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

Abstract

The invention relates to the field of flexible sensing, and discloses a battery global temperature and pressure distribution monitoring system and method based on a flexible sensing material, and the method comprises the steps: carrying out the multi-scale region dynamic capture of flexible sensing units in and on the surface of a battery, generating an initial temperature and pressure response weight matrix through local coupling mapping, and carrying out the measurement of the initial temperature and pressure response weight matrix; establishing a basic signal acquisition sequence covering the whole battery; performing time sequence dislocation mapping on the basic signal acquisition sequence, and combining 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; constructing a multi-dimensional priority gradient map reflecting potential local anomalies by using the multi-layer coupling feature vectors; carrying out evolution trajectory tracking on the multi-dimensional priority gradient map, and carrying out comprehensive evaluation on abnormal evolution speed, signal gradient change and contact reliability attenuation; on the basis of the dynamic priority intervention sequence, temperature and pressure distribution of the flexible sensing array is corrected in real time; the method has the advantage of early and accurate early warning of local abnormity.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of flexible sensing, in particular to a battery global temperature and pressure distribution monitoring system and method based on flexible sensing materials. BACKGROUND

[0002] The existing scheme for monitoring the internal temperature and pressure of a lithium ion battery has proposed to integrate a flexible sensing unit in the battery interior or near the shell, so as to realize in-situ sensing of multiple physical fields of the battery. For example, the prior art arranges a sensor on a PCB at the bottom of a battery cell to detect temperature and pressure, but this scheme can only actually reflect the state of the bottom of the battery cell or the side of the shell, and it is difficult to obtain the real distribution information of each position in the battery cell. In a specific application scenario, when the flexible sensing array is installed in the battery cell or closely attached to the electrode / separator, the effective contact and long-term adhesion stability of the local sensing unit are insufficient, resulting in single-point loss or reading drift under working conditions such as electrolyte swelling, electrode sheet micro-deformation, or safety valve action, thereby forming a blind area when a local hot spot of the battery is initially generated, so that the early local anomaly is difficult to be spatially finely identified and located. This problem is a local distortion occurring at the micro-interface adhesion and local geometric / mechanical coupling, which directly weakens the spatial resolution and early warning capability of the multi-point flexible array under high time-varying working conditions. Therefore, it is necessary to design a battery global temperature and pressure distribution monitoring system and method based on flexible sensing materials for early and accurate early warning of local anomalies. SUMMARY

[0003] In view of the deficiencies of the prior art, the present application provides a battery global temperature and pressure distribution monitoring system and method based on flexible sensing materials, which has the advantages of being able to continuously provide multi-point temperature and pressure distribution data without blind area under complex stress and dynamic swelling environment of the battery, and early and accurate early warning of local anomalies, thereby solving the problems in the background art.

[0004] To achieve the above-mentioned purpose of being able to continuously provide multi-point temperature and pressure distribution data without blind area under complex stress and dynamic swelling environment of the battery, and early and accurate early warning of local anomalies, the present application provides the following technical scheme: a battery global temperature and pressure distribution monitoring method based on flexible sensing materials, comprising the following steps: Multi-scale regionalized collection is performed on the temperature and pressure signals output by the flexible sensing units arranged in the interior and surface of the battery, and based on the electrode sheet stacking density, the separator swelling tendency, and the interface stress gradient, an initial temperature and pressure response weight matrix is generated through local coupling mapping, and a basic signal collection sequence covering the entire battery is established; Time sequence misplacement mapping is performed on the basic signal collection sequence, the local temperature and pressure fluctuations, micro-deformation shifts, and electrolyte swelling interference factors reflected by the flexible sensing units are combined with the temperature and pressure change trajectories formed in the previous charging and discharging cycles, the interface stability records, and the stress distribution data to fuse 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 the 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; Align and filter parameters in combination with typical abnormal patterns in historical cycle database; Connect each feature segment in sequence on time axis to generate multi-layer coupled feature vectors in unified feature space.

[0008] Preferably, the process of constructing a multi-dimensional priority gradient map reflecting potential local anomalies is as follows: Input the multi-layer coupled feature vectors obtained by time series misalignment mapping into a clustering analysis model to divide them into normal sections, slow drift sections and mutation sections; Calculate a spatial consistency factor based on the response correlation between adjacent sensing units, and weight and correct the spatial consistency factor using micro-area thermal gradient and interface stress distribution; Convert the weighted result into a multi-dimensional gradient intensity matrix, and map it into a priority gradient map covering the entire battery.

[0009] Preferably, the process of tracking the evolution trajectory of the multi-dimensional priority gradient map is as follows: After obtaining the priority gradient map, select a high-intensity gradient node as the initial tracking source, and record its migration path in the time series; Combine the cycle data under high-rate charging and discharging conditions to correct the evolution speed of abnormal nodes appearing in the tracking process; In the corrected trajectory sequence, identify mutation points and stress concentration areas, mark them as key evolution nodes, and 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; Associate the risk weight with the battery operating state parameters to locate the key abnormal section; In the located abnormal area, use a dynamic programming method to generate an intervention path, and prioritize the nodes in the path according to the risk weight of the nodes, forming a dynamic priority intervention sequence.

[0011] Preferably, the process of real-time correction of temperature and pressure distribution of the flexible sensing array is as follows: Adjust the weight factor of the corresponding sensing unit according to the dynamic priority intervention sequence; In the adjustment process, combine the statistical parameters of the adjacent units to correct the local anomaly deviation; Weaken the transient interference signal by an adaptive filtering method, and output the corrected global temperature and pressure distribution.

[0012] Preferably, the process of outputting an early warning signal when the local anomaly evolution reaches a set threshold is as follows: Compare the corrected temperature and pressure distribution with the preset threshold interval to detect whether there are abnormal nodes exceeding the threshold; The duration of the abnormal node is counted, and if it exceeds the time threshold, it is determined to be an effective anomaly; After determining that the anomaly is effective, a corresponding early warning signal is generated and transmitted to the battery management system interface; At the same time, the abnormal feature segment is stored in the database, and the corresponding record is established in the index identification mode.

[0013] The battery global temperature and pressure distribution monitoring system based on flexible sensing material, characterized in that it comprises: Capture matrix module: spatially map the sensing unit signal and generate an initial temperature and pressure response weight matrix to establish a full battery basic collection sequence; Misalignment reconstruction module: combine local temperature and pressure fluctuations, micro-deformation and swelling disturbance with historical data to form a micro-zone coupling feature vector; Gradient mapping module: combine interface stability, micro-zone thermal gradient and neighborhood consistency to construct a priority map reflecting local anomalies; Evolution tracking module: fuse high-rate cycling, stress concentration and swelling disturbance information to evaluate anomaly evolution speed and reliability decay, and generate a dynamic intervention sequence; Early warning output module: based on the dynamic intervention sequence, correct the sensing array data, and issue an early warning signal when the local anomaly reaches the threshold.

[0014] Compared with the prior art, the battery global temperature and pressure distribution monitoring system and method based on flexible sensing material have the following beneficial effects: The present application sets up. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The present application is a schematic diagram of the method; Figure 2 The present application is a schematic diagram of the structure. DETAILED DESCRIPTION

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

[0017] Example 1: Please refer to Figure 1 As shown in the drawings, the battery global temperature and pressure distribution monitoring method based on flexible sensing material described in the embodiments of the present application comprises the following steps: S1: Multi-scale regionalized acquisition of temperature and pressure signals output by flexible sensing units arranged inside and on the surface of the battery, and based on the stacking density of the electrode, the swelling tendency of the separator, and the stress gradient at the interface, 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 a 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 shell outer region, the electrode stacking region, and the separator interface region. Different regions have obvious differences in thermal conductivity, mechanical constraints, and material expansion characteristics, so a multi-scale regionalized acquisition method is adopted: high-density sensing units are arranged in local areas where heat is concentrated or mechanical coupling is significant to capture subtle temperature and pressure changes; low-density sampling points are arranged in stable areas to balance overall energy consumption and signal distribution. This makes signal acquisition both globally covered and finely monitored in key areas.

[0019] Important structural parameters affecting local temperature and pressure changes in the battery are determined, including the stacking density of the electrode, the swelling tendency of the separator, and the stress gradient at the interface. The stacking density of the electrode reflects the compaction degree of the electrode material in space, which has a direct impact on the thermal diffusion path and internal stress conduction; the swelling tendency of the separator indicates the thickness change ability of the separator material during charging and discharging cycles, which is used to indicate the adjustment effect of thermal strain on local pressure distribution; the stress gradient at the interface characterizes the non-uniformity of stress transmission between the electrode, the separator, and the shell, and is used to identify high-risk areas of stress concentration. According to the spatial distribution of these parameters, the signal sampling density and sampling weight of each monitoring area are dynamically adjusted to highlight the response sensitivity to key structural positions.

[0020] After completing the regional division and parameter extraction, the system establishes a local coupling mapping relationship. This mapping takes the structural characteristic parameters in different regions as input, and the temperature signals and pressure signals collected by the flexible sensing units as output. Through the correlation analysis of signal characteristics and structural characteristics, the response strength of different structural units to temperature and pressure changes is evaluated. In each monitoring area, the sensitivity of the temperature signal to the stacking density, the offset trend of the pressure signal to the stress gradient, and the coupling compensation effect caused by the swelling of the separator are calculated to obtain the temperature and pressure response weight values of each region. The weights of all regions are normalized and integrated to form an initial temperature and pressure response weight matrix, which describes the comprehensive response distribution of each position in the battery to thermal and mechanical coupling changes.

[0021] The sampling frequency can be set to 50-200 Hz, and a global time synchronization mechanism is used to ensure the timing consistency of the multi-channel signals. The node end performs signal integrity check, noise filtering and outlier rejection to obtain the temperature and pressure raw signals after quality screening.

[0022] After data acquisition is completed, the acquisition signals of each sensing unit are bound to the three-dimensional geometric model of the battery, the spatial signal distribution is reconstructed according to the layout position of the sensing unit, and the signal amplitude is corrected in combination with the temperature and pressure response weight matrix, so as to form a weighted signal field reflecting the real thermal and force state inside the battery. After normalization and baseline drift correction, the signal field is output as a basic signal acquisition sequence covering the entire battery in time sequence.

[0023] S2: Perform time sequence misplacement mapping on the basic signal acquisition sequence, combine 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 trajectories formed in the previous charging and discharging cycles, interface stability records and stress distribution data to construct a multi-layer coupled feature vector.

[0024] The process of performing time sequence misplacement mapping on the basic signal acquisition sequence in S2 is: Extract continuous time sequence segments from the basic signal acquisition sequence, and perform difference analysis on the temperature, pressure and stress signals in each segment. By calculating the amplitude difference, phase difference and mutation frequency between adjacent sampling points, the basic time sequence features reflecting transient fluctuations and periodic changes are obtained.

[0025] In order to describe the dynamic change trend in different time scales, a multi-scale misplacement matrix is constructed using a sliding time window mechanism. The length and step of the time window are dynamically set according to the signal change rate. Short window is used to capture transient mutation, and long window is used to represent slow change trend. The amplitude, phase and mutation statistical features calculated in each window are combined into a local dynamic feature vector. All windows are spliced along the time axis to form a multi-scale time sequence misplacement matrix, which is used to describe the temperature and pressure dynamic distribution pattern of the battery in different time scales.

[0026] The generated misplacement matrix is compared with the historical reference sequence in window difference. The historical reference sequence is derived from the temperature and pressure records and interface stress distribution model formed by the battery under standard charging and discharging cycles, and is used as a reference for the current state. By comparing the deviation of the misplacement matrix and the reference matrix window by window, the abnormal section and signal offset direction are identified, and the preliminary time sequence misplacement mapping result is obtained.

[0027] The time sequence dislocation results are mapped in a three-dimensional space: time as the horizontal axis, sensor unit position as the vertical axis, and deviation amplitude or mutation intensity as the color or height dimension, to reveal the thermal-force coupling trends in time and space within the battery.

[0028] The process of constructing a multi-layer coupled feature vector in S2 by combining local temperature-pressure fluctuations, micro-deformation offsets, and electrolyte expansion interference factors with historical cycle data is as follows: According to the time sequence dislocation 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 represent the transient characteristics of the local thermal field and force field. The temperature change rate reflects the intensity of thermal conduction fluctuations, the pressure change amplitude represents the degree of local stress mutation, and the micro-deformation displacement reflects the deformation response of the electrode sheet and separator structure.

[0029] An electrolyte diffusion model is introduced to quantitatively analyze the electrode sheet spacing disturbance and electrolyte expansion effect. By dynamically estimating the electrolyte volume change and electrode sheet spacing offset under different charge and discharge states, the disturbance is converted into an interference factor vector. This vector corresponds to the sensor unit position in space and is used to correct the actual response amplitude of the 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 cycle database. The historical database includes temperature-pressure trajectories, interface stability curves, and stress distribution templates under normal and abnormal cycles. Time scale alignment and amplitude normalization strategies are used to project the current feature set into the reference mode space; through similarity evaluation and statistical filtering, the feature dimensions sensitive to abnormal prediction or interface stability changes are retained, and noise or redundant information is removed.

[0031] Different sources of feature segments are sequentially connected on the time axis to construct a multi-layer coupled feature vector in a unified feature space, which consists of three layers: the first layer is the dynamically corrected temperature-pressure signal layer, reflecting the global time structure; the second layer is the local deformation and stress response layer, representing the spatial distribution of electrochemical stress and structural strain; the third layer is the electrolyte disturbance correction layer, describing the interference degree of environmental and material coupling factors. The three-layer feature vectors are weighted and fused to generate a multi-layer coupled feature vector representing the differences between local fluctuations and global trends.

[0032] S3: Using the multi-layer coupled feature vector, combined with local interface stability, micro-zone thermal gradient, and neighborhood sensor unit response consistency, a multi-dimensional priority gradient map reflecting potential local anomalies is constructed.

[0033] The process of constructing a multi-dimensional priority gradient map reflecting potential local anomalies in S3 is as follows: The multi-layer coupling feature vector obtained by the time sequence offset mapping is input into a clustering analysis model, and multi-dimensional clustering is performed on the collected battery temperature, stress, deformation, and voltage and other multi-source parameters. According to the variation amplitude, variation rate and deviation degree from the historical stable state of the parameters, the time sequence fragments are divided into three state sections: normal section, slow drift section and mutation section; the normal section corresponds to the parameter fluctuation within the historical reference range, the slow drift section indicates that the parameters appear continuous deviation in a long time, and the mutation section indicates that the local parameter variation amplitude exceeds the preset threshold. The clustering result can output the deviation degree of each data fragment relative to the category center, which is used to quantify the significance of abnormal change.

[0034] Based on the spatial distribution relationship between the adjacent sensing units, the correlation of adjacent sensing units in temperature, stress or deformation and other physical responses is calculated to obtain a spatial consistency factor. The spatial consistency factor is used to reflect the response coordination degree of adjacent regions: if the change trend of adjacent sensing units is similar, it indicates that the regional structure response is consistent; if the difference is large, it indicates that there may be local abnormalities or uneven stress. The consistency factor is dynamically updated by a sliding window method to adapt to the changes of real-time running state.

[0035] The spatial consistency factor is weighted and corrected in combination with the micro thermal gradient and the interface stress distribution. The regions with large thermal gradient are given high weight in the correction process to highlight the potential thermal instability risk; the stress concentration regions are also given high weight to reflect the possible mechanical abnormalities. The consistency index after correction can form a local abnormality sensitivity matrix, which is used to describe the abnormal response intensity of each monitoring region.

[0036] The state section results obtained by clustering, the weighted spatial consistency matrix and the micro thermal force characteristics are integrated to generate a multi-dimensional gradient intensity matrix. Each element in the matrix corresponds to the priority gradient value of a specific sensing unit in a specific time window, representing the abnormal risk level of the unit. The multi-dimensional matrix is mapped to the three-dimensional geometric model of the battery to form a priority gradient atlas covering the whole battery. The risk distribution, abnormal intensity change and possible risk propagation direction of each region can be intuitively displayed, which is used to realize early and accurate warning of local abnormalities of the battery.

[0037] S4: Evolution trajectory tracking is performed on the multi-dimensional priority gradient atlas, fusion of battery high-rate cycle, local stress concentration and expansion disturbance information, comprehensive evaluation of abnormal evolution speed, signal gradient change and contact reliability attenuation, and generation of dynamic priority intervention sequence.

[0038] The process of evolution trajectory tracking of the multi-dimensional priority gradient atlas in S4 is: After obtaining the priority gradient atlas, high-intensity gradient nodes are selected as initial tracking sources, and their migration paths in the time sequence are recorded; The system selects nodes with gradient values higher than a preset threshold from the multi-dimensional priority gradient atlas as initial tracking sources, which usually correspond to local anomalies or potential risk areas. The node selection is based on gradient intensity ranking, and key nodes can be determined in combination with the average gradient of the neighborhood. Each node records the spatial coordinates, timestamp, and gradient value, which are used for subsequent trajectory tracking.

[0039] In combination with the cycle data under high-rate charge and discharge conditions, the evolution speed of abnormal nodes appearing in the tracking process is corrected; The system iterates each initial high-gradient node along the time sequence, finds the gradient peak neighborhood in the next time window, determines the position and gradient value of the subsequent node, forms a preliminary evolution path, introduces cycle data under high-rate charge and discharge conditions, compares the historical evolution trend with the current node change, and corrects the evolution speed of the node. The correction method includes: using a weighted average method to smooth the node speed for the neighborhood gradient change; using a historical trend fitting model to compensate the node evolution speed; an adaptive filtering method can be used to reduce the influence of sudden noise and ensure that the trajectory reflects the actual evolution law.

[0040] In the corrected trajectory sequence, identify the mutation points and stress concentration areas in the trajectory, and mark them as key evolution nodes; The system identifies abnormal mutation points and stress concentration areas in the evolution trajectory and marks them as key evolution nodes: mutation point identification: the gradient value of the node changes sharply in the adjacent time window, exceeding the threshold to determine the mutation; stress concentration area identification: in combination with micro-deformation and stress distribution data, the stress in the area where the node is located is obviously concentrated; the key nodes are used as important references for priority intervention and abnormal early warning.

[0041] Form a priority trajectory sequence containing time evolution characteristics; The system integrates the evolution path and key nodes to generate a priority trajectory sequence containing time evolution characteristics: each trajectory contains node spatial coordinates, gradient value, time sequence information, and key node markers; it can be used to visualize the dynamic migration trend of local anomalies and the potential risk propagation path; it provides standardized input for multi-dimensional dynamic intervention, abnormal prediction, and decision analysis.

[0042] The process of generating a dynamic priority intervention sequence in S4 is: 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 the risk weight of each node: the risk weight comprehensively considers the node gradient value, evolution speed, mutation frequency, and neighborhood consistency factor; the weight can be calculated by weighting the micro-zone thermal gradient and the interface stress distribution; the node weight is used to quantify the contribution of each key node to potential abnormalities or faults.

[0043] The risk weight is associated with the battery operating state parameters to locate the key abnormal section. The system analyzes the calculated risk weight and the battery operating state parameters: for each node in the time window, the abnormal section is determined by combining real-time or historical cycle data; the threshold screening or abnormal scoring method is used to identify the section with risk higher than the preset standard to determine the location of the key abnormal section; the spatial topology analysis of the abnormal section ensures the continuity and integrity of the section.

[0044] In the located abnormal area, a dynamic programming method is used to generate an intervention path, and the nodes in the path are prioritized according to the risk weight of the nodes to form a dynamic priority intervention sequence. The system generates an intervention path based on the key abnormal section and the risk weight of the key nodes using dynamic programming or optimal path search method: the battery space nodes and time sequence are modeled as a graph structure, and each node has a risk weight attribute; the dynamic programming method calculates the optimal intervention path from the starting point to the ending point according to the risk accumulation minimization or intervention priority maximization principle; the nodes in the path are sorted according to the risk weight, and the high-risk nodes are 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 a set threshold.

[0046] The process of real-time correction of the temperature and pressure distribution of the flexible sensing array in S5 is: According to the dynamic priority intervention sequence, adjust the weight factor of the corresponding sensing unit; The system identifies the key nodes and abnormal sections that need to be intervened according to the dynamic priority intervention sequence, and assigns weight factors to the corresponding sensing units: the weight factor reflects the priority and correction strength of the sensing unit in the temperature and pressure distribution correction; high-risk nodes are assigned higher weights to ensure that the correction takes effect on the key abnormal area; the system automatically updates the weight factor table, including sensing unit ID, time window, and adjustment amplitude.

[0047] In the adjustment process, combine the statistical parameters of the neighborhood units to correct the local abnormal deviation; The system combines the neighborhood unit statistical parameters of each sensing unit: calculates the local mean, variance and gradient change trend, which is used to identify local abnormal deviation; corrects the sensing unit deviating from the neighborhood statistical characteristics, so that its output signal is consistent with the whole neighborhood; the correction method can include weighted mean, local regression or spatial filtering, so that the correction maintains physical continuity.

[0048] The transient interference signal is weakened by an adaptive filtering method, 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 influence of transient noise and interference signals: adaptive low-pass filtering, Kalman filtering or recursive smoothing method can be used; during the filtering process, the filtering parameters are adjusted according to the sensing unit weight and abnormal characteristics to ensure signal fidelity 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 of outputting an early warning signal when the local abnormal evolution reaches a set threshold in S5 is: Compare the corrected temperature and pressure distribution with the preset threshold interval to detect whether there are abnormal nodes exceeding the threshold; The system compares the corrected global temperature and pressure distribution with the preset threshold interval: checks whether the temperature, pressure and micro-deformation signals of each sensing unit exceed the corresponding threshold; marks the nodes exceeding the threshold as potential abnormal nodes, and records their spatial position, time window and exceeding amplitude; local abnormal clusters can be identified in combination with neighborhood statistical characteristics to exclude isolated noise points.

[0050] The duration of the abnormal node is counted, and if it exceeds the time threshold, it is determined to be an effective abnormality; The system analyzes the detected potential abnormal nodes in the time dimension: counts the existence duration of each abnormal node in the continuous time window; compares the duration with the preset time threshold to determine whether it is an effective abnormality; an effective abnormal node indicates that the local abnormal evolution has reached a critical state and may need intervention.

[0051] After determining the effectiveness of the abnormality, the corresponding early warning signal is generated and transmitted to the battery management system interface; The system generates an early warning signal according to the effective abnormal node: encodes the abnormal type, node position, risk level and occurrence time into standardized warning information; transmits the warning signal 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 implement a multi-level response mechanism.

[0052] At the same time, the abnormal feature fragments are stored in the database and corresponding records are established in an index identification manner; The system stores the corresponding abnormal feature fragments into the database: save the complete feature fragments including temperature and pressure change sequence, micro-deformation data, evolution track and related weight information; establish index identification for each record, facilitate subsequent model training, threshold optimization and historical data backtracking analysis; record storage time, data source and associated early warning signal ID at the same time, ensure data management and traceability.

[0053] Embodiment 2: As shown in Figure 2 The battery global temperature and pressure distribution monitoring system based on flexible sensing material, characterized in that, comprising: Capture matrix module: spatially map the sensing unit signals and generate an initial temperature and pressure response weight matrix to establish a full battery basic collection sequence; Misalignment reconstruction module: combine local temperature and pressure fluctuations, micro-deformation and swelling disturbance with historical data to form a micro-zone coupled feature vector; Gradient mapping module: combine interface stability, micro-zone thermal gradient and neighborhood consistency to construct a priority map reflecting local anomalies; Evolution tracking module: fuse high-rate cycle, stress concentration and swelling disturbance information to evaluate abnormal evolution speed and reliability decay, and generate a dynamic intervention sequence; Early warning output module: based on the dynamic intervention sequence, correct the sensing array data, and issue an early warning signal when the local anomaly reaches the threshold.

[0054] It should be noted that in this text, 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. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0055] Although embodiments of the present application 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 thereto without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the temperature and pressure distribution of a battery based on a flexible sensing material, characterized in that, The method comprises the following steps: Multi-scale regionalized collection is performed on temperature and pressure signals output by flexible sensing units arranged inside and on the surface of the battery, and based on the electrode sheet stacking density, the diaphragm swelling 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 collection sequence covering the entire battery; Time sequence misplacement mapping is performed on the basic signal collection sequence, local temperature and pressure fluctuations, micro-deformation shifts and electrolyte swelling interference factors reflected by the flexible sensing units are combined with temperature and pressure change trajectories formed in previous charging and discharging cycles, interface stability records and stress distribution data to construct a multi-layer coupled feature vector; The multi-layer coupled feature vector is used to construct a multi-dimensional priority gradient map reflecting potential local abnormalities in combination with local interface stability, micro-zone thermal gradient and neighborhood sensing unit response consistency; Evolution trajectory tracking is performed on the multi-dimensional priority gradient map, and battery high-rate cycle, local stress concentration and swelling disturbance information are fused to comprehensively evaluate abnormal evolution speed, signal gradient change and contact reliability decay, and a dynamic priority intervention sequence is generated; Based on the dynamic priority intervention sequence, real-time correction is performed on the temperature and pressure distribution of the flexible sensing array, and an early warning signal is output when the local abnormal evolution reaches a set threshold. 2.The flexible sensor material-based battery global temperature and pressure distribution monitoring method of claim 1, wherein, The process of establishing a basic signal collection sequence covering the entire battery is as follows: Flexible sensing units are arranged in the electrode sheet stack, diaphragm and inner wall region of the shell of the battery; Parallel data collection is performed on different regions by distributed sampling nodes; Based on the three-dimensional geometric model of the battery, the temperature signals and pressure signals collected by the flexible sensing units are mapped to the spatial coordinate system; The temperature signals and pressure signals are normalized and baseline drift corrected to output the corrected basic signal collection sequence of the entire battery. 3.The flexible sensor material-based battery global temperature and pressure distribution monitoring method of claim 2, wherein, The process of performing time sequence misplacement mapping on the basic signal collection sequence is as follows: Continuous time sequence segments are extracted from the basic signal collection sequence, and the amplitude difference, phase difference and mutation frequency of adjacent sampling points in the continuous time sequence segments are calculated; A multi-scale misplacement matrix is generated using a sliding time window to represent the dynamic change mode of the temperature and pressure distribution; The multi-scale misplacement matrix is compared with a pre-stored historical reference sequence to identify deviated sections, and the historical reference sequence is a standardized time sequence signal formed by the battery under known cycle conditions; A reference mode is extracted from the historical reference sequence, and sections deviating from the reference mode are identified by difference comparison, and corresponding abnormal indicators are extracted; The time sequence misplacement mapping result representing the difference between local fluctuations and global trends is output. 4.The method of claim 3, wherein, The process of constructing a multi-layer coupled feature vector by combining local temperature and pressure fluctuations, micro-deformation shifts and electrolyte swelling interference factors with historical cycle data is as follows: Local temperature change rate, pressure change amplitude and micro-deformation displacement multi-dimensional parameters are extracted from the flexible sensing unit collection signals; An electrolyte diffusion model is introduced to quantify the electrode sheet spacing disturbance and add it as an interference factor to the feature set; Typical abnormal modes in the historical cycle database are combined to align and filter the parameters; The multi-layer coupled feature vector in the unified feature space is generated by connecting the feature segments in sequence on the time axis. 5.The flexible sensor material based battery global temperature and pressure distribution monitoring method of claim 4, wherein, The process of constructing a multi-dimensional priority gradient map reflecting potential local anomalies is as follows: The multi-layer coupled feature vectors obtained by time series misalignment mapping are input into a clustering analysis model to divide them into normal sections, slow drift sections and mutation sections; Based on the response correlation between adjacent sensing units, a spatial consistency factor is calculated, and the spatial consistency factor is weighted and corrected using micro-zone thermal gradient and interface stress distribution; The weighted results are converted into a multi-dimensional gradient intensity matrix and mapped into a priority gradient map covering the entire battery. 6.The flexible sensor material based battery global temperature and pressure distribution monitoring method of claim 5, wherein, The process of tracking the evolution trajectory of the multi-dimensional priority gradient map is as follows: After obtaining the priority gradient map, select high-intensity gradient nodes as the initial tracking source and record their migration path in the time series; Combine the cycle data under high-rate charging and discharging conditions to correct the evolution speed of abnormal nodes in the tracking process; In the corrected trajectory sequence, identify the mutation points and stress concentration areas and mark them as key evolution nodes to form a priority trajectory sequence containing time evolution characteristics. 7.The flexible sensor material based battery global temperature and pressure distribution monitoring method of claim 6, wherein, 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; Correlate the risk weight with the battery operating state parameters to locate the key abnormal section; In the located abnormal area, use dynamic programming method to generate intervention path, and according to the risk weight size of the node, the nodes in the path are prioritized to form a dynamic priority intervention sequence. 8.The battery global temperature and pressure distribution monitoring method based on the flexible sensing material according to claim 7, wherein, The process of real-time correction of temperature and pressure distribution of flexible sensing array is as follows: Adjust the weight factor of the corresponding sensing unit according to the dynamic priority intervention sequence; In the adjustment process, combine the statistical parameters of the adjacent units to correct the local abnormal deviation; Through adaptive filtering method, weaken the transient interference signal, and output the corrected global temperature and pressure distribution. 9.The battery global temperature and pressure distribution monitoring method based on the flexible sensing material of claim 8, wherein, The process of outputting early warning signal when local anomaly evolution reaches the set threshold is as follows: Compare the corrected temperature and pressure distribution with the preset threshold interval to detect whether there are abnormal nodes exceeding the threshold; Statistically analyze the duration of abnormal nodes, and if it exceeds the time threshold, it is determined to be an effective anomaly; After determining the anomaly, generate the corresponding early warning signal and deliver it to the battery management system interface; At the same time, store the abnormal feature segment into the database and establish the corresponding record in the index identification way.

10. A battery global temperature and pressure distribution monitoring system based on flexible sensing material, applied to the method according to any one of claims 1 to 9, characterized in that, It includes: Capture matrix module: spatially map the sensing unit signals and generate an initial temperature and pressure response weight matrix to establish a full battery basic acquisition sequence; Misalignment reconstruction module: combine local temperature and pressure fluctuations, micro-deformation and swelling disturbance with historical data to form micro-zone coupled feature vectors; Gradient mapping module: combine interface stability, micro-zone thermal gradient and neighborhood consistency to construct a priority map reflecting local anomalies; Evolution tracking module: integrate high-rate cycling, stress concentration and swelling disturbance information to evaluate abnormal evolution speed and reliability decay and generate a dynamic intervention sequence; Early warning output module: based on the dynamic intervention sequence, correct the sensing array data and issue an early warning signal when the local anomaly reaches the threshold.

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