Battery deformation real-time monitoring and early warning method based on multi-dimensional honeycomb structure

Abstract

The invention discloses a battery deformation real-time monitoring and early warning method based on a multi-dimensional honeycomb structure, which relates to the technical field of battery safety monitoring and comprises a monitoring target, a sensing network, a data acquisition terminal, a data processing terminal, a feature extraction terminal and a data analysis terminal. The monitoring target is a single battery or a battery module; the sensing network is composed of a sensing unit and a temperature sensor which are interconnected in a cellular topology, and is arranged on the surface of a battery or in a battery module spacing layer; the wavelength offset and temperature data of the sensor are acquired in real time through the data acquisition terminal; the processing terminal obtains a real mechanical strain value through temperature compensation and solution, and reconstructs a global deformation field; the feature extraction terminal extracts deformation feature parameters; and the analysis terminal compares the deformation characteristic parameters with a machine learning early warning model in combination with a threshold value for judgment, and sends out graded early warning signals when conditions are met. According to the method, high-precision and global real-time monitoring and early-stage safety early warning of battery deformation are realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery safety monitoring, and specifically discloses a battery deformation real-time monitoring and early warning method based on a multi-dimensional honeycomb structure. BACKGROUND

[0002] Lithium ion batteries are widely used in electric vehicles and energy storage power stations due to their high energy density and long cycle life. However, under abnormal conditions such as overcharging, overdischarging and internal short circuit, the battery may produce gas and increase internal pressure, leading to bulging and deformation of the battery. Deformation is one of the most direct and critical physical precursory signals before battery thermal runaway. Therefore, real-time, accurate and global monitoring of battery deformation is of great significance for early warning of battery thermal runaway and ensuring system safety.

[0003] Existing battery deformation monitoring technologies are mostly single-point or local monitoring, such as arranging a single or a few sensors on the surface of the battery. However, this method has the following disadvantages: first, it cannot capture the deformation distribution of the entire battery, which may miss local hotspots or abnormal deformation areas; second, it is difficult to distinguish between thermal expansion caused by temperature changes and mechanical deformation caused by internal faults, leading to false positives or false negatives; third, it lacks deep analysis of deformation patterns such as symmetry and gradient, limiting the early warning capability.

[0004] Therefore, it is necessary to invent a battery deformation real-time monitoring and early warning method based on a multi-dimensional honeycomb structure to solve the above problems. SUMMARY

[0005] In order to overcome the defects of the prior art, the application provides a battery deformation real-time monitoring and early warning method based on a multi-dimensional honeycomb structure, which acquires sensor wavelength offset and temperature data in real time through a data acquisition terminal; a processing terminal obtains real mechanical strain values through temperature compensation and calculation, and reconstructs the global deformation field; a feature extraction terminal extracts deformation feature parameters; an analysis terminal compares the deformation feature parameters with a machine learning early warning model based on threshold values for judgment, and issues a graded early warning signal when the conditions are met, effectively solving the problems mentioned in the background art.

[0006] To achieve the above purpose, the application provides the following technical scheme: a battery deformation real-time monitoring and early warning method based on a multi-dimensional honeycomb structure, specifically comprising the following steps: S1, arranging a sensor network on the surface of a battery monomer or inside a battery module; S2, a data acquisition terminal acquires measurement data in real time; S3, a data processing terminal processes and calculates the acquired measurement data to obtain real-time deformation field distribution information of the entire battery; S4, a feature extraction terminal extracts deformation feature parameters based on the real-time deformation field distribution information; S5, the data analysis terminal combines the deformation characteristic parameters and the early warning model to judge, generates and sends an early warning signal when the judgment result meets the early warning condition.

[0007] Preferably, the sensing unit is a fiber grating sensor; the honeycomb topology structure is realized by wavelength division multiplexing to connect multiple fiber grating sensors on a single optical fiber or by space division multiplexing technology to realize multiple fiber grating sensors on multiple optical fibers.

[0008] Preferably, the specific way of arranging the sensing network in the monomer battery is to print or embed the sensing network on a flexible insulating substrate to form an integrated sensing film, and then attach the sensing film to the surface of the battery monomer.

[0009] Preferably, the specific way of arranging the sensing network in the battery module is to weave or embed the sensing network in the spacing layer between the battery monomers, so that the sensing network is in contact with the surface of the multiple battery monomers.

[0010] Preferably, the measurement data includes the offset of the center wavelength of the fiber grating sensor and the temperature sensor data.

[0011] Preferably, the data processing and calculation includes: According to the mapping relationship between the wavelength offset and the strain and the temperature, the initial strain value coupled by the strain and the temperature is calculated; Using the temperature sensor data, the initial strain value is temperature-compensated and decoupled to obtain the real mechanical strain value; Each calculated real mechanical strain value is reconstructed into a continuous deformation field covering the whole domain of the monitoring target by an interpolation algorithm.

[0012] Preferably, the specific analysis method of the feature extraction terminal is to extract deformation characteristic parameters from real-time deformation field distribution information; the deformation characteristic parameters include maximum deformation, average deformation, deformation gradient, deformation distribution symmetry and deformation rate.

[0013] Preferably, the specific analysis method of the data analysis terminal is: The maximum deformation is compared with the preset absolute safety threshold value, and if it exceeds, an alarm is immediately given; Based on the pre-constructed early warning model, the time series data of the deformation characteristic parameters are dynamically analyzed to identify abnormal deformation, and an early warning is given when the deformation development trend deviates from the normal baseline; Combined with the deformation state classification, the deformation characteristic parameters and the position information corresponding to the deformation characteristic parameters, a diagnostic early warning signal containing the fault position and type is generated.

[0014] The technical effects and advantages of the present application are: 1. Realize the whole domain real-time high-precision deformation monitoring of battery: through adopting the sensing network based on multi-dimensional cellular topological structure, the dense distribution with high spatial resolution can be realized, the real-time, continuous and dynamic monitoring of the whole domain deformation field of the surface of battery monomer or the internal of battery module is realized, and the limitation that the traditional point measurement cannot comprehensively reflect the deformation state of the battery is overcome; 2. Excellent anti-interference and temperature compensation ability: the sensing network integrates the dedicated temperature sensor, and adopts the fiber grating sensing technology, the coupling relationship between strain and temperature is solved through the data processing terminal, the interference of temperature change on the deformation measurement result is effectively eliminated, and the accuracy and reliability of the mechanical strain measurement are significantly improved; 3. Flexible deployment and strong applicability: the sensing network can be made into flexible film and attached to the surface of monomer, or woven and embedded in the spacing layer of battery module, and has electrical insulation; the integrated design makes it can adapt to various complex battery structures and small space, realizes integrated deployment without affecting the performance of battery pack, and can monitor the deformation and interaction force of multiple monomers in the battery module at the same time; 4. Provide multi-level intelligent early warning and fault diagnosis: the system not only sets the absolute safety threshold to alarm immediately, more importantly, through feature extraction and early warning model based on machine learning algorithm, the dynamic time sequence analysis of deformation characteristic parameters is carried out, the abnormal deformation development trend can be identified early, the safety early warning is moved forward; at the same time, combined with the accurate positioning ability of the cellular unit, the position and type of abnormal deformation can be accurately judged, the diagnostic early warning signal with guiding significance is generated, and accurate and efficient decision support is provided for the safety management and maintenance of battery system. BRIEF DESCRIPTION OF DRAWINGS

[0015] The present application is further illustrated by the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application. For ordinary skilled in the art, other drawings can be obtained without creative labor on the basis of the following drawings.

[0016] Figure 1 It is the overall structure schematic diagram of the present application.

[0017] Figure 2 It is the step flow schematic diagram of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only 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 ordinary skilled in the art without creative labor belong to the protection scope of the present application.

[0019] The application provides a battery shape deformation real-time monitoring and early warning method based on a multi-dimensional honeycomb structure, as shown in the accompanying drawings. Figure 1 The method comprises the following steps: Figure 2 S1, arranging a sensing network on the surface of a battery monomer or in a battery module; Further, in the technical solution, the sensing network is composed of a plurality of sensing units and temperature sensors interconnected in a honeycomb topological structure. The sensing unit is a fiber Bragg grating (FBG) sensor, which is used as a core sensing unit to measure strain. The temperature sensor can be an integrated special FBG temperature sensor, a thermocouple or a thermal resistor, which is used to synchronously monitor temperature and realize temperature compensation. The honeycomb topological structure is realized by wavelength division multiplexing to connect a plurality of FBG sensors on a single optical fiber or by space division multiplexing technology to realize a plurality of FBG sensors on a plurality of optical fibers. It should be further noted that the honeycomb topological structure adopts a hexagonal honeycomb grid layout, each FBG sensor is located at the top of the honeycomb, each FBG sensor has a different central wavelength, and the FBG sensors are connected by optical fibers to form a network structure. Temperature sensors are arranged at key nodes or central positions of the honeycomb grid to ensure the representativeness of temperature field monitoring.

[0020] Further, in the technical solution, the sensing network is printed or embedded on a flexible insulating substrate to form an integrated sensing film, and the sensing film is attached to the surface of the battery monomer. In the preferred technical solution of the application, the specific implementation of the sensing network arranged on the battery monomer can be that a flexible insulating material, specifically a polyimide or PET film, is selected as the carrier of the sensing network; a photolithography, screen printing or laser etching technology is used to pattern the sensor on the flexible substrate, or the sensing network is embedded between two flexible insulating films, and the integrated sensing film is packaged by hot pressing or gluing, and the sensing film is attached to the surface of the battery monomer to ensure close contact with the battery surface to accurately sense deformation and temperature change.

[0021] Further, in the technical solution, the specific way of arranging the sensing network in the battery module is that the sensing network is woven or embedded in the spacing layer between the battery monomers in the battery module, so that the sensing network is in contact with the surfaces of the plurality of battery monomers to synchronously monitor the deformation of the plurality of battery monomers in the battery module and the interaction force therebetween. Further, in the technical solution, the specific way of arranging the sensing network in the battery module is that the sensing network is woven or embedded in the spacing layer between the battery monomers in the battery module, so that the sensing network is in contact with the surfaces of the plurality of battery monomers to synchronously monitor the deformation of the plurality of battery monomers in the battery module and the interaction force therebetween.

[0022] In the preferred technical solution of the present application, the specific implementation of the sensor network arranged in the battery module can be: a flexible insulating material, specifically a polyimide or a silica gel-based composite material, is selected as the substrate of the sensor network, and the shape and size of the substrate are completely consistent with those of the intermediate layer in the battery module; the sensor network is manufactured on the selected flexible substrate by using precise printing, photolithography or flexible circuit board embedding technology, and a thin flexible protective layer, such as another layer of polyimide or silica gel-based composite material, is covered thereon to form a new intermediate layer of the battery module, and the new intermediate layer of the battery module is used to replace the original intermediate layer of the battery module.

[0023] S2, the data acquisition terminal acquires measurement data in real time; Further, in the above technical solution, the measurement data includes the offset of the center wavelength of the fiber grating sensor and the temperature sensor data.

[0024] In the preferred technical solution of the present application, the specific implementation of the data acquisition terminal acquiring measurement data in real time is: a multi-channel fiber demodulator is used as the core equipment of the data acquisition terminal, which supports high-speed and high-precision wavelength demodulation, each channel corresponds to one or more fiber grating sensors connected in series or parallel through wavelength division multiplexing or space division multiplexing technology, and the data of the temperature sensor is synchronously acquired through an integrated temperature acquisition module, such as a thermocouple or an RTD acquisition card; S3, the data processing terminal processes and calculates the acquired measurement data to obtain real-time deformation field distribution information of the whole battery; Further, in the above technical solution, the data processing and calculation includes: According to the mapping relationship between the wavelength offset and the strain and the temperature, the initial strain value coupled with the strain and the temperature is calculated; It should be further pointed out that, according to the physical characteristics of the fiber grating sensor, a mapping relationship model between the wavelength offset Δλ and the strain ε and the temperature T is established , in which K ε is the strain sensitivity coefficient, K T is the temperature sensitivity coefficient, and ΔT is the temperature deviation of the temperature measured by the temperature sensor from the reference temperature; Temperature compensation calculation example: Strain sensitivity coefficient: K ε = 1.2 pm / με, temperature sensitivity coefficient: K T = 10.0 pm / ℃, at a certain moment, the data acquisition terminal obtains a wavelength offset Δλ = 120.0 pm; the temperature sensor measures a temperature deviation ΔT = 5.0℃ from the reference temperature; Calculation process: K ε ·ε + K T ·ΔT = 120.0; ε = (120 - K T · ΔT) / K ε = (120 - 10 x 5) / 1.2 = 70 / 1.2 ≈ 58.33 T The determination mode of the temperature sensitivity coefficient K Place the FBG sensor to be calibrated in a high-precision thermostat, change the temperature of the thermostat at a certain temperature step, such as 5℃, cover the entire temperature range of the battery operation, and accurately record the center wavelength value λ of the FBG sensor at each temperature stable point using the demodulator; Calculate the wavelength shift Δλ and the temperature change ΔT at each temperature point relative to the reference temperature, such as 25℃; Draw a scatter plot of Δλ / λ0 and ΔT, and linearly fit the data points. The slope of the obtained straight line is the temperature sensitivity coefficient K of the FBG sensor T Further, the determination mode of the strain sensitivity coefficient K ε is as follows: Tightly paste the FBG sensor to be calibrated on an equal strength beam or tensile specimen of a known material property, such as aluminum alloy. The specimen needs to be placed in a constant temperature environment, and the pasting process is consistent with the process used on the battery in the end; Use a precision tensile testing machine to apply a known size of axial strain ε to the specimen, and use the demodulator to record the change Δλ of the center wavelength of the FBG sensor; Draw a scatter plot of Δλ / λ0 and the applied strain ε, and linearly fit the data points. The slope of the obtained straight line is the strain sensitivity coefficient K of the FBG sensor ε .

[0025] Using temperature sensor data, the initial strain value is temperature compensated and decoupled to eliminate the influence of temperature change on the deformation measurement result, and the true mechanical strain value is obtained; Each calculated true mechanical strain value is reconstructed to a continuous deformation field covering the entire domain of the monitoring target through an interpolation algorithm.

[0026] It needs to be further explained that after the true mechanical strain value obtained by each sensing unit is calculated, the true mechanical strain value is reconstructed to a continuous deformation field covering the entire domain of the battery monomer or battery module through a difference algorithm, such as triangular subdivision interpolation, inverse distance weighting method, Kriging interpolation method or spline interpolation method, to form a visual strain distribution map.

[0027] It needs to be further explained that taking triangular subdivision difference as an example, the specific way of reconstructing the true mechanical strain value to a continuous deformation field is as follows: Triangular meshing: Delaunay triangulation is performed on the FBG sensors as data points to generate a triangular mesh covering the whole battery surface, and the coordinates of the vertices of each triangle and the real mechanical strain values after temperature compensation are obtained; Define the output mesh: define a regular mesh covering all the sensor points, which is dense enough, for example, one point per 1mm; Interpolate the points on the output mesh to obtain the strain field: for any point P, interpolation can be performed based on the coordinates of the three vertices of the triangle it belongs to and the real mechanical strain values after temperature compensation. The specific process of interpolation is as follows: Assume that point P is in triangle T, with coordinates A(x P , y P , ε P ), and the vertices of triangle T are A(x a , y a , ε a ), B(x b , y b , ε b ), and C(x c , y c , ε c ), where x and y represent the coordinates of the corresponding points in the triangular mesh, and ε is the real mechanical strain value of the corresponding point; Calculate the barycentric coordinates of point P in triangle T (λ a , λ b , λ c ), and the calculation formula is: , where Area(U, V, W) represents the area of the triangle formed by points U, V, and W; Calculate the strain value of point P ε P , and the calculation formula is: ; Convert the strain value to the deformation variable: for each point on the output mesh, its deformation variable δ can be calculated by the formula δ = ε⋅L0, where ε is the real mechanical strain value of the point, and L0 is the effective base length of the sensing unit; The value of L0 depends on the type and layout of the sensor, and mainly has the following two cases: For a single FBG sensor based on wavelength division multiplexing, L0 is the physical length of the grating itself; When multiple FBGs are connected in series on a fiber to form a sensing network, the L0 of each sensor can be understood as the distance between adjacent two sensor nodes, i.e. the side length of the honeycomb mesh.

[0028] It should be further noted that in the case of the sensor network is arranged in the battery module, the calculated true mechanical strain value should be the strain superposition value of the multiple surfaces in contact with the sensor network, and the calculation result reflects the relative deformation variable of a certain point.

[0029] Deformation variable calculation example: The vertices of triangle T: A: coordinates (x a , y a )=(0, 0), true mechanical strain value ε a =0.01; B: coordinates (x b , y b )=(4, 0), true mechanical strain value ε b =0.02; C: coordinates (x c , y c )=(0, 3), true mechanical strain value ε c =0.03; Point P: coordinates (x p , y p )=(1, 1), effective base length L0=100mm; Calculation steps: Calculate the area of triangle ABC: Area(A, B, C)=(1 / 2)×|(4−0)×(3−0)−(0−0)×(0−0)|=(1 / 2)×|12−0|=6; Area(P, B, C)=(1 / 2)×|(4−1)×(3−1)−(0−1)×(0−1)|=(1 / 2)×|6−(-1)|=2.5; Area(A, P, C)=(1 / 2)×|(1−0)×(3−0)−(0−0)×(0−1)|= (1 / 2)×|3−0|=1.5; Area(A, B, P)=(1 / 2)×|(4−0)×(1−0)−(1−0)×(0−0)|=(1 / 2)×|4−0|=2; Calculate the barycentric coordinates of point P: λ a =2.5 / 6=5 / 12≈0.4167; λ b =1.5 / 6=1 / 4≈0.25; λ c =2 / 6=1 / 3≈0.3333; Interpolation calculation of strain value ε p of point P: ε p= (5 / 12) x 0.01 + (1 / 4) x 0.02 + (1 / 3) x 0.03 = (0.05 / 12) + (0.06 / 12) + (0.12 / 12) = (0.23 / 12) = 0.019167; Convert the strain value to the deformation variable δ: δ = ε p L0 = 0.019167 x 100 = 1.9167 mm; Conclusion: The deformation variable of point P is 1.9167 mm.

[0030] S4, the feature extraction terminal extracts deformation feature parameters based on real-time deformation field distribution information; Further, in the above technical solution, the specific analysis method of the feature extraction terminal is to extract deformation feature parameters from real-time deformation field distribution information; the deformation feature parameters include maximum deformation variable, average deformation variable, deformation gradient, deformation distribution symmetry, and deformation rate.

[0031] It needs to be further explained that the process of extracting deformation feature parameters is as follows: Calculate the maximum deformation variable δ max : traverse the deformation variables δ of all points in the entire deformation field; find the maximum value, denoted as δ max , for subsequent absolute safety threshold comparison; Calculate the average deformation variable δ avg : take the arithmetic mean of the deformation variables δ in all deformation fields, which is used to reflect the overall deformation level, and the formula is , where N is the number of points of the output grid divided in the real-time deformation field; Calculate the deformation gradient ∇δ: use numerical differentiation methods such as central difference method to calculate the gradient of each point in the deformation field; the x-direction and y-direction gradient components can be calculated using the formulas and respectively, and then the total gradient is synthesized according to the formula , where δ x+Δx，y , δ x-Δx，y , δ x，y+Δy , and δ x，y-Δy represent the deformation variables of the corresponding coordinates, and Δx and Δy represent the distance between the two adjacent points of the output grid divided in the real-time deformation field in the corresponding direction; the gradient value reflects the degree of deformation change and can be used to identify local stress concentration areas; It needs to be further explained that for points on the boundary, forward or backward difference method is used to calculate the gradient components in the x-direction and y-direction; Deformation gradient calculation example: a 3x3 grid with grid spacing Δx = Δy = 1 mm, and the deformation variable data is as follows: δ0，0 = 10 mm, δ 0，1 = 12 mm, δ 0，2 = 15 mm, δ 1，0 = 11 mm, δ 1，1 = 20 mm, δ 1，2 = 18 mm, δ 2，0 = 13 mm, δ 2，1 = 17 mm, δ 2，2 = 16 mm; Calculate the gradient of the center point (1, 1): ∂δ / ∂x ≈ (δ 1+1，1 - δ 1-1，1 ) / (2 x 1) = (δ 2，1 - δ 0，1 ) / 2 = 2.5 μm / mm; ∂δ / ∂y ≈ (δ 1，1+1 - δ 1，1-1 ) / (2 x 1) = (δ 1，2 - δ 1，0 ) / 2 = 3.5 μm / mm; Strain gradient ∇δ = (2.5 2 + 3.5 2 ) 1 / 2 ≈ 4.3 μm / mm; Conclusion: The strain gradient ∇δ at the coordinate (1, 1) is 4.3 μm / mm.

[0032] Calculate the symmetry S of the strain distribution: mirror the strain field along the battery center axis or a pre-set symmetry axis; calculate the difference of the strain values in the symmetric region; use the correlation coefficient or the root mean square error as the symmetry index; for example, use the formula Calculate the symmetry S of the strain distribution; the closer S is to 1, the better the symmetry; the lower it is, the more asymmetric the strain distribution is, and there may be local abnormalities; in the formula, RMSE() is the root mean square error formula, δ left and δ right are the strain values of a pair of points in the output grid of the real-time strain field symmetric about the symmetry axis; Calculate the strain rate dδ / dt: combine historical strain field data and use the formula Calculate the rate of change of the current strain relative to the previous time step; the rate can be calculated for the entire domain or key areas such as the maximum strain point; it is used to determine whether the strain development trend is abnormal; in the formula, δ t is the strain value of a point in the output grid of the real-time strain field at time t, and Δt is a short time interval, such as 1 s.

[0033] S5, the data analysis terminal combines the deformation characteristic parameters and the early warning model to judge, when the judgment result meets the early warning condition, an early warning signal is generated and sent out.

[0034] Further, in the above technical solution, the specific analysis mode of the data analysis terminal is: The maximum deformation value is compared with the preset absolute safety threshold value, and if it exceeds, an alarm is immediately given; Based on the pre-constructed early warning model, the time series data of the deformation characteristic parameters are dynamically analyzed, abnormal deformation is identified, and early warning is given when the deformation development trend deviates from the normal baseline; It should be further pointed out that the early warning model uses historical deformation data, including deformation characteristic parameter time series in normal and abnormal states to train machine learning models such as LSTM, GRU, CNN or ensemble learning algorithm, the model input is the time series of deformation characteristic parameters, and the output is the classification of deformation state, such as normal, slight abnormality and serious abnormality.

[0035] Combined with the deformation state classification, the deformation characteristic parameters and the position information corresponding to the deformation characteristic parameters, a diagnostic early warning signal containing the fault position and type is generated.

[0036] Finally, it should be pointed out that: the above only describes the preferred embodiments of the present application and does not limit the present application, although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments or make equivalent replacement for part of the technical features, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A battery deformation real-time monitoring and early warning method based on a multi-dimensional cellular structure, characterized in that, The application relates to a battery safety monitoring system, which comprises a monitoring target, a sensing network, a data collection terminal, a data processing terminal, a feature extraction terminal and a data analysis terminal, wherein the monitoring target is a battery monomer or a battery module, the sensing network is composed of a plurality of sensing units and temperature sensors interconnected in a honeycomb topological structure, and the system comprises the following steps: S1, arranging the sensing network on the surface of the battery monomer or in the battery module; S2, collecting measurement data in real time by the data collection terminal; S3, processing and calculating the collected measurement data by the data processing terminal to obtain real-time deformation field distribution information of the whole battery; S4, extracting deformation characteristic parameters based on the real-time deformation field distribution information by the feature extraction terminal; S5, judging by the data analysis terminal in combination with the deformation characteristic parameters and a warning model, and generating and sending a warning signal when the judgment result meets the warning condition. The sensing unit is a fiber grating sensor; the honeycomb topological structure is realized by wavelength division multiplexing to connect a plurality of fiber grating sensors on a single optical fiber or by space division multiplexing to realize the plurality of fiber grating sensors on a plurality of optical fibers.

2. The method for battery shape real-time monitoring and early warning based on multi-dimensional cellular structure according to claim 1, characterized in that: The specific arrangement mode of the sensing network on the battery monomer is that the sensing network is printed or embedded on a flexible insulating substrate to form an integrated sensing film, and then the sensing film is attached to the surface of the battery monomer.

3. The method of claim 1, wherein the method comprises: The specific arrangement mode of the sensing network on the battery module is that the sensing network is woven or embedded in the spacing layer between the battery monomers in the battery module, so that the sensing network is in contact with the surfaces of the plurality of battery monomers.

4. The method of claim 1, wherein the method comprises: The measurement data comprises the wavelength shift of the fiber grating sensor and the temperature sensor data.

5. The method for battery shape real-time monitoring and early warning based on multi-dimensional cellular structure according to claim 1, characterized in that: The data processing and calculation comprises the following steps:

6. The multi-dimensional cellular structure based battery shape change real-time monitoring and early warning method of claim 1, wherein: calculating the initial strain value coupled with strain and temperature according to the mapping relationship between the wavelength shift and strain and temperature; carrying out temperature compensation decoupling on the initial strain value by using the temperature sensor data to obtain the real mechanical strain value; reconstructing each calculated real mechanical strain value into a continuous deformation field covering the whole domain of the monitoring target by an interpolation algorithm. The specific analysis mode of the feature extraction terminal is to extract deformation characteristic parameters from the real-time deformation field distribution information; the deformation characteristic parameters comprise a maximum deformation amount, an average deformation amount, a deformation gradient, a deformation distribution symmetry and a deformation rate.

7. The multi-dimensional cellular structure based battery shape change real-time monitoring and early warning method of claim 1, wherein: The specific analysis mode of the data analysis terminal is as follows:

8. The method for battery shape real-time monitoring and early warning based on multi-dimensional cellular structure according to claim 1, characterized in that: comparing the maximum deformation amount with a preset absolute safety threshold value, and immediately alarming if the maximum deformation amount exceeds the preset absolute safety threshold value; carrying out dynamic analysis on the time series data of the deformation characteristic parameters based on a pre-constructed warning model to identify abnormal deformation and send an early warning when the deformation development trend deviates from the normal baseline; generating a diagnostic warning signal containing a fault position and type in combination with the deformation state classification, the deformation characteristic parameters and the position information corresponding to the deformation characteristic parameters. ​

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