Battery global temperature and pressure distribution monitoring system and method based on flexible sensing material

Abstract

The present application relates to the field of flexible sensing, and discloses a battery global temperature and pressure distribution monitoring system and method based on flexible sensing material, which comprises multi-scale area dynamic capture of flexible sensing units inside and on the surface of the battery, generation of an initial temperature and pressure response weight matrix through local coupling mapping, establishment of a basic signal acquisition sequence covering the whole battery, execution of time sequence misplacement mapping on the basic signal acquisition sequence, combination of local temperature and pressure fluctuation, micro-deformation offset and electrolyte expansion interference factors with historical cycle data to construct a multi-layer coupling feature vector, construction of a multi-dimensional priority gradient atlas reflecting potential local anomalies using the multi-layer coupling feature vector, evolution trajectory tracking of the multi-dimensional priority gradient atlas, comprehensive evaluation of abnormal evolution speed, signal gradient change and contact reliability attenuation, real-time correction of the temperature and pressure distribution of the flexible sensing array based on a dynamic priority intervention sequence, and the present application has the advantage of early and accurate early warning of local anomalies.

Description

Technical Field

[0001] This invention relates to the field of flexible sensing, specifically to a battery global temperature and pressure distribution monitoring system and method based on flexible sensing materials. Background Technology

[0002] Existing solutions for monitoring internal temperature / pressure in lithium-ion batteries have proposed integrating flexible sensing units inside the battery or near the casing to achieve in-situ sensing of multiple physical fields within the battery. For example, current technologies place sensors on the PCB at the bottom of the cell to detect temperature and pressure. However, this approach only reflects the state at the bottom of the cell or the side of the casing, making it difficult to obtain the true distribution information of various locations within the cell. In specific application scenarios, when flexible sensing arrays are installed inside the cell or closely attached to the electrodes / separator, insufficient effective contact and long-term adhesion stability of local sensing units can lead to single-point readouts or reading drift under conditions such as electrolyte expansion, electrode micro-deformation, or safety valve activation. This creates blind spots when local hot spots first appear in the battery, making it difficult to spatially identify and locate early local anomalies with sufficient precision. This problem is a local distortion occurring at micro-interface adhesion and local geometric / mechanical coupling points, directly weakening the spatial resolution and early warning capabilities of multi-point flexible arrays under highly time-varying conditions. Therefore, it is essential to design a battery global temperature and pressure distribution monitoring system and method based on flexible sensing materials for early and accurate warning of local anomalies. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a battery global temperature and pressure distribution monitoring system and method based on flexible sensing materials. It has the advantages of continuously providing multi-point temperature and pressure distribution data without blind spots under complex stress and dynamic expansion environments of batteries, and early and accurate warning of local anomalies, thus solving the problems mentioned in the background technology.

[0004] To achieve the goal of continuously providing blind-spot-free multi-point temperature and pressure distribution data under complex stress and dynamic expansion environments of batteries, and to provide early and accurate warnings of local anomalies, this invention provides the following technical solution: a battery global temperature and pressure distribution monitoring method based on flexible sensing materials, comprising the following steps: The temperature and pressure signals output by the flexible sensing units deployed inside and on the surface of the battery are collected in a multi-scale regional manner. Based on the electrode stacking density, the membrane expansion tendency and the interface stress gradient, an initial temperature and pressure response weight matrix is ​​generated through local coupling mapping to establish a basic signal acquisition sequence covering the entire battery. A time-series misalignment mapping is performed on the basic signal acquisition sequence. The local temperature and pressure fluctuations, micro-deformation offsets and electrolyte expansion interference factors reflected by the flexible sensing unit are combined with the temperature and pressure change trajectory, interface stability records and stress distribution data formed during each charge and discharge cycle to construct a multi-layer coupled feature vector. By utilizing multi-layer coupled feature vectors and combining local interface stability, micro-region thermal gradient and neighborhood sensing unit response consistency, a multi-dimensional priority gradient map reflecting potential local anomalies is constructed. The evolution trajectory of the multidimensional priority gradient map is tracked, and the information of high-rate cycling, local stress concentration and expansion disturbance of the battery is integrated to comprehensively evaluate the abnormal evolution speed, signal gradient change and contact reliability decay, and generate dynamic priority intervention sequence. Based on the dynamic priority intervention sequence, the temperature and pressure distribution of the flexible sensing array is corrected in real time, and an early warning signal is output when the local abnormal evolution reaches a set threshold.

[0005] Preferably, the process of establishing a basic signal acquisition sequence covering the entire battery is as follows: Flexible sensing units are arranged in the battery electrode stack, separator and inner wall area of ​​the casing; Parallel data acquisition is performed in different areas using distributed sampling nodes; Based on the three-dimensional geometric model of the battery, the temperature and pressure signals collected by the flexible sensing unit are mapped to the spatial coordinate system. The temperature and pressure signals are normalized and baseline drift corrected to output the corrected full-cell basic signal acquisition sequence.

[0006] Preferably, the process of performing time-series misalignment mapping on the basic signal acquisition sequence is as follows: Extract continuous time segments from the basic signal acquisition sequence, and calculate the amplitude difference, phase difference, and abrupt change frequency of adjacent sampling points within the continuous time segments; A multi-scale misalignment matrix is ​​generated using a sliding time window to characterize the dynamic change pattern of temperature and pressure distribution; The multi-scale misalignment matrix is ​​compared with the pre-stored historical reference sequence to identify the deviation segment. The historical reference sequence is the standardized time-series signal formed by the battery under known cycle conditions. Reference patterns are extracted from historical reference sequences, and segments that deviate from the reference patterns are identified through differential comparison, and corresponding abnormal indicators are extracted. Output the temporal misalignment mapping results that characterize the difference between local fluctuations and global trends.

[0007] Preferably, the process of constructing a multi-layer coupled feature vector by combining local temperature and pressure fluctuations, micro-deformation shifts, and electrolyte expansion interference factors with historical cyclic data is as follows: Multidimensional parameters such as local temperature change rate, pressure change amplitude, and micro-deformation displacement are extracted from signals collected by the flexible sensing unit. An electrolyte diffusion model is introduced to quantify the electrode spacing perturbation and add it as an interference factor to the feature set; By combining typical anomaly patterns in the historical cyclic database, parameters are aligned and filtered. By sequentially connecting the feature segments on the time axis, a multi-layered coupled feature vector in a unified feature space is generated.

[0008] Preferably, the process of constructing a multidimensional priority gradient map reflecting potential local anomalies is as follows: The multi-layer coupling feature vector obtained by the temporal misalignment mapping is input into the clustering analysis model and divided into normal segments, slowly drifting segments and abrupt change segments. The spatial consistency factor is calculated based on the response correlation between neighboring sensing units, and the spatial consistency factor is weighted and corrected using micro-region thermal gradient and interface stress distribution. The weighted result is transformed into a multidimensional gradient intensity matrix and mapped to a priority gradient map covering the entire cell.

[0009] Preferably, the process of tracing the evolution trajectory of the multidimensional priority gradient map is as follows: After obtaining the priority gradient map, high-intensity gradient nodes are selected as the initial tracking source, and their migration paths in the time series are recorded. By combining cyclic data under high-rate charge and discharge conditions, the evolution speed of abnormal nodes that appear during the tracking process is corrected; In the corrected trajectory sequence, abrupt change points and stress concentration areas are identified and marked as key evolution nodes to form a priority trajectory sequence containing time evolution characteristics.

[0010] Preferably, the process of generating a dynamic priority intervention sequence is as follows: Calculate the risk weight of each key node in the priority trajectory sequence; By correlating risk weights with battery operating status parameters, key abnormal sections can be located. Within the identified abnormal area, a dynamic programming approach is used to generate an intervention path, and the nodes within the path are prioritized according to their risk weights to form a dynamic priority intervention sequence.

[0011] Preferably, the process of real-time correction of the temperature and pressure distribution of the flexible sensing array is as follows: Adjust the weight factors of the corresponding sensing units according to the dynamic priority intervention sequence; During the adjustment process, statistical parameters of neighboring units are incorporated to correct local anomalies. Transient interference signals are weakened by adaptive filtering, and the corrected global temperature and pressure distribution is output.

[0012] Preferably, the process of outputting an early warning signal when the local abnormal evolution reaches a set threshold is as follows: The corrected temperature and pressure distribution is compared with a preset threshold range to detect whether there are any abnormal nodes that exceed the threshold. The duration of abnormal nodes is statistically analyzed, and if it exceeds a time threshold, it is determined to be a valid abnormality. After determining that the anomaly is valid, a corresponding early warning signal is generated and transmitted to the battery management system interface; At the same time, abnormal feature fragments are stored in the database, and corresponding records are created using index identification.

[0013] A battery global temperature and pressure distribution monitoring system based on flexible sensing materials, characterized in that it includes: The capture matrix module performs spatial mapping on the sensor unit signals and generates an initial temperature and pressure response weight matrix to establish the basic acquisition sequence for the full battery. The misalignment reconstruction module combines local temperature and pressure fluctuations, micro-deformations, and expansion disturbances with historical data to form a micro-region coupling feature vector. Gradient mapping module: Combining interface stability, micro-region thermal gradient and neighborhood consistency, it constructs a priority map that reflects local anomalies; Evolution tracking module: Integrates high-rate cycling, stress concentration and expansion perturbation information to assess the rate of anomalous evolution and reliability decay, and generates dynamic intervention sequences; Early warning output module: Based on dynamic intervention sequence, it corrects sensor array data and issues early warning signals when local anomalies reach a threshold.

[0014] Compared with existing technologies, the present invention provides a battery global temperature and pressure distribution monitoring system and method based on flexible sensing materials, which has the following advantages: This invention is achieved through a specific setting. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method of the present invention; Figure 2 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: Please refer to Figure 1 As shown in the embodiment of the present invention, the battery global temperature and pressure distribution monitoring method based on flexible sensing materials includes the following steps: S1: Multi-scale regional acquisition of temperature and pressure signals output by flexible sensing units deployed inside and on the surface of the battery, and based on electrode stacking density, separator expansion tendency and interface stress gradient, an initial temperature and pressure response weight matrix is ​​generated through local coupling mapping to establish a basic signal acquisition sequence covering the entire battery.

[0018] The process of establishing the basic signal acquisition sequence covering the entire battery in S1 is as follows: Based on the battery structure model, the monitoring area is divided into multiple scale levels according to spatial hierarchy and structural characteristics, including the outer shell region, the electrode stacking region, and the separator interface region. Different regions exhibit significant differences in thermal conductivity, mechanical constraints, and material expansion characteristics. Therefore, a multi-scale regional acquisition approach is adopted: high-density sensing units are deployed in localized areas with concentrated heat or significant mechanical coupling to capture subtle temperature and pressure changes; low-density sampling points are deployed in stable regions to achieve a balance between overall energy consumption and signal distribution. This approach ensures both global signal acquisition and refined monitoring in key areas.

[0019] Key structural parameters influencing local temperature and pressure changes in the battery were identified, including electrode stacking density, separator expansion tendency, and interfacial stress gradient. Electrode stacking density reflects the spatial compaction of the electrode material, directly impacting heat diffusion paths and internal stress transmission. Separator expansion tendency indicates the battery's ability to change separator material thickness during charge-discharge cycles, indicating the moderating effect of thermal strain on local pressure distribution. Interfacial stress gradient characterizes the non-uniformity of stress transmission between the electrodes, separator, and casing, identifying high-risk areas of stress concentration. Based on the spatial distribution of these parameters, the signal sampling density and sampling weights for each monitoring area were dynamically adjusted to enhance response sensitivity to critical structural locations.

[0020] After completing the region division and parameter extraction, the system establishes a local coupling mapping relationship. This mapping takes the structural characteristic parameters of different regions as input and the temperature and pressure signals collected by the flexible sensing unit as output. Through correlation analysis between signal features and structural features, the system evaluates the response intensity of different structural units to temperature and pressure changes. In each monitoring region, the sensitivity of the temperature signal to changes in stacking density, the offset trend of the pressure signal to changes in stress gradient, and the coupling compensation effect caused by membrane expansion are calculated to obtain the temperature and pressure response weight values ​​for each region. The weights of all regions are normalized and integrated to form an initial temperature and pressure response weight matrix, which is used to describe the comprehensive response distribution of each location inside the battery to changes in thermal and mechanical coupling.

[0021] Parallel data acquisition from each sensing unit is achieved through distributed sampling nodes. The sampling frequency can be set to 50–200Hz, and a global time synchronization mechanism is employed to ensure the timing consistency of multi-channel signals. Signal integrity checks, noise filtering, and outlier removal are performed at the node level to obtain raw temperature and pressure signals that have undergone quality screening.

[0022] After data acquisition, the acquired signals from each sensor unit are bound to the corresponding three-dimensional geometric model of the battery. The spatial signal distribution is reconstructed based on the sensor unit placement, and the signal amplitude is corrected using a temperature and pressure response weight matrix, thus forming a weighted signal field that reflects the true thermo-mechanical state inside the battery. After normalization and baseline drift correction, this signal field is output as a time-series basic signal acquisition sequence covering the entire battery.

[0023] S2: Perform time-series misalignment mapping on the basic signal acquisition sequence, and fuse the local temperature and pressure fluctuations, micro-deformation offsets and electrolyte expansion interference factors reflected by the flexible sensing unit with the temperature and pressure change trajectory, interface stability records and stress distribution data formed during each charge and discharge cycle to construct a multi-layer coupled feature vector.

[0024] The process of performing time-series misalignment mapping on the basic signal acquisition sequence in S2 is as follows: Continuous time segments are extracted from the basic signal acquisition sequence, and differential analysis is performed on the temperature, pressure, and stress signals within each segment. By calculating the amplitude difference, phase difference, and abrupt change frequency between adjacent sampling points, the basic time-series characteristics reflecting transient fluctuations and periodic changes are obtained.

[0025] To describe the dynamic trends at different time scales, a sliding time window mechanism is used to construct a multi-scale misalignment matrix. The length and step size of the time window are dynamically set according to the signal change rate; short windows are used to capture transient changes, while long windows are used to characterize gradual trends. The amplitude, phase, and abrupt change statistical features calculated within each window are combined to form a local dynamic feature vector. All windows are concatenated along the time axis to form a multi-scale temporal misalignment matrix, which is used to describe the dynamic temperature and pressure distribution patterns of the battery at different time scales.

[0026] The generated misalignment matrix is ​​compared with the historical reference sequence using window-level difference. The historical reference sequence is derived from the temperature and pressure records and interface stress distribution model formed by the battery under standard charge and discharge cycles, and is used as a reference for the current state. By comparing the deviation between the misalignment matrix and the reference matrix window by window, abnormal segments and signal offset directions are identified, and preliminary time-series misalignment mapping results are obtained.

[0027] The temporal misalignment results are visualized and mapped in a spatial coordinate system: time is the horizontal axis, the position of the sensing unit is the vertical axis, and the deviation magnitude or abrupt change intensity is the color or height dimension, so as to reveal the temporal and spatial thermo-mechanical coupling change trend inside the battery.

[0028] The process in S2 of constructing a multi-layer coupled feature vector by combining local temperature and pressure fluctuations, micro-deformation shifts, and electrolyte expansion interference factors with historical cyclic data is as follows: Based on the temporal misalignment mapping results, local physical change parameters, including temperature change rate, pressure change amplitude, and micro-deformation displacement, are extracted from the corresponding time segments to characterize the transient features of the local thermal and force fields. The temperature change rate reflects the intensity of heat conduction fluctuations, the pressure change amplitude reflects the degree of abrupt changes in local stress, and the micro-deformation displacement reflects the deformation response of the electrode and diaphragm structures.

[0029] An electrolyte diffusion model is introduced to quantitatively analyze the effects of electrode spacing perturbation and electrolyte expansion. By dynamically estimating the electrolyte volume change and electrode spacing offset under different charge and discharge states, this perturbation is transformed into an interference factor vector. This vector spatially corresponds one-to-one with the position of the sensing unit and is used to correct the actual response amplitude of local temperature, pressure, and deformation signals, thereby obtaining a multi-dimensional feature set after interference correction.

[0030] The corrected feature set is aligned and filtered with typical patterns in the historical cyclic database. The historical database contains temperature and pressure trajectories, interface stability curves, and stress distribution templates under normal and abnormal cycles. Using time-scale alignment and amplitude normalization strategies, the current feature set is projected into the reference pattern space. Through similarity evaluation and statistical filtering, feature dimensions sensitive to anomaly prediction or interface stability changes are retained, while noise or redundant information is removed.

[0031] Feature fragments from different sources are sequentially connected along the time axis to construct a multi-layered coupled feature vector in a unified feature space, consisting of three layers: the first layer is a dynamic temperature and pressure signal layer corrected by misalignment mapping, reflecting the global time structure; the second layer is a local deformation and stress response layer, reflecting the spatial distribution of electrochemical stress and structural strain; and the third layer is an electrolyte disturbance correction layer, describing the degree of interference from environmental and material coupling factors. The three feature vectors are then weighted and fused to generate a multi-layered coupled feature vector characterizing the difference between local fluctuations and global trends.

[0032] S3: By utilizing multi-layer coupled feature vectors and combining local interface stability, micro-region thermal gradient, and the response consistency of neighboring sensing units, a multi-dimensional priority gradient map reflecting potential local anomalies is constructed.

[0033] The process of constructing a multidimensional priority gradient map reflecting potential local anomalies in S3 is as follows: The multi-layered coupled feature vectors obtained through time-series misalignment mapping are input into the clustering analysis model to perform multi-dimensional clustering of collected battery temperature, stress, deformation, voltage, and other multi-source parameters. Based on the magnitude, rate of change, and deviation from historical stable states, time series segments are divided into three state segments: normal segments, slowly drifting segments, and abrupt change segments. Normal segments correspond to parameter fluctuations within the historical reference range; slowly drifting segments indicate continuous parameter shifts over a longer period; and abrupt change segments indicate local parameter changes exceeding a preset threshold. The clustering results can output the deviation of each data segment from its cluster center, used to quantify the significance of anomalous changes.

[0034] Based on the spatial distribution relationship between neighboring sensing units, the correlation of physical responses such as temperature, stress, or deformation of adjacent sensing units is calculated to obtain a spatial consistency factor. The spatial consistency factor reflects the degree of coordination in the responses of adjacent regions: if the changing trends of adjacent sensing units are similar, it indicates that the regional structural response is consistent; if the differences are large, it indicates that there may be local anomalies or uneven stress. The consistency factor is dynamically updated using a sliding window method to adapt to changes in real-time operating conditions.

[0035] The spatial consistency factor is weighted and corrected by combining micro-regional thermal gradients and interfacial stress distributions. Regions with larger thermal gradients are given higher weights during the correction process to highlight potential thermal instability risks; stress concentration regions are also given increased weights to reflect possible mechanical anomalies. The corrected consistency index can form a local anomaly sensitivity matrix to describe the intensity of anomaly response in each monitoring area.

[0036] The clustering results, weighted spatial consistency matrix, and micro-region thermal characteristics are combined to generate a multidimensional gradient intensity matrix. Each element in this matrix corresponds to the priority gradient value of a specific sensing unit within a specific time window, representing the anomaly risk level of that unit. This multidimensional matrix is ​​mapped onto the three-dimensional geometric model of the battery to form a priority gradient map covering the entire battery. This map can intuitively display the risk distribution, anomaly intensity changes, and possible risk propagation directions in each region, enabling early and accurate warnings of local anomalies in the battery.

[0037] S4: Track the evolution trajectory of the multidimensional priority gradient map, integrate information on high-rate cycling of the battery, local stress concentration and expansion disturbance, comprehensively evaluate the abnormal evolution speed, signal gradient change and contact reliability decay, and generate a dynamic priority intervention sequence.

[0038] The process of tracing the evolution trajectory of the multidimensional priority gradient map in S4 is as follows: After obtaining the priority gradient map, high-intensity gradient nodes are selected as the initial tracking source, and their migration paths in the time series are recorded. The system selects nodes with gradient values ​​higher than a preset threshold from the multidimensional priority gradient map as the initial tracking source. These nodes usually correspond to local anomalies or potential risk areas. The node selection is based on gradient strength sorting and can be combined with the neighborhood average gradient to determine key nodes. Each node records spatial coordinates, timestamps and gradient values ​​for subsequent trajectory tracking.

[0039] By combining cyclic data under high-rate charge and discharge conditions, the evolution speed of abnormal nodes that appear during the tracking process is corrected; The system iterates along the time series for each initial high-gradient node, searches for the gradient peak neighborhood within the next time window, determines the position and gradient value of subsequent nodes, and forms a preliminary evolution path. It introduces cyclic data under high-rate charging and discharging conditions, compares historical evolution trends with current node changes, and uses this comparison to correct the node's evolution speed. Correction methods include: using a weighted average method to smooth the node speed based on neighborhood gradient changes; using a historical trend fitting model to compensate for the node's evolution speed; and employing adaptive filtering methods to reduce the impact of abrupt noise, ensuring the trajectory reflects the actual evolutionary pattern.

[0040] In the corrected trajectory sequence, abrupt change points and stress concentration regions are identified and marked as key evolution nodes; The system identifies anomalous mutation points and stress concentration areas in the evolution trajectory and marks them as key evolution nodes: mutation point identification: the node gradient value changes sharply in adjacent time windows and exceeds the threshold to be identified as a mutation; stress concentration area identification: combined with micro-deformation and stress distribution data, the stress concentration in the area where the node is located is obvious; key nodes are used as important references for priority intervention and anomaly early warning.

[0041] Form a priority trajectory sequence that includes characteristics of temporal evolution; The system integrates evolution paths and key nodes to generate priority trajectory sequences containing time evolution characteristics: each trajectory includes node spatial coordinates, gradient values, time series information, and key node markers; it can be used to visualize the dynamic migration trend of local anomalies and the propagation path of potential risks; and it provides standardized input for multidimensional dynamic intervention, anomaly prediction, and decision analysis.

[0042] The process of generating the dynamic priority intervention sequence in S4 is as follows: Calculate the risk weight of each key node in the priority trajectory sequence; The system extracts all key evolution nodes from the priority trajectory sequence and calculates a risk weight for each node. The risk weight comprehensively considers the node gradient value, evolution speed, mutation frequency, and neighborhood consistency factor. The weight can be weighted by combining the micro-region thermal gradient and interface stress distribution. The node weight is used to quantify the contribution of each key node to potential anomalies or failures.

[0043] By correlating risk weights with battery operating status parameters, key abnormal sections can be located. The system correlates the calculated risk weights with battery operating status parameters: for each node's time window, it determines the spatiotemporal range of abnormal segments by combining real-time or historical cyclic data; it identifies segments with risks higher than preset standards through threshold screening or anomaly scoring methods, and determines the location of key abnormal segments; it performs spatial topology analysis on abnormal segments to ensure segment continuity and integrity.

[0044] Within the identified abnormal area, a dynamic programming method is used to generate an intervention path, and the nodes in the path are prioritized according to their risk weights to form a dynamic priority intervention sequence. The system generates intervention paths based on the risk weights of key abnormal sections and key nodes using dynamic programming or optimal path search methods: the battery spatial nodes and time series are modeled as a graph structure, with each node having a risk weight attribute; the dynamic programming method calculates the optimal intervention path from the starting point to the ending point based on the principle of minimizing risk accumulation or maximizing intervention priority; the nodes in the path are sorted according to their risk weights, with high-risk nodes being prioritized for intervention.

[0045] S5: Based on the dynamic priority intervention sequence, the temperature and pressure distribution of the flexible sensing array is corrected in real time, and an early warning signal is output when the local abnormal evolution reaches the set threshold.

[0046] The process of real-time correction of the temperature and pressure distribution of the flexible sensing array in S5 is as follows: Adjust the weight factors of the corresponding sensing units according to the dynamic priority intervention sequence; The system identifies key nodes and abnormal sections requiring intervention based on a dynamic priority intervention sequence, and assigns weight factors to the corresponding sensing units. The weight factors reflect the priority and correction intensity of the sensing unit in temperature and pressure distribution correction. High-risk nodes are assigned higher weights to ensure that the correction is effective for key abnormal areas. The system automatically updates the weight factor table, including the sensing unit ID, time window, and adjustment range.

[0047] During the adjustment process, statistical parameters of neighboring units are incorporated to correct local anomalies. The system combines the statistical parameters of the neighborhood units of each sensing unit to calculate the local mean, variance, and gradient trend to identify local abnormal deviations; it corrects sensing units that deviate from the statistical characteristics of the neighborhood to make their output signals consistent with the overall neighborhood; the correction methods may include weighted mean, local regression, or spatial filtering to maintain physical continuity of the correction.

[0048] Transient interference signals are weakened by adaptive filtering, and the corrected global temperature and pressure distribution is output. The system applies an adaptive filtering method to the corrected temperature and pressure data to reduce the impact of transient noise and interference signals: adaptive low-pass filtering, Kalman filtering, or recursive smoothing methods can be used; during the filtering process, the filtering parameters are adjusted according to the sensor unit weights and abnormal characteristics to preserve the signal in key areas; after processing, a stable global temperature and pressure distribution is obtained, which can truly reflect the dynamic state of the battery.

[0049] The process in S5 of outputting an early warning signal when the local abnormal evolution reaches a set threshold is as follows: The corrected temperature and pressure distribution is compared with a preset threshold range to detect whether there are any abnormal nodes that exceed the threshold. The system compares the corrected global temperature and pressure distribution with the preset threshold range: it checks whether the temperature, pressure and micro-deformation signals of each sensing unit exceed the corresponding threshold; it marks nodes that exceed the threshold as potential abnormal nodes and records their spatial location, time window and exceedance range; it can combine neighborhood statistical features to identify local abnormal clusters and eliminate isolated noise points.

[0050] The duration of abnormal nodes is statistically analyzed, and if it exceeds a time threshold, it is determined to be a valid abnormality. The system performs time-dimensional analysis on the detected potential abnormal nodes: it counts the duration of each abnormal node within a continuous time window; it compares the duration with a preset time threshold to determine whether it is a valid abnormality; a valid abnormal node indicates that the local abnormal evolution has reached a critical state and may require intervention.

[0051] After determining that the anomaly is valid, a corresponding early warning signal is generated and transmitted to the battery management system interface; The system generates early warning signals based on valid abnormal nodes: it encodes the abnormality type, node location, risk level, and occurrence time into standardized warning information; the warning signals are transmitted to the battery management system interface, which can be used for real-time display, alarm, or control strategy triggering; multiple abnormal nodes can be sorted by priority to realize a multi-level response mechanism.

[0052] At the same time, abnormal feature fragments are stored in the database, and corresponding records are created using index identification. The system stores the corresponding abnormal feature fragments in the database: it saves complete feature fragments including temperature and pressure change sequences, micro-deformation data, evolution trajectories and related weight information; it establishes an index for each record to facilitate subsequent model training, threshold optimization and historical data backtracking analysis; and it records the storage time, data source and associated early warning signal ID to ensure data management and traceability.

[0053] Example 2: Figure 2 As shown, a battery global temperature and pressure distribution monitoring system based on flexible sensing materials is characterized by comprising: The capture matrix module performs spatial mapping on the sensor unit signals and generates an initial temperature and pressure response weight matrix to establish the basic acquisition sequence for the full battery. The misalignment reconstruction module combines local temperature and pressure fluctuations, micro-deformations, and expansion disturbances with historical data to form a micro-region coupling feature vector. Gradient mapping module: Combining interface stability, micro-region thermal gradient and neighborhood consistency, it constructs a priority map that reflects local anomalies; Evolution tracking module: Integrates high-rate cycling, stress concentration and expansion perturbation information to assess the rate of anomalous evolution and reliability decay, and generates dynamic intervention sequences; Early warning output module: Based on dynamic intervention sequence, it corrects sensor array data and issues early warning signals when local anomalies reach a threshold.

[0054] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the global temperature and pressure distribution of a battery based on flexible sensing materials, characterized in that, Includes the following steps: Multi-scale regional acquisition of temperature and pressure signals output by flexible sensing units deployed inside and on the surface of the battery is performed. High-density sensing units are deployed in local areas with concentrated heat or significant mechanical coupling, while low-density sampling points are deployed in stable areas. Based on the electrode stacking density, separator expansion tendency, and interface stress gradient, an initial temperature and pressure response weight matrix is ​​generated through local coupling mapping. The local coupling mapping takes the structural characteristic parameters of different regions as input and the temperature and pressure signals collected by the flexible sensing units as output. In each monitoring region, the sensitivity of the temperature signal to changes in stacking density, the offset trend of the pressure signal to changes in stress gradient, and the coupling compensation effect caused by separator expansion are calculated to obtain the temperature and pressure response weight values ​​of each region and establish a basic signal acquisition sequence covering the entire battery. The process of establishing a basic signal acquisition sequence covering the entire battery is as follows: Flexible sensing units are arranged in the battery electrode stack, separator and inner wall area of ​​the casing; Parallel data acquisition is performed in different areas using distributed sampling nodes; Based on the three-dimensional geometric model of the battery, the temperature and pressure signals collected by the flexible sensing unit are mapped to the spatial coordinate system. The signal amplitude is corrected by combining the temperature and pressure response weight matrix, and the temperature and pressure signals are normalized and baseline drift is corrected to output the corrected full-cell basic signal acquisition sequence. A time-series misalignment mapping is performed on the basic signal acquisition sequence. The local temperature and pressure fluctuations, micro-deformation offsets and electrolyte expansion interference factors reflected by the flexible sensing unit are combined with the temperature and pressure change trajectory, interface stability records and stress distribution data formed during each charge and discharge cycle to construct a multi-layer coupled feature vector. The process of performing timing misalignment mapping on the basic signal acquisition sequence is as follows: Extract continuous time segments from the basic signal acquisition sequence, and calculate the amplitude difference, phase difference, and abrupt change frequency of adjacent sampling points within the continuous time segments; A multi-scale misalignment matrix is ​​generated using a sliding time window to characterize the dynamic change pattern of temperature and pressure distribution; The multi-scale misalignment matrix is ​​compared with the pre-stored historical reference sequence to identify the deviation segment. The historical reference sequence is the standardized time-series signal formed by the battery under known cycle conditions. Reference patterns are extracted from historical reference sequences, and segments that deviate from the reference patterns are identified through differential comparison, and corresponding abnormal indicators are extracted. Output the temporal misalignment mapping results that characterize the difference between local fluctuations and global trends; By utilizing multi-layer coupled feature vectors and combining local interface stability, micro-region thermal gradient and neighborhood sensing unit response consistency, a multi-dimensional priority gradient map reflecting potential local anomalies is constructed. The evolution trajectory of the multidimensional priority gradient map is tracked, and the information of high-rate cycling, local stress concentration and expansion disturbance of the battery is integrated to comprehensively evaluate the abnormal evolution speed, signal gradient change and contact reliability decay, and generate dynamic priority intervention sequence. Based on the dynamic priority intervention sequence, the temperature and pressure distribution of the flexible sensing array is corrected in real time, and an early warning signal is output when the local abnormal evolution reaches the set threshold.

2. The battery global temperature and pressure distribution monitoring method based on flexible sensing materials according to claim 1, characterized in that, The process of constructing a multi-layer coupled feature vector by combining local temperature and pressure fluctuations, micro-deformation shifts, and electrolyte expansion interference factors with historical cyclic data is as follows: Multidimensional parameters such as local temperature change rate, pressure change amplitude, and micro-deformation displacement are extracted from signals collected by the flexible sensing unit. An electrolyte diffusion model is introduced to quantify the electrode spacing perturbation and add it as an interference factor to the feature set; By combining typical anomaly patterns in the historical cyclic database, parameters are aligned and filtered. By sequentially connecting the feature segments on the time axis, a multi-layered coupled feature vector in a unified feature space is generated.

3. The battery global temperature and pressure distribution monitoring method based on flexible sensing materials according to claim 1, characterized in that, The process of constructing a multidimensional priority gradient map reflecting potential local anomalies is as follows: The multi-layer coupling feature vector obtained by the temporal misalignment mapping is input into the clustering analysis model and divided into normal segments, slowly drifting segments and abrupt change segments. The spatial consistency factor is calculated based on the response correlation between neighboring sensing units, and the spatial consistency factor is weighted and corrected using micro-region thermal gradient and interface stress distribution. The weighted result is transformed into a multidimensional gradient intensity matrix and mapped to a priority gradient map covering the entire cell.

4. The battery global temperature and pressure distribution monitoring method based on flexible sensing materials according to claim 3, characterized in that, The process of tracing the evolutionary trajectory of a multidimensional priority gradient map is as follows: After obtaining the priority gradient map, high-intensity gradient nodes are selected as the initial tracking source, and their migration paths in the time series are recorded. By combining cyclic data under high-rate charge and discharge conditions, the evolution speed of abnormal nodes that appear during the tracking process is corrected; In the corrected trajectory sequence, abrupt change points and stress concentration areas are identified and marked as key evolution nodes to form a priority trajectory sequence containing time evolution characteristics.

5. The battery global temperature and pressure distribution monitoring method based on flexible sensing materials according to claim 4, characterized in that, The process of generating dynamic priority intervention sequences is as follows: Calculate the risk weight of each key node in the priority trajectory sequence; By correlating risk weights with battery operating status parameters, key abnormal sections can be located. Within the identified abnormal area, a dynamic programming approach is used to generate an intervention path, and the nodes within the path are prioritized according to their risk weights to form a dynamic priority intervention sequence.

6. The battery global temperature and pressure distribution monitoring method based on flexible sensing materials according to claim 5, characterized in that, The process of real-time correction of the temperature and pressure distribution of the flexible sensing array is as follows: Adjust the weight factors of the corresponding sensing units according to the dynamic priority intervention sequence; During the adjustment process, statistical parameters of neighboring units are incorporated to correct local anomalies. Transient interference signals are weakened by adaptive filtering, and the corrected global temperature and pressure distribution is output.

7. The battery global temperature and pressure distribution monitoring method based on flexible sensing materials according to claim 6, characterized in that, The process of outputting an early warning signal when the local abnormal evolution reaches a set threshold is as follows: The corrected temperature and pressure distribution is compared with a preset threshold range to detect whether there are any abnormal nodes that exceed the threshold. The duration of abnormal nodes is statistically analyzed, and if it exceeds the time threshold, it is determined to be a valid abnormality. After determining that the anomaly is valid, a corresponding early warning signal is generated and transmitted to the battery management system interface; At the same time, abnormal feature fragments are stored in the database, and corresponding records are created using index identification.

8. A battery global temperature and pressure distribution monitoring system based on flexible sensing materials, applied to the method described in any one of claims 1-7, characterized in that, include: The capture matrix module performs spatial mapping on the sensor unit signals and generates an initial temperature and pressure response weight matrix to establish the basic acquisition sequence for the full battery. The misalignment reconstruction module combines local temperature and pressure fluctuations, micro-deformations, and expansion disturbances with historical data to form a micro-region coupling feature vector; Gradient mapping module: Combining interface stability, micro-region thermal gradient and neighborhood consistency, it constructs a priority map that reflects local anomalies; Evolution tracking module: Integrates high-rate cycling, stress concentration and expansion perturbation information to assess the rate of abnormal evolution and reliability decay, and generate dynamic intervention sequences; Early warning output module: Based on dynamic intervention sequence, it corrects sensor array data and issues an early warning signal when local anomalies reach a threshold.

Images