A method and system for early warning of abnormal battery expansion based on flexible pressure monitoring

Abstract

This invention belongs to the field of battery management technology, specifically disclosing a method and system for early warning of abnormal battery expansion based on flexible pressure monitoring. Addressing the problems of poor adhesion of rigid sensors, easy masking of weak deformation signals under high stacking pressure, and severe temperature-pressure coupling interference, this invention first fabricates a flexible pressure sensing unit with a three-dimensional porous conductive network, then installs a heterogeneous sensing array, employs a multi-state machine adaptive energy-saving method to acquire dual-modal data, aligns the data through spatiotemporal interpolation, decouples temperature and pressure using a binary polynomial coupling model and dynamic compensation technology, extracts multi-dimensional spatiotemporal features, and finally constructs a comprehensive risk assessment model to achieve abnormal expansion location and graded early warning. This invention improves the physical sensing sensitivity and signal-to-noise ratio of small expansion signals under high pre-tightening conditions, further enhancing the early warning accuracy under complex operating conditions. Combined with a multi-state machine adaptive energy-saving strategy, it achieves low power consumption and high robustness for long-term online monitoring.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a method and system for early warning of abnormal battery expansion based on flexible pressure monitoring. Background Technology

[0002] With the rapid development of new energy vehicles and large-scale energy storage systems, lithium-ion power batteries, as core energy storage units, have become a key factor restricting the further development of the industry in terms of safety, reliability, and service life. In actual operation, power batteries are subjected to complex operating conditions for extended periods, such as high-rate charging and discharging, frequent cycling, and wide-temperature environments. These factors not only cause capacity decay and electrochemical performance degradation but may also induce serious safety accidents such as thermal runaway. Among numerous failure symptoms, battery swelling is a typical and important physical characteristic with early warning significance. Battery swelling is usually caused by factors such as gas production from internal side reactions, electrolyte decomposition, and electrode structure degradation, and its development often precedes signals such as temperature and voltage anomalies. Therefore, real-time monitoring of power battery swelling behavior or changes in stacking pressure helps to identify changes in the battery's internal structure in advance, providing crucial evidence for battery health status assessment, fault diagnosis, and early warning of thermal runaway.

[0003] Currently, power battery systems mainly rely on battery management systems to monitor and manage parameters such as voltage, current, and temperature. However, these electrical and thermal parameters primarily reflect the macroscopic state of the battery and are slow to respond to changes in the battery's internal mechanical structure, making it difficult to capture early abnormal signals in a timely manner and resulting in certain monitoring blind spots.

[0004] To address the battery swelling problem, various monitoring methods have been proposed in existing technologies, mainly including the following categories:

[0005] (1) An external displacement sensor indirectly reflects the degree of expansion by measuring the deformation of the battery casing;

[0006] (2) Rigid pressure sensor, used to detect changes in stacking pressure within the battery module;

[0007] (3) Strain gauges or fiber optic sensors are used to achieve indirect measurement through structural deformation.

[0008] However, the above methods generally have the following shortcomings:

[0009] First, traditional sensors are mostly rigid structures, making it difficult to achieve a high degree of contact with the battery surface, especially when the internal space of the battery module or battery pack is limited, making integration difficult.

[0010] Second, in the working environment where battery modules are usually subjected to high preload, the sensor needs to distinguish minute dynamic changes under a large static background pressure, and the sensitivity and resolution of existing sensors are difficult to achieve simultaneously.

[0011] Third, during vehicle operation, there are vibrations, shocks, and temperature fluctuations, which make rigid sensors less stable and prone to measurement errors.

[0012] Fourth, single-point monitoring methods are insufficient to reflect the expansion differences between different cells within a battery module and lack spatial resolution.

[0013] In recent years, the development of flexible electronics and flexible sensing technologies has provided new technical solutions to the aforementioned problems. Flexible pressure sensors possess excellent flexibility, attachability, and potential for array integration, enabling them to adapt to the complex structural environments of battery surfaces and module interiors, and show broad application prospects in the field of power battery safety monitoring.

[0014] However, existing flexible pressure sensors still face the following key technical challenges in practical applications with power batteries:

[0015] (1) Insufficient ability to distinguish small signals under high preload pressure: Power battery modules are usually subjected to large preload during assembly. The sensor needs to detect small expansion changes under high baseline pressure, which puts higher requirements on sensitivity and linear range.

[0016] (2) Significant coupling problem between temperature and pressure signals: The electrical properties of flexible piezoresistive materials are easily affected by temperature, which causes the pressure measurement results to drift and reduces the accuracy of monitoring;

[0017] (3) Limited spatial monitoring capability in multi-cell scenarios: The internal structure of the battery pack is complex, and it is difficult to capture local abnormal expansion areas by single-point or low-density deployment;

[0018] (4) Long-term online monitoring has prominent stability and power consumption issues: Flexible sensors have problems such as aging and drift during long-term service, and multi-point array monitoring will bring high energy consumption burden;

[0019] (5) Lack of system-level integrated monitoring and intelligent early warning mechanism: Existing solutions mostly rely on single sensing or simple threshold judgment, lacking intelligent analysis and dynamic early warning capabilities based on multi-dimensional data fusion. Summary of the Invention

[0020] This invention provides a method and system for early warning of abnormal battery expansion based on flexible pressure monitoring. The technical problem it solves is: how to achieve high-sensitivity acquisition and high-precision, low-power early warning of minute abnormal battery expansion pressure signals under the harsh environment of high stacking pre-tightening pressure, complex temperature alternation and confined space constraints inside the power battery module.

[0021] To address the above technical problems, this invention provides a method for early warning of abnormal battery expansion based on flexible pressure monitoring, comprising:

[0022] A flexible porous piezoresistive sensitive layer with three-dimensional interconnected pores is prepared, and a flexible pressure sensing unit is prepared based on the flexible porous piezoresistive sensitive layer;

[0023] Multiple flexible pressure sensing units and temperature sensing units are regularly attached to the surface of the battery module to obtain a heterogeneous sensing array.

[0024] Under different operating conditions and external ambient temperatures, an adaptive energy-saving control strategy based on a multi-state machine is adopted. The heterogeneous sensor array collects pressure array data and temperature array data under pre-tightening pressure background in real time to obtain dual-mode array data.

[0025] The dual-modal array data is time-aligned and spatial-aligned to obtain dual-modal aligned array data;

[0026] The dual-mode aligned array data is corrected by temperature and pressure coupling to obtain the real pressure array data, and the corresponding relative expansion pressure change array data is calculated.

[0027] Multidimensional feature parameters are extracted based on the array data of the relative expansion pressure change.

[0028] Based on the multidimensional feature parameters, a risk assessment index is calculated. When the risk assessment index exceeds a dynamically preset threshold, the coordinates of the cell where the abnormal expansion occurs are located, and a warning signal is output to the battery management system.

[0029] Furthermore, the fabrication process of the flexible porous piezoresistive sensitive layer is as follows:

[0030] Two-dimensional conductive nanomaterials are uniformly mixed with an elastic polymer matrix to obtain a hybrid matrix;

[0031] Sodium chloride particles and polymethyl methacrylate microspheres are added to the mixed matrix as sacrificial template particles, and a uniform composite slurry is formed by stirring and vacuum degassing.

[0032] The composite slurry is poured into a mold and thermo-cured and cross-linked under a constant temperature environment to obtain a solid elastomer.

[0033] The sacrificial module particles in the solid elastomer are removed by water-soluble and solvent etching to form a three-dimensional porous piezoresistive network with a porosity of 40%-80% and a pore size range of 10-200μm.

[0034] The three-dimensional porous piezoresistive network is cut to obtain a flexible porous piezoresistive sensitive layer.

[0035] Furthermore, the flexible pressure sensing unit is formed by sequentially bonding and encapsulating a lower flexible insulating substrate layer, an interdigitated electrode layer, a flexible porous piezoresistive sensitive layer, a buffer transition layer, and an upper flexible protective layer; both the lower flexible insulating substrate layer and the upper flexible protective layer are made of thermoplastic polyurethane material; the buffer transition layer uses polydimethylsiloxane silicone rubber as the base material, and by changing the addition ratio of crosslinking agent in the thickness direction, or by incorporating silica nanoparticles with a gradient mass fraction, a gradient distribution structure with low bottom modulus and high top modulus is formed.

[0036] Furthermore, the multiple flexible pressure sensing units and temperature sensing units are regularly attached to the surface of the battery module, specifically as follows:

[0037] For the flexible pressure sensing unit, the main monitoring point is set at the geometric center of the large surface of the cell, and the auxiliary monitoring point is set at 1 / 4 of the diagonal direction, forming a cross or quincunx topology.

[0038] A temperature sensing unit is placed at the diagonal geometric intersection of every four adjacent pressure sensing units.

[0039] Furthermore, the adaptive energy-saving control strategy based on multi-state machines is as follows:

[0040] The working mode of the heterogeneous sensor array is adaptively switched according to the vehicle's operating status, which is either high-frequency monitoring mode, sleep-wake mode, or local alert mode.

[0041] The high-frequency monitoring mode is as follows: when a vehicle start signal is received or the battery management system is in a high-rate charging / discharging or fast-charging state, the heterogeneous sensor array is fully opened to collect dynamic pressure and temperature data in real time at a preset high-frequency sampling rate.

[0042] The sleep-wake mode is as follows: when the vehicle is turned off and the ambient temperature is within a safe operating range, the system enters a microampere-level low-power sleep mode, and the internal timer periodically wakes up the heterogeneous sensor array to perform a single inspection.

[0043] The local alert mode is as follows: during the dormant inspection period, if a node in the heterogeneous sensor array detects that the pressure change rate or absolute temperature exceeds the set low-level alert threshold, only the sensors of the abnormal node and its adjacent topology area are activated to resume high-frequency continuous sampling.

[0044] Furthermore, the dual-modal aligned array data is corrected by temperature and pressure coupling to obtain the actual pressure array data, specifically as follows:

[0045] The dual-modal aligned array data is corrected for temperature and pressure coupling using a binary polynomial temperature-pressure coupling correction model to obtain the true absolute pressure of the current sensing node:

[0046] ,

[0047] in, to These are the model fitting coefficients. Indicates the location index of the sensor node. Indicates time, , These represent the pressure array data and temperature array data in the dual-modal aligned array data, respectively.

[0048] Furthermore, the current moment in the relative expansion pressure change array data node The relative change in expansion pressure is:

[0049] ,

[0050] in, For nodes The average data during the baseline period after assembly at standard room temperature. This refers to low-frequency zero drift caused by aging of sensor materials or creep of module structural components.

[0051] Furthermore, the multidimensional feature parameters include:

[0052] Local pressure change rate ;

[0053] Cumulative Increment of Local Pressure in Space , For cumulative time periods, For any point in time within the cumulative time period;

[0054] Historical Cyclic Comparison Bias , This is standard historical data under the same health conditions.

[0055] Furthermore, the risk assessment index is equal to the weighted sum of the rate of change of local space pressure, the cumulative increment of local space pressure, and the historical cycle comparison deviation;

[0056] The dynamically preset threshold is related to the current state of charge of the battery cell and the ambient temperature.

[0057] This invention also provides a battery abnormal expansion early warning system based on flexible pressure monitoring. The key to applying the battery abnormal expansion early warning method based on flexible pressure monitoring is that the system includes the heterogeneous sensor array, a signal acquisition module, a signal preprocessing module, a temperature and pressure decoupling multiple compensation module, a multi-dimensional spatiotemporal feature extraction module, and a battery abnormal expansion early warning module. The signal acquisition module, the signal preprocessing module, the temperature and pressure decoupling multiple compensation module, the multi-dimensional spatiotemporal feature extraction module, and the battery abnormal expansion early warning module are respectively used to perform the above steps S3, S4, S5, S6, and S7.

[0058] This invention provides a battery abnormal expansion early warning method and system based on flexible pressure monitoring. Addressing the pain points of poor adhesion of rigid sensors, easy masking of weak deformation signals under high stacking pressure, and severe temperature-pressure coupling interference, this invention introduces a sacrificial template method and exposed interdigitated electrode technology to construct a three-dimensional porous conductive network structure within the elastomer composite conductive material. Utilizing the low Poisson's ratio stress concentration effect of the porous structure, it significantly enhances the response capability to small deformations under high pre-tightening background pressure, greatly improving the physical sensing signal-to-noise ratio and sensitivity coefficient in the initial strain stage. Furthermore, it constructs a decoupling and evaluation architecture based on multi-dimensional spatiotemporal characteristics, utilizing spatial interpolation algorithms... By accurately aligning heterogeneous sensor array data and utilizing a binary polynomial coupling model and dynamic compensation technology, the nonlinear temperature and pressure drift law of flexible materials is deeply learned. The algorithm fits and compensates for the complex errors caused by alternating temperature and prestress. Finally, the weak expansion characteristics hidden in the high-stress baseline are mathematically decoupled from the environmental temperature interference in a high dimension. Combined with a multi-state machine adaptive energy-saving strategy, the large-area, high-precision stable identification of expansion state is successfully achieved in complex dynamic scenarios such as vehicle charging and discharging, and shutdown and hibernation. This effectively improves the safety perception capability and long-term operational robustness of power batteries and energy storage systems in harsh operating environments. Attached Figure Description

[0059] Figure 1 This is a flowchart of a battery abnormal expansion early warning method based on flexible pressure monitoring provided in an embodiment of the present invention;

[0060] Figure 2 This is a structural diagram of the flexible pressure sensing unit provided in an embodiment of the present invention;

[0061] Figure 3 This is a diagram illustrating the layout of a heterogeneous sensor array provided in an embodiment of the present invention.

[0062] Figure 4 This is a structural diagram of a battery abnormal expansion early warning system based on flexible pressure monitoring provided in an embodiment of the present invention. Detailed Implementation

[0063] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments are given for illustrative purposes only and should not be construed as limiting the present invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0064] This invention first provides a method for early warning of abnormal battery expansion based on flexible pressure monitoring, the process of which is as follows: Figure 1 As shown, the steps include:

[0065] S1. Fabrication of flexible pressure sensing unit: Fabrication of flexible porous piezoresistive sensitive layer 3 with three-dimensional interconnected pores, and fabrication of flexible pressure sensing unit based on flexible porous piezoresistive sensitive layer 3.

[0066] S2. Installing the heterogeneous sensor array: Regularly attach multiple flexible pressure sensing units and temperature sensing units to the surface of the battery module to obtain a heterogeneous sensor array.

[0067] S3. Adaptive energy-saving acquisition of dual-modal array data: When the battery module is under different operating conditions and external ambient temperature, an adaptive energy-saving control strategy based on a multi-state machine is adopted. The heterogeneous sensor array collects pressure array data and temperature array data under pre-tightening pressure background in real time to obtain dual-modal array data.

[0068] S4. Perform spatiotemporal mapping and alignment on the bimodal array data: Perform time and spatial alignment on the bimodal array data to obtain bimodal aligned array data;

[0069] S5. Temperature and pressure decoupling and multiple compensation: Temperature and pressure coupling correction is performed on the dual-mode aligned array data to obtain the real pressure array data, and the corresponding relative expansion pressure change array data is calculated.

[0070] S6. Multidimensional spatiotemporal feature extraction: Extract multidimensional feature parameters based on the array data of relative expansion pressure change;

[0071] S7. Battery Abnormal Expansion Warning: Calculate the risk assessment index based on the multi-dimensional feature parameters. When the risk assessment index exceeds the dynamic preset threshold, locate the cell coordinates where the abnormal expansion occurs and output a warning signal to the battery management system.

[0072] This invention provides a method for early warning of abnormal battery expansion based on flexible pressure monitoring, wherein:

[0073] A flexible porous piezoresistive sensitive layer with three-dimensional interconnected pores is prepared by S1, and a flexible pressure sensing unit is prepared based on the sensitive layer, so that the sensing unit has high deformation response capability, thereby improving the physical sensing sensitivity to small expansion pressure.

[0074] By regularly attaching multiple flexible pressure sensing units and temperature sensing units to the surface of the battery module through S2, a heterogeneous sensing array is formed, which realizes the synchronous acquisition of pressure and temperature signals, thereby obtaining spatial resolution and being able to capture the expansion differences between different cells.

[0075] By using S3 under different operating conditions and ambient temperatures, an adaptive energy-saving control strategy based on a multi-state machine is adopted. Through a heterogeneous sensor array, pressure array data and temperature array data under pre-tightening pressure background are collected in real time to obtain dual-mode array data, realizing dynamic adjustment of the acquisition frequency, thereby significantly reducing system power consumption while ensuring monitoring effectiveness.

[0076] By performing time and spatial alignment on the dual-mode array data using S4, dual-mode aligned array data is obtained, eliminating the spatiotemporal misalignment between heterogeneous sensors, thus providing a precise data foundation for subsequent temperature and pressure decoupling;

[0077] By performing temperature and pressure coupling correction on the dual-mode aligned array data using S5, the true pressure array data is obtained, and the corresponding relative expansion pressure change array data is calculated. This effectively removes temperature interference and static pre-tightening baseline, thereby obtaining true expansion pressure change information.

[0078] By extracting multidimensional feature parameters based on the relative expansion pressure change array data using S6, the spatiotemporal evolution of battery expansion can be comprehensively characterized, thereby providing a highly discriminative evaluation basis for anomaly identification.

[0079] By inputting multi-dimensional feature parameters into the risk assessment model via S7, when the risk assessment index exceeds the dynamic preset threshold, the coordinates of the cell where abnormal expansion occurs are located and an early warning signal is output to the battery management system. This achieves accurate positioning and dynamic early warning in the early stages of expansion, thereby effectively avoiding safety accidents such as thermal runaway.

[0080] Through the complete process from S1 to S7 described above, this method successfully solves the core problems of difficulty in distinguishing minute expansion changes under high pre-tightening pressure, severe temperature coupling interference, and high power consumption in large-scale deployment, significantly improving the safety perception capability and long-term operational robustness of power batteries in complex operating environments.

[0081] The following provides a more detailed explanation of each step.

[0082] (1) S1: Fabrication of flexible pressure sensing unit

[0083] The hierarchical structure of the flexible pressure sensing unit is as follows: Figure 2 As shown, it is composed of a lower flexible insulating substrate layer 1, an interdigitated electrode layer 2, a flexible porous piezoresistive sensitive layer 3, a buffer transition layer 4, and an upper flexible protective layer 5, which are sequentially bonded and encapsulated.

[0084] The lower flexible insulating substrate 1 is made of thermoplastic polyurethane (TPU) material with a thickness precisely controlled between 25-100μm, and is used to provide basic mechanical support and electrical insulation functions.

[0085] An interdigitated electrode layer 2 is disposed on the lower flexible insulating substrate layer 1. It is made of copper, a highly conductive material, and is formed by screen printing, inkjet printing, or photolithography. The electrode finger width is preferably 50-200 μm, the finger spacing is 50-300 μm, and the overall electrode thickness is 5-20 μm. In particular, the side of the interdigitated electrode layer 2 facing the flexible porous piezoresistive sensing layer 3 is kept exposed or partially covered, so that it can form an adjustable micro-contact interface with the sensing layer under pressure, thereby significantly enhancing the response to contact resistance changes under small deformations.

[0086] The core flexible porous piezoresistive sensing layer 3 is prepared as follows: Preferred MXene two-dimensional conductive nanomaterials are uniformly mixed with an elastic polymer matrix such as Ecoflex silicone rubber to obtain a mixed matrix. Subsequently, NaCl (sodium chloride) particles and PMMA (polymethyl methacrylate) microspheres are added to the mixed matrix as removable sacrificial template particles. A uniform composite slurry is formed through high-frequency stirring and vacuum degassing. The slurry is poured into a mold and heated to 60-80°C. The solid elastomer was obtained by thermosetting and crosslinking under constant temperature for 2-4 hours. Finally, the template particles inside the solid elastomer were completely removed by water-soluble and solvent etching (NaCl particles dissolved in water, PMMA microspheres dissolved in organic solvents), forming a three-dimensional porous piezoresistive network with a porosity of 40%-80% and a pore size range of 10-200 μm. This porous structure endows the material with an extremely low Poisson's ratio, and the collapse of the pore walls under pressure triggers significant remodeling of the conductive path, thus maintaining extremely high piezoresistive sensitivity even under high initial static pressure. Finally, the three-dimensional porous piezoresistive network was cut to obtain a flexible porous piezoresistive sensitive layer 3, which can be used to fabricate flexible pressure sensing units.

[0087] A buffer transition layer 4 is disposed above the flexible porous piezoresistive sensitive layer 3, which uses PDMS (polydimethylsiloxane) silicone rubber as the substrate material. By varying the proportion of crosslinking agent added in the thickness direction, or by incorporating silica nanoparticles with a gradient mass fraction, a gradient distribution structure with low bottom modulus and high top modulus is formed, preferably with a thickness range of 100-50 μm. This layer can achieve uniform distribution of internal stress under high pre-tightening pressure of the battery module, effectively buffering mechanical shocks and vehicle vibrations during charging and discharging, further improving the discernibility of minute pressure changes and reducing hysteresis effects.

[0088] Finally, a flexible protective layer 5 is attached, made of TPU film material with a thickness of 20-100μm, for comprehensive protection against electrolyte corrosion and external moisture interference.

[0089] The flexible pressure sensing unit prepared in step S1 has a five-layer structure that synergistically achieves high sensitivity, low hysteresis, and long-term stable monitoring under high preload pressure, temperature alternation, and vibration shock: the lower flexible insulating substrate layer 1 and the upper flexible protective layer 5 provide electrical insulation and environmental protection, the buffer transition layer 4 uniformly disperses the preload force and vibration energy through gradient modulus, and the exposed interdigitated electrode layer 2 and the flexible porous piezoresistive sensitive layer 3 form a highly fitted micro-contact interface; among them, the flexible porous piezoresistive sensitive layer 3 is made into a three-dimensional interconnected conductive network by the sacrificial template method, which uses the stress concentration effect caused by low Poisson's ratio to significantly amplify small deformations into dramatic changes in resistance, so that it can still clearly distinguish weak expansion signals under high static background pressure, and has a wide linear range, high resilience and low hysteresis characteristics. It is the key to overcoming the core problem that traditional sensors are difficult to capture early expansion characteristics under high pressure background.

[0090] (2) Step S2: Install heterogeneous sensor array

[0091] The specific steps involve: deploying multiple flexible pressure sensing units and temperature sensing units on the surface of the battery module according to a sparse array topology with shared nodes, resulting in a heterogeneous sensing array. The specific topology for the flexible pressure sensing units is as follows: a main monitoring point is placed at the geometric center of the large surface area of ​​the cell, and auxiliary monitoring points are placed at 1 / 4 positions along the diagonal, forming a cross-shaped or quincunx-shaped topology. To achieve dual-modal monitoring of temperature and pressure without redundancy and increased wiring complexity, a shared node architecture is adopted: a temperature sensing unit (thin-film temperature sensor) is placed at the diagonal geometric intersection of every four adjacent pressure sensing units. An example of one topology is shown below. Figure 3 As shown. This deployment method, known as a sparse array topology with shared nodes, can reduce the number of temperature sensors to one-quarter of the original number while ensuring 100% coverage of critical deformation areas. Subsequently, the complete global temperature field information will be recovered at the software layer through spatial interpolation algorithms.

[0092] (3) Step S3: Adaptive energy-saving acquisition of dual-modal array data

[0093] This step aims to balance high-fidelity data from long-term online monitoring with overall system power consumption control. The heterogeneous sensor array's operating mode is adaptively switched based on the vehicle's operating status: high-frequency monitoring mode, sleep / wake-up mode, and local alert mode.

[0094] High-frequency monitoring mode: When the vehicle start signal is received or the battery management system is in a high-rate charging / discharging or fast charging state, the system wakes up the heterogeneous sensor array to fully open and collects dynamic pressure and temperature data in real time at a preset high-frequency sampling rate (the sampling frequencies of temperature and pressure may be the same or different) to capture transient bulging characteristics.

[0095] Sleep / Wake-up Mode: When the vehicle is turned off and the ambient temperature is within a safe operating range, the system enters a microampere-level low-power sleep mode. An internal timer periodically wakes up the heterogeneous sensor array for a single inspection, significantly extending device lifespan. The timers for the temperature and pressure sensor arrays can be the same or different.

[0096] Local Alert Mode: During sleep inspection, if a node in the array detects that the rate of pressure change or absolute temperature exceeds the set low-level alert threshold, the system immediately triggers an alert interruption, activating only the sensors of the abnormal node and its adjacent topology area to resume high-frequency continuous sampling until the parameters fall back to the safe baseline.

[0097] Step S3 introduces an adaptive energy-saving control strategy based on a multi-state machine. According to the different operating conditions of the battery module (such as high-rate charging and discharging, fast charging, shutdown and hibernation), the working mode of the heterogeneous sensor array (high-frequency monitoring, hibernation wake-up, local alarm) is dynamically adjusted. Under the premise of ensuring that transient expansion characteristics are effectively captured under critical conditions, the system power consumption of long-term online monitoring is greatly reduced, the service life of sensors and monitoring equipment is effectively extended, and the prominent problem of high energy consumption of multi-point array monitoring is solved.

[0098] (4) Step S4: Perform spatiotemporal mapping and alignment on the dual-modal array data.

[0099] Specifically, this step involves performing time interpolation mapping on array data with a lower time acquisition frequency and spatial interpolation mapping on array data with a lower distribution quantity, so that the pressure array data and temperature array data are aligned in both time and spatial dimensions.

[0100] First, a fourth-order Butterworth low-pass digital filter with dynamically adjustable cutoff frequency is used for frequency domain denoising to remove high-frequency environmental noise and obtain a smooth denoised signal sequence, thereby preserving the real battery expansion fluctuations and transient gas generation characteristics in the frequency range of 0.1Hz-0.5Hz.

[0101] Then, spatiotemporal mapping alignment is performed: assuming that the sampling frequencies of pressure and temperature data are consistent, at time t, the system simultaneously acquires high-dimensional pressure array data. (R represents the real number field,) Indicates the dimension of the pressure sensing array. , (This represents the number of flexible pressure sensing units arranged horizontally and vertically, respectively) and low-dimensional temperature array data. ( This indicates the dimension of the temperature sensing array. , These represent the number of temperature sensing units arranged horizontally and vertically, respectively. Due to the use of shared node deployment, ... Therefore, bilinear interpolation and Kriging geostatistical spatial interpolation algorithms are used to smoothly map and amplify the low-resolution temperature data, generating a high-resolution temperature array with the same dimensions as the pressure data. This allows for precise alignment of temperature and pressure data on physical coordinates.

[0102] Step S4 performs temporal and spatial interpolation mapping on the pressure and temperature data with inconsistent temporal and spatial resolutions in the heterogeneous sensor array (e.g., using bilinear interpolation and the Kriging algorithm to reconstruct the low-resolution temperature field into a high-resolution temperature array). This achieves strict alignment of the dual-modal data in the spatiotemporal dimensions, effectively eliminating spatiotemporal misalignment between sensors. It provides a precise and synchronized data foundation for subsequent temperature-pressure decoupling and multiple compensation, significantly improving the accuracy of temperature-pressure coupling correction and the reliability of expansion feature extraction.

[0103] (5) S5: Temperature and pressure decoupling and multiple compensation

[0104] Because flexible porous piezoresistive materials exhibit semiconductor temperature characteristics, their output resistance drifts nonlinearly with temperature. Based on the feature surface extracted from offline calibration experiments, a binary polynomial temperature-pressure coupling correction model is established to calculate the true absolute pressure of the current sensing node.

[0105] ,

[0106] in, to These are the model fitting coefficients. Indicates the location index of the sensor node. .

[0107] Subsequently, to completely eliminate static constraint interference, the system needs to perform zero-point calibration and preload pressure compensation: First, when the battery module has just finished assembly and is in a static state at standard room temperature (e.g., 25°C), data is continuously collected over a reference period, averaged, and solidified into an initial preload pressure reference array specific to this module. Secondly, by identifying the steady-state range where the vehicle is in a long-term idle state and the rate of temperature change approaches zero, an adaptive sliding window averaging method is used to dynamically extract and update low-frequency zero-drift terms caused by sensor material aging or creep of module structural components. Finally, by subtracting the aforementioned baseline preload and dynamic zero drift from the true absolute pressure, the actual relative expansion pressure change caused solely by the cell's own electrochemical reaction or thermal runaway can be determined. The calculation formula is as follows:

[0108] .

[0109] Step S5 establishes a binary polynomial temperature-pressure coupling correction model, and combines offline calibration and dynamic zero-point calibration to effectively eliminate baseline interference introduced by temperature drift, static preload, and material aging. It accurately decouples the relative pressure change caused only by the battery's own expansion, significantly improving the accuracy and stability of pressure measurement in high preload background and wide temperature range environment, and providing a clean and reliable mechanical signal for subsequent expansion feature extraction and anomaly warning.

[0110] (6) Step S6: Multidimensional spatiotemporal feature extraction

[0111] To avoid false alarms from a single point of failure, this step is based on the compensated spatiotemporal array. Extracting multi-dimensional evolutionary features:

[0112] Local pressure change rate It is used to detect transient sudden bulges caused by internal gas generation;

[0113] Cumulative Increment of Local Pressure in Space It is used to capture the cumulative deformation characteristics during long-term cycling. For cumulative time periods, For any point in time within the cumulative time period;

[0114] Historical Cyclic Comparison Bias Used to compare current characteristics with standard historical data under equivalent state of health (SOH). By comparing the values, the deviation values ​​are obtained.

[0115] Step S6 extracts multi-dimensional spatiotemporal features such as spatial local pressure change rate, cumulative increment, and historical cycle comparison deviation from the compensated expansion pressure change array. This comprehensively characterizes the subtle differences in the spatial distribution and temporal evolution of battery expansion, effectively avoids single-point false alarms, significantly improves the distinguishability and reliability of anomaly identification, and provides high-information feature support for subsequent risk assessment.

[0116] (7) Step S7: Battery abnormal expansion warning

[0117] First, differentiated safety sensitivity weights are assigned based on the chemical system of the power battery used (such as ternary lithium or lithium iron phosphate). ,in This is used to calculate the risk assessment index. Batteries with different chemical systems exhibit significant differences in thermal runaway mechanisms and expansion evolution characteristics. Therefore, by adaptively matching the weight coefficients of these three characteristics, the accuracy and timeliness of the early warning model can be maximized.

[0118] Specifically, when applied to ternary lithium battery systems, the weights are preferably set as follows: Ternary lithium batteries have high energy density, and their thermal runaway evolution process is usually accompanied by violent internal side reactions and rapid gas production, resulting in battery swelling that is often highly sudden and abrupt. Therefore, this system assigns the highest weight to the rate of change of local spatial pressure, enabling the early warning model to maintain extremely high sensitivity to small, sudden deformations in high-frequency transients, thereby achieving the fastest possible early intervention; at the same time, it relatively reduces the weight of historical cycle comparison bias, effectively avoiding the smoothing effect of long-cycle historical data from masking the sudden risks of transient events.

[0119] When applied to lithium iron phosphate battery systems, it is preferable to set the weights as follows: Because lithium iron phosphate batteries have a relatively stable crystal structure and a high thermal runaway trigger threshold, the probability of early failures causing severe sudden gas generation is relatively low. Their abnormal expansion characteristics are more often manifested as continuous side reactions during long-term operation, or as gradual cumulative deformation of the electrode material during charge-discharge cycles. Based on this physical mechanism, this system appropriately reduces the weight of the assessment of transient pressure change rates to effectively reduce false alarm rates under complex operating conditions, and balances the weighting coefficients of the cumulative increase in localized pressure and the historical cycle comparison deviation, making the risk assessment model more focused on accurately capturing long-term cumulative anomalies and structural variations caused by cell lifespan degradation.

[0120] Further calculate the risk assessment index:

[0121] ,

[0122] in, , , Represents the rate of change of local pressure in the computational space. Cumulative increase in local pressure in space Historical cycle comparison deviation The maximum value.

[0123] Then, a dynamic preset threshold is determined. This invention does not use a fixed dead zone threshold, but rather sets multi-dimensional dynamic preset thresholds. This dynamically preset threshold It is highly dependent on the current state of charge (SOC) of the battery cell and the ambient temperature (T). For example, when the battery is fully charged (SOC>90%) or operating at high temperatures, it will experience normal structural expansion and extreme thermal expansion. In this case, the controller will adaptively raise the alarm threshold to prevent false alarms. However, in the relaxed state of low charge (SOC<20%), if a sudden expansion occurs, it is highly likely that an internal fault is causing gas production. In this case, the controller will adaptively lower the alarm threshold to improve alertness.

[0124] Finally, the risk assessment index With dynamic preset threshold Compared to the risk assessment index Greater than the dynamic preset threshold If this is detected, it is determined that there is a risk of abnormal expansion, and the coordinates of the pressure sensing unit will be immediately adjusted to be precise. The alarm signal is output to the battery management system (BMS), thereby triggering various levels of safety protection actions such as power reduction, liquid cooling activation, or high-voltage fuse trip.

[0125] The BMS executes a tiered safety protection strategy based on the received alarm signals, including but not limited to: dynamically reducing the battery charge / discharge rate from 1C to below 0.2C to suppress the lithium plating reaction rate; controlling the activation of the liquid cooling thermal management system to increase the coolant flow rate to 2-5L / min to forcibly control the cell surface temperature rise rate to within 1℃ / min; and controlling the high-voltage relay to disconnect within 10ms to perform physical fuse protection when the cell expansion exceeds 150μm or the stacking pressure change exceeds 8% of the rated value. The above control steps can be executed sequentially, in parallel, or alternately according to the actual control strategy to effectively suppress abnormal states of the power battery.

[0126] Step S7 constructs a risk assessment index by integrating multi-dimensional spatiotemporal features and introduces a dynamic preset threshold bound to the state of charge and ambient temperature. This enables precise location and adaptive graded early warning of abnormal battery expansion, effectively avoiding false alarms and missed alarms under complex operating conditions with fixed thresholds. It provides a reliable decision-making basis for the battery management system, thereby triggering safety protection actions such as power reduction, liquid cooling enhancement, or high-voltage fuse in a timely manner, significantly improving the early prevention capability of power battery thermal runaway.

[0127] After step S7 is completed, the complete early warning process from physical perception to logical judgment is achieved. To more clearly demonstrate the collaborative working relationship between the various stages of the above method and its interaction with external execution units, this embodiment of the invention also provides a battery abnormal expansion early warning system based on flexible pressure monitoring. For example... Figure 4As shown, the system consists of a heterogeneous sensor array attached to the surface of the battery module at the physical layer, and sequentially includes a signal acquisition module, a signal preprocessing module, a temperature and pressure decoupling and multi-compensation module, a multi-dimensional spatiotemporal feature extraction module, and a battery abnormal expansion warning module at the data processing and control layer. Specifically, the heterogeneous sensor array is responsible for performing the raw signal sensing described in steps S1 and S2; the signal acquisition module acquires dual-modal data according to the adaptive energy-saving control strategy described in step S3; the signal preprocessing module performs the spatiotemporal mapping alignment and filtering described in step S4; and the temperature and pressure decoupling and multi-compensation module, the multi-dimensional spatiotemporal feature extraction module, and the battery abnormal expansion warning module respectively execute the core algorithms described in steps S5, S6, and S7. Finally, the risk assessment index and abnormal location information generated by the battery abnormal expansion warning module will be output as a warning signal to the vehicle's battery management system (BMS), which will then perform safety protection actions such as power reduction, liquid cooling activation, or high-voltage fuse tripping according to a preset strategy. The system architecture diagram fully presents the closed-loop processing flow of data from acquisition, preprocessing, decoupling, feature extraction, risk assessment to the final output to the battery management system for protection. It is a preferred hardware and logic implementation carrier for the method of this invention.

[0128] The effectiveness of the battery abnormal expansion early warning method based on flexible pressure monitoring provided in this embodiment of the invention is experimentally verified. This experiment was conducted using a high and low temperature alternating damp heat test chamber, a universal testing machine, and a high-precision battery module charge and discharge test bench system. Specific experimental design and verification data are as follows:

[0129] Experiment 1: Sensing and Sensitivity Verification of Small Deformations under High Preload Pressure

[0130] Test conditions: The three-dimensional porous flexible pressure sensing unit (experimental group) prepared in this invention and the traditional non-porous dense flexible piezoresistive sensor (control group) were placed in a universal testing machine. An initial static preload of 1.2 MPa was applied, and then a high-precision piezoelectric actuator was used to superimpose small dynamic displacements with amplitudes of 30 μm and 45 μm in the vertical direction to simulate the micro-expansion of the battery cell.

[0131] Validation data: Test results show that under a high baseline pressure of 1.2 MPa, the control group sensor, due to severe compression of the elastomer and complete pore collapse, exhibits almost no electrical signal response to a minute deformation of 30 μm, with a pressure sensitivity of only 0.3 kPa. - ¹; The experimental sensor of this invention, benefiting from the low Poisson's ratio stress concentration effect of the three-dimensional interconnected porous structure, successfully output a clear step resistance signal with a signal-to-noise ratio greater than 20dB. Its measured pressure sensitivity in the 0-50kPa dynamic expansion range reached 1.25kPa. - ¹.

[0132] Experiment 2: Verification of Temperature-Pressure Decoupling and Drift Error Compensation under Wide Temperature Range Environment

[0133] Test conditions: A heterogeneous sensor array was attached to the surface of an aluminum simulated battery cell, a constant pressure of 1.0 MPa was applied, and the cell was placed in a high and low temperature alternating test chamber. The temperature was set to -20℃ and increased to 80℃ at a rate of 5℃ / min for multiple cycles.

[0134] Verification data: Before compensation by the algorithm of this invention, the flexible pressure sensing unit is affected by the temperature characteristics of the semiconductor material, and its output pressure mapping value shows significant nonlinear drift with temperature alternation, with the maximum physical signal drift error reaching ±18.5%. After connecting to the system of this invention and running the temperature and pressure decoupling multi-compensation module, spatiotemporal alignment is performed based on the bilinear mapping model and the Kriging space interpolation algorithm, and a binary polynomial coupling correction model is substituted. The true absolute pressure curve output by the system remains highly stable throughout the temperature range of -20℃ to 80℃, and the maximum physical signal drift error can be controlled within ±1.8%. After introducing dynamic zero drift update, the error of the finally extracted relative expansion pressure feature can be controlled within ±2.6%.

[0135] Experiment 3: Verification of Array Topology Deployment and Overall System Power Consumption Control

[0136] Test conditions: A standard 16-cell square aluminum-cased battery module was used as the test object. The control group used a traditional dense full-coverage array with "one pressure and one temperature" and a fixed high frequency of 10Hz sampling; the experimental group used the shared node sparse topology layout strategy of the present invention and ran an adaptive energy-saving control strategy based on a multi-state machine.

[0137] Verification data: After adopting the topology strategy of this invention, the experimental group only needs 64 pressure nodes and 16 temperature nodes to recover high-resolution data across the entire domain through spatial interpolation. The total number of physical sensing units is reduced to 80, a reduction of 37.5%. During 72 hours of continuous simulated operation, including 4 hours of fast charging, 8 hours of vehicle discharge, and 60 hours of engine-off sleep mode, the measured standby power consumption of the system in sleep mode was 8.2mW. Calculating the total energy consumption over 72 hours, the control group consumed 158.4Wh, while the experimental group, by automatically switching between high-frequency, sleep, and local alert states, reduced its total energy consumption to 85.5Wh, a sharp decrease of 46% in overall operating power consumption.

[0138] This invention also provides a computer-readable storage medium storing a computer-executable program. When the program runs on an in-vehicle controller or embedded processor, it implements steps S3 to S7. The processor uses an ARM architecture microcontroller or an automotive-grade processing chip, with a hardware clock frequency range of 100-400MHz.

[0139] The system described in this invention can be directly deployed in a distributed computing architecture that includes a front-end data acquisition unit, an intermediate data processing unit, and a back-end monitoring platform. The computing units interact at high speed via a controller area network (CAN) or an in-vehicle Ethernet network, with the CAN transmission rate set to 500kbps and the Ethernet transmission rate set to 100Mbps, thereby achieving millisecond-level data synchronization and control command response.

[0140] The computer program described in this invention is written in C or Python and burned into a non-volatile memory, occupying a storage capacity of 256kB-4MB. The computer-readable storage medium is a physical hardware storage medium, specifically encompassing flash memory chips, electrically erasable programmable read-only memory, or solid-state memory.

[0141] In summary, the battery abnormal expansion early warning method and system based on flexible pressure monitoring provided by the embodiments of the present invention, with the structural design of the flexible pressure sensing unit and the collaborative optimization of temperature and pressure decoupling and multiple compensation as the core, constructs a closed-loop monitoring system covering the entire process of "sensing-decoupling-discrimination-control", and specifically achieves the following significant technical effects:

[0142] 1. Actual measurements show that the sensitivity reaches 1.25 kPa⁻¹ within the 0-50 kPa dynamic expansion range, the drift error can be controlled within ±1.8% over a wide temperature range (-20℃ to 80℃), and the overall system power consumption is reduced by 46%.

[0143] 2. Based on the bilinear mapping model and the Kriging space interpolation algorithm, the physical signal drift error caused by the wide temperature range change from -20℃ to 80℃ is strictly reduced to within ±2%, and the extraction error of the final pressure feature is less than ±3%.

[0144] 3. By employing an array sparse topology deployment strategy, the number of physical sensing units is reduced by more than 30%. At the same time, the multi-state adaptive low-power control strategy keeps the system standby power consumption below 10mW. Under the premise of achieving full-domain distributed coverage of the battery module and accurate positioning of three-dimensional abnormal areas, the overall system power consumption is reduced by more than 40%.

[0145] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for early warning of abnormal battery expansion based on flexible pressure monitoring, characterized in that, include: A flexible porous piezoresistive sensitive layer (3) with three-dimensional interconnected pores is prepared, and a flexible pressure sensing unit is prepared based on the flexible porous piezoresistive sensitive layer (3). Multiple flexible pressure sensing units and temperature sensing units are regularly attached to the surface of the battery module to obtain a heterogeneous sensing array. Under different operating conditions and external ambient temperatures, an adaptive energy-saving control strategy based on a multi-state machine is adopted. The heterogeneous sensor array collects pressure array data and temperature array data under pre-tightening pressure background in real time to obtain dual-mode array data. The dual-modal array data is time-aligned and spatial-aligned to obtain dual-modal aligned array data; The dual-mode aligned array data is corrected by temperature and pressure coupling to obtain the real pressure array data, and the corresponding relative expansion pressure change array data is calculated. Multidimensional feature parameters are extracted based on the array data of the relative expansion pressure change. Based on the multidimensional feature parameters, a risk assessment index is calculated. When the risk assessment index exceeds a dynamically preset threshold, the coordinates of the cell where the abnormal expansion occurs are located, and a warning signal is output to the battery management system.

2. The battery abnormal expansion early warning method based on flexible pressure monitoring according to claim 1, characterized in that, The preparation process of the flexible porous piezoresistive sensitive layer (3) is as follows: Two-dimensional conductive nanomaterials are uniformly mixed with an elastic polymer matrix to obtain a hybrid matrix; Sodium chloride particles and polymethyl methacrylate microspheres are added to the mixed matrix as sacrificial template particles, and a uniform composite slurry is formed by stirring and vacuum degassing. The composite slurry is poured into a mold and thermo-cured and cross-linked under a constant temperature environment to obtain a solid elastomer. The sacrificial module particles in the solid elastomer are removed by water-soluble and solvent etching to form a three-dimensional porous piezoresistive network with a porosity of 40%-80% and a pore size range of 10-200μm. The three-dimensional porous piezoresistive network is cut to obtain a flexible porous piezoresistive sensitive layer (3).

3. The battery abnormal expansion early warning method based on flexible pressure monitoring according to claim 2, characterized in that: The flexible pressure sensing unit is formed by sequentially bonding and encapsulating a lower flexible insulating substrate layer (1), an interdigitated electrode layer (2), a flexible porous piezoresistive sensitive layer (3), a buffer transition layer (4), and an upper flexible protective layer (5). The lower flexible insulating substrate layer (1) and the upper flexible protective layer (5) are both made of thermoplastic polyurethane material. The buffer transition layer (4) uses polydimethylsiloxane silicone rubber as the base material, and by changing the addition ratio of crosslinking agent in the thickness direction, or by incorporating silica nanoparticles with a gradient mass fraction, a gradient distribution structure with low bottom modulus and high top modulus is formed.

4. The battery abnormal expansion early warning method based on flexible pressure monitoring according to claim 3, characterized in that, The multiple flexible pressure sensing units and temperature sensing units are regularly attached to the surface of the battery module, specifically as follows: For the flexible pressure sensing unit, the main monitoring point is set at the geometric center of the large surface of the cell, and the auxiliary monitoring point is set at 1 / 4 of the diagonal direction, forming a cross or quincunx topology. A temperature sensing unit is placed at the diagonal geometric intersection of every four adjacent pressure sensing units.

5. The battery abnormal expansion early warning method based on flexible pressure monitoring according to claim 3, characterized in that, The adaptive energy-saving control strategy based on multi-state machines is as follows: The working mode of the heterogeneous sensor array is adaptively switched according to the vehicle's operating status, which is either high-frequency monitoring mode, sleep-wake mode, or local alert mode. The high-frequency monitoring mode is as follows: when a vehicle start signal is received or the battery management system is in a high-rate charging / discharging or fast-charging state, the heterogeneous sensor array is fully opened to collect dynamic pressure and temperature data in real time at a preset high-frequency sampling rate. The sleep-wake mode is as follows: when the vehicle is turned off and the ambient temperature is within a safe operating range, the system enters a microampere-level low-power sleep mode, and the internal timer periodically wakes up the heterogeneous sensor array to perform a single inspection. The local alert mode is as follows: during the dormant inspection period, if a node in the heterogeneous sensor array detects that the pressure change rate or absolute temperature exceeds the set low-level alert threshold, only the sensors of the abnormal node and its adjacent topology area are activated to resume high-frequency continuous sampling.

6. The battery abnormal expansion early warning method based on flexible pressure monitoring according to claim 3, characterized in that, The dual-modal aligned array data is subjected to temperature and pressure coupling correction to obtain the true pressure array data, specifically as follows: The dual-modal aligned array data is corrected for temperature and pressure coupling using a binary polynomial temperature-pressure coupling correction model to obtain the true absolute pressure of the current sensing node: ， in, to These are the model fitting coefficients. Indicates the location index of the sensor node. Indicates time, , These represent the pressure array data and temperature array data in the dual-modal aligned array data, respectively.

7. The battery abnormal expansion early warning method based on flexible pressure monitoring according to claim 6, characterized in that, The current moment in the relative expansion pressure change array data node The relative change in expansion pressure is: ， in, For nodes The average data during the baseline period after assembly at standard room temperature. This refers to low-frequency zero drift caused by aging of sensor materials or creep of module structural components.

8. The battery abnormal expansion early warning method based on flexible pressure monitoring according to claim 7, characterized in that, The multidimensional feature parameters include: Local pressure change rate ; Cumulative Increment of Local Pressure in Space , For cumulative time periods, For any point in time within the cumulative time period; Historical Cyclic Comparison Bias , This is standard historical data under the same health conditions.

9. The battery abnormal expansion early warning method based on flexible pressure monitoring according to claim 8, characterized in that: The risk assessment index is equal to the weighted sum of the rate of change of local space pressure, the cumulative increment of local space pressure, and the historical cycle comparison deviation. The dynamically preset threshold is related to the current state of charge of the battery cell and the ambient temperature.

10. A battery abnormal expansion early warning system based on flexible pressure monitoring, employing the battery abnormal expansion early warning method based on flexible pressure monitoring as described in any one of claims 1 to 9, characterized in that: The system includes the heterogeneous sensor array, a signal acquisition module, a signal preprocessing module, a temperature and pressure decoupling multiple compensation module, a multi-dimensional spatiotemporal feature extraction module, and a battery abnormal expansion early warning module. The signal acquisition module, the signal preprocessing module, the temperature and pressure decoupling multiple compensation module, the multi-dimensional spatiotemporal feature extraction module, and the battery abnormal expansion early warning module are respectively used to execute the above steps S3, S4, S5, S6, and S7.

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