the correlation between the two by the following formula: ; ; in, For the moment The stress-pressure correlation value of and denote the pressure change rate and stress change rate respectively, and are the weight coefficients for measuring the impact of pressure change and stress change on thermal runaway risk, For the moment Pressure value, is the pressure change, is the maximum stress value in the current period, is the minimum stress value in the current period, is the stress change; if If it 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 irrelevant.

[0009] As a preferred solution of the thermal runaway detection method for the energy storage battery pack PACK of the present invention, wherein: 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 the 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. 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 a nonlinear index; the extraction of sensitive areas 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.

[0010] As a preferred solution of the thermal runaway detection method for the energy storage battery pack PACK of the present invention, the interface fatigue analysis model performs a detailed analysis on the stress distribution in the aging sensitive area, and the identification of possible failure points includes the following contents: using the interface fatigue analysis model to analyze the stress distribution in the aging sensitive area: ; ; 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.

[0011] As a preferred solution of the thermal runaway detection method for the energy storage battery pack PACK of the present invention, the quantification of the gas release risk includes: performing a detailed analysis of the aging sensitive area in combination with the interface fatigue analysis model to quantify the gas release risk, and the calculation formula is as follows: ; in, is a gas release risk indicator, and is the weight coefficient, which reflects the impact of the mean and fluctuation on the risk; the gas release risk of each sensitive area is mapped to the risk level, and an interface failure risk level map is generated to show the location of the sensitive area, the risk level and the corresponding time point.

[0012] As a preferred solution of the thermal runaway detection method for the energy storage battery pack PACK of the present invention, wherein: the material aging factor is correlated and matched with the frequency domain characteristics of the pressure fluctuation sequence, and the response relationship of the pressure fluctuation characteristics to the material aging is extracted, including: performing a fast Fourier transform on the pressure fluctuation sequence 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 the material aging factor and the pressure frequency domain energy; The correlation coefficient The time periods above the set threshold are marked as aging-sensitive areas, and a list of aging-sensitive areas is output.

[0013] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the energy storage battery pack PACK thermal runaway detection method as described in the first aspect of the present invention are implemented.

[0014] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the method for detecting thermal runaway of an energy storage battery pack PACK as described in the first aspect of the present invention are implemented.

[0015] The beneficial effects of the present invention are as follows: the present invention starts with the collection of mechanical stress data of the battery charge and discharge cycle, analyzes stress, pressure and their correlation synchronously, forms a complete data chain, provides multi-dimensional early warning signals, and realizes 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 of energy storage battery packs in the prior art, and greatly improves the safety and reliability of energy storage battery systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 The figure is a flow chart of a thermal runaway detection method for a storage battery pack PACK. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] Example 1 Reference Figure 1 , which is the first embodiment of the present invention, and provides a method for detecting thermal runaway of an energy storage battery pack PACK, comprising: S1: Configure the battery cell stress sensing unit to 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.

[0022] S1.1: Flexible stress sensors are arranged at the key stress points of the battery cells, and piezoelectric materials with high sensitivity and anti-electromagnetic interference performance are selected as the sensing layer to accurately measure the microstress changes generated during the charge and discharge cycles.

[0023] It should be noted that the stress sensor of the present invention adopts a flexible stress sensor, which reflects the stress state by detecting the electrical signal generated by the mechanical strain.

[0024] It is generally installed at key stress points of the battery cell, such as the battery casing, near the tabs, or at the internal material interface to capture stress changes.

[0025] Among them, the stress sensor needs to be directly attached to the surface of the material, as close as possible to the stress concentration area.

[0026] S1.2: The output signal of the flexible stress sensor is converted into a digital signal through a high-precision ADC (analog-to-digital converter) to form a stress data stream corresponding to the time series.

[0027] S1.3: Design a stress data labeling algorithm based on time segmentation, segment the stress data stream according to the charging stage and the discharging stage, and automatically identify the stress peak and minimum values ​​in each segment of data, and generate corresponding segmented stress labels.

[0028] Preferably, stress data is collected , record all monomers in signal sequence; Stress data Divide the time into segments to ensure that each segment corresponds to a charge and discharge cycle; For all stress data in the segment Directly find the maximum and minimum values, mark them, and store the segmented stress marks together with the time series in the stress change dataset.

[0029] It should be noted that the joint data set stores the peak and minimum stress values, which can be used to quickly analyze the critical status of a specific stage.

[0030] S2: Micro pressure sensors are arranged inside the battery pack to monitor the pressure fluctuation trend inside the battery 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.

[0031] S2.1: Arrange a micro pressure sensor inside the battery cell to collect the pressure signal of the cell during the charge and discharge cycle and record it as pressure time series data.

[0032] It should be noted that the micro pressure sensor is arranged in the cavity inside the battery cell or in the area where gas may accumulate, and is used to monitor the gas pressure fluctuation inside the battery in real time.

[0033] The pressure sensor needs to be installed in an airtight environment to avoid interference with the signal.

[0034] It should be noted that according to the battery structure design, the installation position should take into account both signal accuracy and battery functional integrity.

[0035] S2.2: Perform analog-to-digital conversion on the collected pressure time series data, remove environmental noise and instantaneous spikes through a filter, extract the effective signal of pressure fluctuation, and obtain the pressure fluctuation sequence.

[0036] S2.3: By means of segmentation marking technology, the pressure fluctuation sequence is divided into a charging pressure subsequence and a discharging pressure subsequence according to the charging and discharging stages, and the subsequence data is recorded, and the pressure peak value and the minimum value are marked.

[0037] S2.4: synchronize the pressure fluctuation sequence with the stress change data set obtained in step S1 on the time axis, calculate the correlation between the two using a data fitting algorithm, and form a stress-pressure joint data set, in which each record includes the corresponding relationship between the timestamp, pressure value, and stress value.

[0038] Preferably, the pressure fluctuation sequence is synchronized with the stress change data set in time axis, and the formula for calculating the correlation between the two is as follows: ; ; in, For the moment The stress-pressure correlation value of and denote the pressure change rate and stress change rate respectively, and are the weight coefficients for measuring the impact of pressure change and stress change on thermal runaway risk, For the moment Pressure value, is the pressure change, is the maximum stress value in the current period, is the minimum stress value in the current period, is the stress variation.

[0039] in, and Based on a large amount of charging and discharging experimental data, the weight coefficient is adjusted so that the model can more accurately reflect the risk association.

[0040] Based on historical experimental data or model calibration, a correlation threshold is determined. If it 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 irrelevant.

[0041] Furthermore, it should be noted that The synchronization of the pressure change rate and the stress change rate is comprehensively considered. If the change rates of the two increase synchronously, it means that there is a strong coupling relationship between the two.

[0042] when When it 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.

[0043] in addition, The core of is the synchronization of the rate of change, not the absolute value. and Larger, and only when the two are synchronized will increase, so The physical meaning of is more about the correlation between the changing trends of the two.

[0044] Furthermore, the stress change dataset and the pressure fluctuation sequence are aligned to the time axis, and the missing data points are completed by interpolation to ensure the consistency of the data after synchronization, and finally a joint data record containing timestamps, pressure values, and stress values ​​is formed.

[0045] It should be noted that By combining the stress change rate and pressure change rate, the synchronization and correlation of the two changes during the charge and discharge cycle are evaluated, and when forming a stress-pressure joint data set, It can reflect the correlation between stress and pressure changes at each moment and is used to identify common sources of abnormal fluctuations during the charging and discharging process, such as gas accumulation caused by decomposition reactions inside 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.

[0046] When stress or pressure data is missing at a certain moment, the The trend of change can determine whether the interpolation needs to emphasize the stress or pressure characteristics.

[0047] And the strong correlation indicates that there is a coupling relationship between pressure fluctuations and stress changes, which is usually a manifestation of early signs of thermal runaway: Synchronous changes in pressure peaks and stress peaks may reflect gas release, material aging, or increased internal fatigue. By identifying moments when the correlation is abnormally high, timely warning of possible thermal runaway risks can be provided.

[0048] It should be noted that stress refers to the internal force per unit area, which is directly related to the deformation state of the material; pressure refers to the external force applied per unit area; changes in stress are usually related to the structural strain and material aging inside the battery, while changes in pressure mainly reflect changes in environmental parameters such as gas release or electrolyte volatilization, and are key indicators for early detection of thermal runaway. The present invention can achieve more comprehensive state monitoring and abnormal source identification through the joint analysis of stress and pressure.

[0049] S3: Input the data of the stress-pressure joint data set into the material aging model and the interface fatigue analysis model to extract the sensitive areas of interface aging and possible failure points, and quantify the gas release risk.

[0050] S3.1: Input the combined stress-pressure data set into the material aging model and calculate the material aging factor based on the time axis data in the material aging model parameters.

[0051] It should be noted that the material aging factor reflects the degree of structural degradation of key parts of the energy storage battery pack.

[0052] Preferably, 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. 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 It is a nonlinear index that reflects the sensitivity of stress and pressure to the degree of aging.

[0053] The material aging model is used to calculate the aging factors of key parts of the energy storage battery pack at each moment, and the time evolution curve of the aging factor is obtained to reflect the degree of structural degradation.

[0054] 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 the material aging, generate a list of aging-sensitive areas and mark the corresponding time points.

[0055] Specifically, the pressure fluctuation sequence is subjected to fast Fourier transform (FFT) to obtain the 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 the material aging factor and the pressure frequency domain energy; 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.

[0056] 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.

[0057] Preferably, the interface fatigue analysis model is used to analyze the stress distribution in the aging sensitive area: ; ; 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.

[0058] 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.

[0059] 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: ; in, is a gas release risk indicator, and is the weight coefficient, which reflects the impact of mean and volatility on risk.

[0060] The gas release risk of each sensitive area is mapped to a risk level (such as low, medium, and high) to generate an interface failure risk level map, showing the location of the sensitive area, the risk level, and the corresponding time point.

[0061] S4: 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.

[0062] Based on the output gas release risk level and interface failure risk level diagram, a multi-parameter screening algorithm is used to mark monomers with abnormal stress and pressure change characteristics, and the time point and corresponding data of each monomer are recorded, for example, to determine whether the gas release risk exceeds the 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 and other conditions. If any one of them is met, it is marked as abnormal, otherwise, it is considered normal.

[0063] Furthermore, the marked monomer data is dynamically compared with the historical data set, and the abnormal development trend of the monomer is calculated through the trend fitting algorithm to generate an abnormal trend curve. Based on the abnormal development trend, the monomer whose development speed and acceleration exceed the set threshold are marked as high-risk monomers.

[0064] The abnormal trend curves and multi-parameter screening results of high-risk monomers are fed back to form a comprehensive report including monomer locations, abnormal trend characteristics and high-risk indicators.

[0065] This embodiment also provides a computer device, which is applicable to the case of the thermal runaway detection method for an energy storage battery pack PACK, and includes 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.

[0066] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through 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 a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0067] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for detecting thermal runaway of an energy storage battery pack PACK proposed in the above embodiment is implemented.

[0068] In summary, the present invention starts with the collection of mechanical stress data of battery charge and discharge cycles, analyzes stress, pressure and their correlation synchronously, forms a complete data chain, provides multi-dimensional early warning signals, and realizes 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 of energy storage battery packs in the prior art, and greatly improves the safety and reliability of energy storage battery systems.

[0069] Example 2 Referring to Table 1, which is the second embodiment of the present invention, this embodiment provides a thermal runaway detection method for an energy storage battery pack PACK. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0070] In order to verify the effectiveness of the battery thermal runaway prediction and safety detection method proposed in the present invention, six battery cells (labeled as A, B, C, D, E, and F) were designed and tested in this experiment.

[0071] The experimental goal is to compare the performance differences between the method of the present invention and the traditional method under different conditions, especially in terms of thermal runaway response of battery cells, material aging, and gas release risks.

[0072] The experiment first used an intelligent sensor array to monitor the temperature, pressure and stress changes of battery cells in various working environments in real time, and combined with external gas detection sensors to conduct a comprehensive assessment of the gas release risk of each battery cell. The specific process is as follows: Each battery cell is placed in a controlled environment with the ambient temperature maintained between 25°C and 40°C and the humidity set at 50%.

[0073] The peak pressure, peak stress, minimum pressure and minimum stress of each battery are recorded in real time through the intelligent sensor array, and the data collection frequency is once every 10 seconds.

[0074] At the same time, infrared thermal imaging technology is used to monitor the temperature of the battery surface in real time to ensure the accuracy of the thermal response data of each battery.

[0075] The gas sensor is used to monitor whether each battery cell releases gas during the test, and the data is recorded for subsequent analysis.

[0076] According to the response characteristics of different battery cells, the material aging factor is calculated to evaluate the decay rate and durability of battery performance.

[0077] All data is processed and stored in real time through the embedded system, and finally analyzed and model trained through the cloud platform. The following is part of the experimental data of this experiment: Table 1 Experimental data From the table data, we can see that the battery cells show different characteristics of pressure, stress peak, material aging factor and gas release risk during the experiment. By comparing the performance of different battery cells, we can draw the following analysis conclusions: From the table data, the peak pressure and stress of battery cell B and battery cell E are higher, 900Pa and 1300N / m², 920Pa and 1280N / m² respectively, which indicates that their structures are more likely to withstand high loads, but 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 lower (875Pa and 1150N / m²), and its structure is relatively stable, showing a lower risk of thermal runaway.

[0078] The material aging factor of battery cell E is 0.16, which is significantly higher than other battery cells. This data shows that battery cell E ages faster, may need 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 decays more slowly.

[0079] The gas release risk of battery cell E is 7, which is significantly higher than that of other battery cells (such as battery cell B is 6, and battery cells A and F are 4), which indicates that battery cell E is more prone to gas leakage during use, increasing the risk of thermal runaway.

[0080] This data emphasizes the importance of the gas monitoring sensor in the present invention, which can capture gas release phenomena in real time, provide early warning for the battery management system, and avoid thermal runaway accidents.

[0081] It can be seen from the above data comparison that the present invention can accurately predict the potential thermal runaway risk of battery cells through real-time monitoring of multiple sensors, thereby improving the safety and reliability of the battery management system.

[0082] In particular, the combination of the intelligent sensor array and the gas detection technology enables the present invention to accurately predict the thermal runaway and gas leakage risks of the battery cells in real time, thereby effectively avoiding the blind spots and delay problems of traditional detection methods.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in 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.

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 correlation analysis performed by data synchronization and stress change data set includes: The formula for synchronizing the pressure fluctuation sequence with the stress change data set and calculating the correlation between the two is as follows: ； ； in, For the moment The stress-pressure correlation value of and denote the pressure change rate and stress change rate respectively, and are the weight coefficients for measuring the impact of pressure change and stress change on thermal runaway risk, For the moment Pressure value, is the pressure change, is the maximum stress value in the current period, is the minimum stress value in the current period, is the stress variation; like If it 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 irrelevant.

4. The method for detecting thermal runaway of an energy storage battery pack PACK according to claim 3, characterized in that: Inputting data from the combined stress-pressure data set into a 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. The material aging factor is calculated according to the time axis data in the material aging model parameters. 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; The sensitive areas of the extraction interface aging include: The material aging factor is correlated and matched with the frequency domain characteristics of the pressure fluctuation sequence, the response relationship of the pressure fluctuation characteristics to the material aging is extracted, a list of aging-sensitive areas is generated and the corresponding time points are marked.

5. The method for detecting thermal runaway of an energy storage battery pack PACK according to claim 4, characterized in that: The interface fatigue analysis model performs a detailed analysis of the stress distribution in the aging-sensitive area and identifies possible failure points including the following: Using the interface fatigue analysis model, the stress distribution in the aging sensitive area is analyzed: ； ； 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.

6. The method for detecting thermal runaway of an energy storage battery pack PACK according to claim 5, characterized in that: The quantified gas release risks include: A detailed analysis of aging-sensitive areas is performed to quantify the risk of gas release, and the calculation formula is as follows: ； in, is a gas release risk indicator, and is the weight coefficient, reflecting the impact of mean and volatility on risk; The gas release risk of each sensitive area is mapped to the risk level, and an interface failure risk level map is generated to show the location of the sensitive area, the risk level and the corresponding time point.

7. The method for detecting thermal runaway of an energy storage battery pack PACK according to claim 6, characterized in that: The step of correlating and matching the material aging factor with the frequency domain characteristics of the pressure fluctuation sequence to extract the response relationship between the pressure fluctuation characteristics and the material aging includes: Perform fast Fourier transform on the pressure fluctuation sequence to obtain the 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 the material aging factor and the pressure frequency domain energy; The correlation coefficient The time periods above the set threshold are marked as aging-sensitive areas, and a list of aging-sensitive areas is output.

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