Lithium battery full life cycle health state monitoring method

Abstract

The invention discloses a lithium battery full life cycle health state monitoring method, belongs to the technical field of lithium ion battery detection, and aims to identify pore defects and dynamic bubble behaviors in a battery in real time. According to the method, broadband ultrasonic waves are emitted through an air coupling multi-probe array, and back scattering signals caused by bubbles and uneven structures in a battery in the charging and discharging process are collected. And in combination with a porous medium acoustic theory, extracting time domain and frequency domain characteristic parameters related to the bubble form and distribution, and inputting the time domain and frequency domain characteristic parameters into a preset bubble state-signal response model. On the basis, the evolution state of the internal gas production defect of the battery is judged, so that effective support is provided for fault early warning and safe operation of the lithium ion battery. The method has the remarkable advantages of simplicity and convenience in operation, high real-time performance, high accuracy and the like, and can be widely applied to online detection and quality control of a battery production line.

Description

Technical Field

[0001] This application relates to the field of lithium-ion battery testing technology, and in particular to a method for monitoring the health status of lithium batteries throughout their entire life cycle. Background Technology

[0002] Lithium-ion batteries, as high-performance rechargeable batteries, are widely used in new energy vehicles, portable electronic devices, and energy storage systems. However, the safety and reliability of lithium-ion batteries have always been key factors restricting their large-scale application. Dynamic defects, such as bubble formation and lithium dendrite growth caused by overcharging, can lead to internal short circuits, thermal runaway, and even combustion or explosion, posing a serious threat to battery safety.

[0003] Dynamic defect detection in lithium-ion batteries is crucial for ensuring their safety and reliability. Traditional detection methods, such as voltage and temperature monitoring, while providing some early warning, typically only issue alerts when defects have progressed to a certain extent, failing to provide early warning. Furthermore, traditional detection methods often rely on contact measurements, which not only increases the complexity of the measurement process but may also damage the battery itself. In recent years, ultrasonic testing technology has been widely used in the field of non-destructive testing of materials due to its advantages such as non-contact nature, high resolution, and high sensitivity. However, applying ultrasonic testing technology to dynamic defect detection in lithium-ion batteries still faces many challenges, such as the complex internal structure of the battery, the significant differences in the acoustic properties of multiphase media, and the complex propagation characteristics of ultrasonic signals within the battery. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies in the early, non-destructive, quantitative, and online detection of dynamic defects in lithium-ion batteries (especially overcharge-induced bubble clusters), this application provides an ultrasonic-based method for detecting dynamic defects in lithium-ion batteries, enabling real-time, non-destructive, and high-precision detection of internal dynamic defects in lithium-ion batteries.

[0005] On one hand, embodiments of this application provide a method for monitoring the health status of a lithium battery throughout its entire lifecycle, the health status monitoring method comprising: Emit ultrasonic waves to the lithium battery under test and receive response signals caused by the battery's internal structure and / or defects; The response signal is processed to identify defects inside the lithium battery; The defects include static defects and dynamic defects, and the state of the defects is determined based on the characteristic parameters corresponding to the defects.

[0006] Furthermore, the identification of the static defects includes: A non-contact air-coupled multi-probe array was used to collect ultrasonic data from different depths inside the battery. Based on the homogenization theory of solid mechanics, characteristic parameters sensitive to pore depth are extracted from the waveform data. The feature parameters sensitive to pore depth are input into a preset depth-parameter mapping relationship to determine the depth location of pore defects.

[0007] Furthermore, the identification of the dynamic defects includes: During the charging and discharging process of lithium batteries, an ultrasonic backscattering probe is used to emit ultrasonic excitation and receive backscattering signals caused by bubbles and structural inhomogeneities inside the battery. Based on the acoustic theory of porous media, time-domain and frequency-domain characteristic parameters related to bubble morphology and distribution are extracted from the backscattered signal. The time-domain and frequency-domain characteristic parameters are input into a preset bubble state-signal response model to determine the evolution state of gas generation defects inside the battery.

[0008] This application has the following advantages: Real-time monitoring: This method can detect bubble behavior in real time during the charging and discharging process of lithium-ion batteries and identify internal defects in a timely manner.

[0009] High-precision positioning: By combining ultrasonic backscattered signals with a porous medium model, the positioning accuracy of bubble state and defects is improved.

[0010] Non-contact detection: It adopts air coupling technology, which avoids the pollution and additional maintenance costs caused by traditional coupling agents and is suitable for the compact layout of the production line.

[0011] Multi-dimensional information integration: By combining ultrasonic signal characteristics with electrochemical performance, a comprehensive assessment of battery health status can be achieved, enhancing the reliability of diagnosis.

[0012] Wide adaptability: It can be applied to various lithium-ion battery production and use scenarios, providing assurance for battery safety and performance, and has significant economic and social value. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the ultrasonic testing layout based on porosity defects provided by the present invention; Figure 2 This is a homogenization model diagram of the lithium iron phosphate battery cell provided by the present invention; Figure 3 This is a homogenized finite element model diagram of a lithium iron phosphate battery; Figure 4 This is a defect structure diagram of a lithium iron phosphate battery model; Figure 5 This is a diagram showing the ultrasonic test results of a defect-free lithium iron phosphate pouch battery. Figure 6 This is a diagram showing the ultrasonic test results of a defective lithium iron phosphate pouch battery. Figure 7 This is a diagram showing the ultrasonic test results of a lithium iron phosphate button battery. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] Example 1: This embodiment proposes an ultrasound-based method for detecting the health status of lithium batteries, including the following steps: S1: Ultrasonic-based static defect detection of lithium-ion batteries: For example, in static defect detection targeting porosity defects in lithium-ion batteries, a homogenized finite element model was established based on pressure acoustics and solid mechanics theories to equivalently characterize the microstructure of the electrode material. An air-coupled multi-probe transmission array detection method was proposed. By comparing and analyzing the ultrasonic signal parameters of porosity defects at different depths, accurate localization of the porosity defect depth was achieved.

[0017] S2: Ultrasonic-based dynamic defect detection of lithium-ion batteries: For example, in dynamic defect detection, a method for detecting bubble anomalies based on ultrasonic backscattering signals is proposed to address safety accidents caused by overcharging of lithium-ion batteries. A two-dimensional monolayer lithium iron phosphate battery model is established, and the bubble clusters generated at the electrode / separator interface are represented as a porous medium model. Based on the finite element model, the acoustic characteristics of the bubble defects are studied. Experiments show that ultrasound has good sensitivity to internal bubble defects; the bubble density is positively correlated with the ultrasonic backscattering echo attenuation rate; and changes in bubble size cause a nonlinear response in the backscattering signal amplitude.

[0018] In one exemplary embodiment, ultrasonic-based static defect detection of lithium-ion batteries specifically includes: S101: Design of a detection method for porosity defects: like Figure 1 As shown, for the porosity defects caused by uneven slurry dispersion, improper coating and drying processes, or insufficient rolling in lithium-ion battery manufacturing, the presence and location scale of porosity defects are characterized by changes in ultrasonic parameters. When selecting an ultrasonic testing method that is compatible with the production line environment and lithium battery manufacturing process, the focus should be on the compatibility of the testing method with the actual operating conditions of the production line and the fit between the testing process and the characteristics of the lithium battery production process. Integrated ultrasonic testing conditions that meet the following industrial requirements: The detection speed is matched with the production line cycle time, enabling real-time signal processing; The equipment integration adapts to the compact layout of the production line, and prioritizes water immersion or air coupling technology to avoid contamination from traditional coupling agents or the extra space occupied by wiping devices. Balancing equipment cost-effectiveness with low maintenance requirements; It has multi-layer thin-layer penetration capability, making it compatible with the materials and structure of lithium batteries; A detection scheme combining multi-probe transmission and air coupling technology is adopted to balance efficiency and accuracy. The ultrasonic detection layout enables high-precision detection and positioning of the location distribution of pore defects inside lithium-ion batteries.

[0019] S102: Finite element homogenization model design and porosity defect model construction: S1021: The design of the finite element homogenization model includes: A multiphysics coupling model is constructed, which combines the physical properties of different media, including the pressure acoustics problem of electrolyte media, the stress-strain problem of solid media, and the stress-strain problem of fluid-structure interaction at the fluid-structure interaction interface. The analysis and discussion of wave equations and control theories in various media provide an accurate and reliable theoretical basis for finite element analysis, specifically including: (1) The propagation of sound waves in the electrolyte medium satisfies the pressure acoustic equation and the time-domain wave equation (Helmholtz equation).

[0020] in, For sound pressure, For fluid density, For the speed of sound, Angular frequency, For Hamiltonian operators, denoted as spatial gradient.

[0021] Time-domain wave equation (for transient analysis):

[0022] Sound pressure Regarding time The second partial derivative of represents the acceleration of sound pressure (similar to how the second derivative of displacement is acceleration), describing how quickly sound pressure changes over time.

[0023] (2) The propagation of sound waves in metals such as aluminum and copper and polymer (PP / nylon) solid media follows the linear elastic wave equation, and the stress tensor is defined by the constitutive equation;

[0024] in, It is a displacement vector. For solid density, This is a volume force (usually negligible). Here, is the acceleration of the particle. Let be the displacement vector of the particle. Taking its second derivative with time gives the acceleration.

[0025] Stress Tensor Defined by the constitutive equation:

[0026] in, For the elastic stiffness tensor, For strain tensor, Let be the displacement gradient tensor, which describes the rate of change of the displacement field in space; For transpose; (3) When ultrasound propagates at the fluid-solid interface, the displacement-pressure continuity condition must be met, including the continuity of normal stress and the continuity of normal displacement; Normal stress continuity:

[0027] in, For solid side stress, For fluid-side sound pressure, It is the normal vector.

[0028] Normal displacement continuity:

[0029] in, For solid displacement, This represents the displacement of fluid particles (calculated from the sound pressure gradient).

[0030] (4) Finite element discretization of wave equations, including weak forms of pressure acoustic equations (fluid domain) and elastic wave equations (solid domain); Including the weak form of the pressure acoustic equations (fluid domain):

[0031] in, For the test function, through the form function sound pressure Interpolation (e.g.) ), generating a discretized system of linear equations.

[0032] The weak form of the elastic wave equation (solid domain) for the displacement field is:

[0033] in, It is a virtual displacement field. Given the solid density, the stiffness matrix and mass matrix are generated through shape function interpolation.

[0034] (5) Multi-physics coupling processing of fluid-structure interaction: the displacement and stress continuity conditions of the fluid-structure interface are realized through multi-physics nodes, and the control equations of fluid and solid are merged into a unified set of equations.

[0035] in, and Both represent fluid-structure interaction matrices; Represents the stiffness matrix of the fluid domain; This represents the stiffness matrix of the solid domain.

[0036] like Figure 2 As shown, a homogenization model is designed. Considering the periodic distribution characteristics of the multilayer composite structure of lithium iron phosphate batteries, a simplified lithium-ion battery model is constructed. The periodically occurring medium is integrated, and the thickness of the medium is increased proportionally to form a homogenization model of lithium-ion batteries, thereby reducing the modeling difficulty. In the homogenization model, the number of interface layers in the lithium-ion medium is reduced, which amplifies the energy of the echo signal by a certain factor, while having little impact on the first wave of arrival and the time of flight, thereby realizing the simulation of ultrasonic testing of lithium-ion batteries based on the homogenization model. like Figure 3 As shown, a homogenized finite element model of a lithium-ion battery is designed, including an air domain, a lithium-ion battery domain, and a signal transmitter (receiver). The distance between the sound source and the battery is set as the focal length of the sound source to maximize the energy coupling efficiency between the sound source and the lithium-ion battery.

[0037] S1022: As Figure 4 As shown (from left to right, these are the first, third, and fifth electrolyte layers), the construction of the porosity defect model includes: Air layers are embedded in the first, third, and fifth layers of the electrolyte layer of a lithium-ion battery to simulate the porosity defects inside the battery. The first layer corresponds to the near-surface region of the lithium-ion battery, the third layer represents the middle layer, and the fifth layer is located in the region near the bottom of the lithium-ion battery. In finite element modeling, material parameters for the fluid domain and the solid domain are defined. For the fluid domain, the density, sound velocity, acoustic impedance, and loss factor of the medium need to be defined, and sound-absorbing boundary conditions or radiation conditions need to be set to simulate the sound field propagation characteristics. For the solid domain, parameters such as elastic modulus, Poisson's ratio, and damping characteristics need to be defined to characterize the mechanical response of the structure under dynamic or static loads. Different mesh sizes are selected according to different media. Usually, 1 / 6 of the wavelength of the sound wave in the current medium is used as the mesh size to ensure the accuracy of deformation and stress calculation at the geometric change point in the solid domain. The ratio between the mesh size and the wavelength of the sound wave is controlled in the pressure acoustic domain. Local refinement is carried out near the sound source area, sound absorption boundary and fluid-structure interaction interface. The meshes of the two types of solution domains maintain geometric continuity and data transfer consistency at the interface. Cylindrical wave radiation boundaries are added in the pressure acoustic domain to reduce the influence of reflected echoes on the finite element analysis results. Low-reflection boundaries are selected for the solid mechanics domain to achieve good boundary acoustic wave absorption effect. The detection source is a harmonic excitation signal based on Hanning window modulation, with a center frequency set at 400kHz and a spectral broadening characteristic of 0.5MHz. The detection system adopts an air-coupled multi-probe transmission array, with probe groups symmetrically arranged along the interface between the positive and negative electrodes of the battery. Dynamic displacement excitation is applied in the interface normal direction, and dynamic acoustic pressure load is applied simultaneously at the top of the pore defect interface to form a composite excitation field. The time step was set to 18 nanoseconds to accurately represent the subtle features of the sound wave propagation process; In terms of physical field configuration, a partitioning strategy is adopted, assigning the air domain and electrolyte region to the pressure acoustics module for analysis, and incorporating solid components such as the battery body structure, including the aluminum-plastic film encapsulation system, porous membrane assembly, LiFePO4 positive electrode-Al current collector system, and graphite negative electrode-Cu current collector system into the solid mechanics module for structural mechanics analysis, balancing computational accuracy and solution efficiency, and laying the model foundation for the acoustic-solid multiphysics coupling simulation of lithium batteries.

[0038] S103: Study on the influence of ultrasonic signals on the sensitivity to porosity defects: Studies on the influence of ultrasonic signals on porosity defect sensitivity include: A multiphysics coupling model is constructed, which combines the physical properties of different media, including the pressure acoustics problem of electrolyte media, the stress-strain problem of solid media, and the stress-strain problem of fluid-structure interaction at the fluid-structure interaction interface. The analysis and discussion of wave equations and control theories in various media provide an accurate and reliable theoretical basis for finite element analysis, specifically including: The propagation of sound waves in an electrolyte medium satisfies the pressure acoustic equation and the time-domain wave equation (Helmholtz equation). The propagation of sound waves in metals such as aluminum and copper, as well as in polymer (PP / nylon) solid media, follows the linear elastic wave equation, and the stress tensor is defined by the constitutive equation. When ultrasound propagates at a fluid-solid interface, the displacement-pressure continuity condition must be met, including continuity of normal stress and continuity of normal displacement. Finite element discretization of the wave equation includes weak forms of the pressure acoustic equation (fluid domain) and elastic wave equation (solid domain). Multiphysics coupling processing of fluid-structure interaction realizes the displacement and stress continuity conditions of the fluid-structure interface through multiphysics nodes, and merges the governing equations of fluid and solid into a unified set of equations. A homogenization model was designed for the multilayer composite structure of lithium iron phosphate batteries, taking into account its periodic distribution characteristics. A simplified lithium-ion battery model was constructed, integrating the periodically occurring dielectrics and increasing the dielectric thickness proportionally to form a homogenized lithium-ion battery model, thereby reducing the modeling difficulty. In the homogenization model, the number of interface layers in the lithium-ion medium is reduced, which amplifies the energy of the echo signal by a certain factor, while having little impact on the first wave of arrival and the time of flight, thereby realizing the simulation of ultrasonic testing of lithium-ion batteries based on the homogenization model. A homogenized finite element model of a lithium-ion battery is designed, including an air domain, a lithium-ion battery domain, and a signal transmitter (receiver). The distance between the sound source and the battery is set as the focal length of the sound source to maximize the energy coupling efficiency between the sound source and the lithium-ion battery.

[0039] S104: Study on the influence of stomatal depth on ultrasonic parameters: Studies on the influence of stomatal depth on ultrasonic parameters include: The acoustic response of pore defects in lithium-ion batteries was simulated. The combined stress amplitude of intact lithium-ion batteries and lithium-ion batteries with pore defects was compared by finite element simulation to study the influence of pore defects on the propagation of ultrasonic longitudinal waves. The influence of pore defects at different depths (first, third, and fifth electrolyte layers) on the energy of the transmitted initial wave was analyzed. The changes in the energy of the transmitted initial wave were used to characterize the pore defects inside the lithium-ion battery. When pore defects were present in the first electrolyte layer, the energy of the transmitted initial signal decreased from 4.453 Pa when there were no defects to 0.128 Pa when there were pore defects. When pore defects were present in the third electrolyte layer, the energy of the transmitted initial signal decreased from 4.453 Pa when there were no defects to 0.951 Pa when there were pore defects. When pore defects were present in the fifth electrolyte layer, the energy of the transmitted initial signal decreased to 0.149 Pa. Through analysis of experimental results, the changes in ultrasonic parameters such as sound pressure and sound attenuation were studied, and the equivalent sound attenuation was calculated. The results show that the established homogenized finite element model of lithium-ion battery can correctly invert the propagation process of sound waves in lithium-ion battery. The receiver simulation transmission signal has a good signal-to-noise ratio and can clearly identify the initial wave of transmission. The simulated transmission signal can effectively characterize pore defects at different depths. As shown in Table 3-4, when there are pore defects in the third electrolyte layer, the equivalent sound attenuation is smaller than that in the first and fifth electrolyte layers with pore defects. This is mainly because the sound diffraction effect is more obvious when there are pore defects in the third electrolyte layer.

[0040] In one exemplary embodiment, the ultrasound-based dynamic defect detection of lithium-ion batteries includes: S201: Design of a method for detecting bubble defects: The design of the bubble defect detection method includes: Dynamic internal defects in lithium-ion batteries are often closely related to user behavior or design flaws in the battery management system. Overcharging is the most dangerous inducing factor among dynamic internal defects in lithium-ion batteries. During overcharging, as the battery's state of charge (SOC) increases, excess lithium ions are unevenly deposited on the negative electrode surface, leading to the growth of lithium dendrites that eventually pierce the separator, inducing an internal short circuit. On the other hand, excess lithium ions detaching from the positive electrode structure can cause its collapse, resulting in the dissolution of active materials and the release of oxygen. The increased electrode potential and oxygen accelerate the oxidative decomposition of the electrolyte. These processes and reactions generate a large amount of heat and gas, ultimately leading to battery thermal runaway. Although the abnormal gas production phenomenon in lithium-ion batteries caused by overcharging is triggered by external abuse, its scale is directly related to the internal material characteristics of the battery. Therefore, from the perspective of detection targets and technical logic, it can be classified as a dynamic internal defect. Under normal operating conditions, lithium-ion batteries produce little and slow gas, which exists in a minute, dispersed form with extremely low density and a very small number of bubbles per unit volume. This gas is typically absorbed or slowly released by the battery's safety mechanisms, without causing significant pressure changes. Furthermore, the gas composition is relatively simple, primarily consisting of carbon dioxide and hydrogen, with no violent side reactions, maintaining the stability of the electrolyte layer (SEI) and inhibiting further decomposition. However, during overcharging, the gas production of lithium-ion batteries increases dramatically, with the electrolyte undergoing violent decomposition to generate large amounts of combustible gases such as CO, CH4, and CH4. Table 1 compares the bubble parameters and characteristics of lithium-ion batteries under normal operating and overcharge conditions.

[0041] Therefore, this application systematically investigates the dynamic evolution characteristics of bubbles inside lithium-ion batteries under overcharge conditions, focusing on monitoring changes in key parameters such as bubble density and size distribution. This allows for the inverse inference of the battery's internal material tolerance defects. By establishing a quantitative relationship between bubble evolution characteristics and material failure severity through ultrasonic parameter sensitivity analysis, abnormal gas generation phenomena inside lithium-ion batteries can be characterized, thereby detecting and providing early warning of the hazards caused by overcharging behavior in lithium-ion batteries. This provides a new technical approach for the safety monitoring and failure prevention of power battery systems.

[0042] Table 1 - Comparison of bubble parameter characteristics between normal and overcharged states of lithium-ion batteries

[0043] Chu et al. conducted an experimental study on the gas generation characteristics of lithium iron phosphate (LFP) batteries during overcharge. The results showed that the main gaseous products of thermal runaway in LFP batteries were CO, H2, CO2, and alkanes, as well as a large amount of electrolyte vapor. Among these, H2 and CO2 had the highest concentrations. Furthermore, the gas sensor first captured H2, followed by CO and CH4; the H2 capture time was 271 s earlier than the smoke and 579 s earlier than the occurrence of thermal runaway. Therefore, this further confirms the feasibility of achieving early warning of battery thermal runaway by capturing gases. The core challenges of bubble detection during overcharge include dynamics, high resolution requirements, penetration limitations, and safety risks. In terms of dynamics, gas is rapidly generated and grows non-linearly during overcharge, requiring the detection system to have real-time or rapid response capabilities to capture transient gas generation events. The high resolution requirement stems from the need to accurately distinguish between micron-sized microbubbles and larger-scale localized gas expansion phenomena, which places strict demands on the spatial resolution and sensitivity of the detection technology. Penetration limitations arise because the multi-layered heterogeneous structure inside the battery makes it difficult to simultaneously achieve both signal penetration depth and resolution. Furthermore, the heat conduction problem caused by contact probes must be avoided during the testing process to prevent exacerbating the risk of battery thermal runaway. A comprehensive solution to these challenges requires balancing multiple factors such as real-time performance, accuracy, penetration capability, and safety.

[0044] Phased array ultrasonic testing offers high resolution, enabling real-time capture of bubble formation, merging, and migration during overcharging. It can clearly identify the spatial distribution of dense microbubbles, distinguish areas of localized thermal runaway risk, and is suitable for multi-layered structures. The beam angle and depth can be adjusted to penetrate the multi-layered electrode-separator structure of lithium-ion batteries, reducing interference from complex internal interfaces. However, its limitations remain its high cost and complexity, requiring professional operation and preventing its widespread adoption in daily life.

[0045] Ultrasonic backscattering is highly sensitive to the scattering signals of dense small bubbles, making it particularly suitable for detecting micron-sized bubbles. It does not require penetrating the entire battery and is suitable for detecting one side of the battery. However, the porous structure of the battery electrodes may cause scattering noise interference, which needs to be combined with time-frequency analysis (such as wavelet transform) or machine learning algorithms to separate bubble scattering noise from structural noise.

[0046] The non-contact advantage of air-coupled ultrasonic testing is offset by low penetration, making it difficult to detect deep air bubbles inside the battery. Ultrasonic transmission methods cannot locate air bubbles and can only reflect overall degradation; signal saturation (completely dark area) in the later stages of overcharging leads to failure.

[0047] Table 2 - Comparison of the advantages, limitations, and applicable scenarios of ultrasound testing methods

[0048] Based on the comparative analysis in Table 2 above, this application ultimately selected ultrasonic backscattering as the core method for detecting overcharge bubbles in lithium-ion batteries. By analyzing the echo signal, while maintaining non-destructive testing, a high-frequency probe is used to capture the reflection characteristics of micron-sized bubbles, balancing the requirements for penetration depth and resolution.

[0049] Feasibility analysis for ultrasonic backscattering detection of bubbles, including: A porous medium acoustic scattering model is established to describe the propagation and scattering behavior of sound waves in a solid-liquid-gas multiphase medium. The influence of structural features such as porosity and pore size distribution on sound wave propagation is analyzed. Based on Biot theory, the governing equations satisfied by sound wave propagation in porous media are studied, as well as the influence of the acoustic impedance difference between pores and the framework on the scattered signal. Equivalent analysis of lithium-ion battery bubbles and porous media was conducted. The similarity between the bubble system and porous media was analyzed from the aspects of pore structure and acoustic response mechanism. The influence of bubble size, density and connectivity on sound wave propagation path was studied, as well as the interface scattering and wave field interference caused by the difference in acoustic impedance between bubbles and electrolyte / electrode. The changes in bubble parameters were characterized by in-situ sound velocity monitoring to achieve effective detection of bubble defects. S202: Finite Element Simulation Model Design The finite element simulation model design includes: A two-dimensional monolayer electrochemical model of lithium iron phosphate batteries was constructed based on the following key parameters and assumptions: Electrode materials: the positive electrode is lithium iron phosphate (LiFePO4), the negative electrode is graphite, and the stoichiometric ratio of lithium ions to electrons at the positive and negative electrodes is set to 1:1; Electrolyte: The composition is lithium hexafluorophosphate (LiPF6), and the concentration is set to 1M; Electrode structure: The porosity of both the positive and negative electrodes is set to 40%. The geometry of the positive electrode is a two-dimensional rectangle with a width and height of 200 μm. Separator: The thickness is set to 25μm to act as a physical barrier between the positive and negative electrodes, while allowing lithium ions to pass through; Out-of-plane thickness: set at 200 μm to reflect the design trend of micro or thin batteries; Other parameters: Parameters such as lithium-ion diffusion coefficient and tortuosity of electrode material are either assumed to be default values ​​or reasonably estimated based on the physical properties of the material. The theoretical specific capacity of the positive electrode material is set to be approximately 170 mAh / g, and the density is approximately 3.6 g / cm³. The positive electrode capacity is calculated according to the formula, and the charging current is calculated using the charge / discharge rate formula. The theoretical specific capacity of the negative electrode material is set to be approximately 372 mAh / g, and the density is approximately 2.26 g / cm³. The thickness of the negative electrode material is calculated according to the formula, and the actual material thickness is determined by taking porosity into account. An overcharge model was constructed, and the overcharge conditions were set to 1.0C or 1.5C. Based on multiphysics finite element simulation software, an electrochemical model of lithium iron phosphate battery was constructed in the lithium-ion battery physical field interface. Porous electrode parameters were set, with the electrode phase volume fraction set to 0.6 and the electrolyte phase volume fraction set to 0.4. The initial conditions of the model were set. The initial value of the lithium intercalation concentration of the positive electrode active particles was taken as the maximum lithium concentration reference value in the material properties of LiFePO4. The initial value of the lithium intercalation concentration of the negative electrode graphite particles was set to 0 mol / m³. A ground potential boundary condition was applied at the boundary of the negative electrode current collector. The membrane porosity parameter was set to 0.4. The initial state of charge of the battery is defined by the open-circuit voltage initialization method. The influence of the solid-phase potential gradient on the electrode edge effect is ignored. Only the boundary condition settings in the charging mode are retained. According to the 1.0C rate charging requirement, the corresponding current density parameters and cutoff voltage parameters are set to complete the overcharge condition model. To construct a model of internal bubble defects in lithium-ion batteries, bubbles with a certain pore size are randomly generated within the separator interface of lithium iron phosphate batteries. This application employs the following steps to construct an electrolyte geometric model containing random bubbles: (1) Definition of electrolyte layer space: The geometric parameters of the two layers of separator / electrolyte are set according to the layered stacked structure of the battery. Assuming that the battery is stacked along the vertical direction (z-axis), the two-dimensional model is constructed in the xz plane. The thickness of the separator / electrolyte is 25μm, the z-axis range is z∈[0,25]μm; the width is 200μm, the x-axis range is x∈[0,200]μm; (2) Bubble space constraint: Taking a bubble radius of 10 μm as an example, the center of the bubble must be completely located within the electrolyte layer, and the edge must be ≥10 μm away from the electrolyte boundary; the minimum center-to-center distance between bubbles must be ≥20 μm to avoid geometric overlap; (3) Random center generation algorithm: The Monte Carlo method is used to generate bubble center coordinates. First, the coordinate range is restricted. In the x direction, the coordinates are [10, 190] μm, and in the z direction, they are centered and the coordinates are [10, 15] μm. Second, anti-overlap verification is performed. For each newly generated bubble center coordinate, the Euclidean distance between it and the existing bubble is calculated. If the distance is less than 20 μm, the coordinates are regenerated until the non-overlap condition is met.

[0050] Set pressure acoustic properties and solid mechanical properties, including parameters such as sound velocity, density, acoustic impedance, Young's modulus and Poisson's ratio, and systematically assign values ​​to core acoustic parameters such as material mass density, longitudinal wave propagation rate and transverse wave velocity through multi-source data fusion method; A boundary control strategy conforming to the propagation characteristics of the wave equation is constructed. The Perfectly Matched Layer (PML) theory is adopted to construct a spatially gradient PML structure and set the gradient parameters for continuous transition of material impedance to ensure complete dissipation of acoustic energy in the boundary transition region. Mesh partitioning is performed using a spatial step size of approximately one-sixth of the characteristic wavelength of the ultrasonic wave. Local mesh refinement is then carried out in conjunction with geometric model features to create mesh systems with different shape characteristics. This application employs an acoustic-solid coupled modeling method, integrating a pressure acoustics module and a solid mechanics module for co-simulation. The pressure acoustics module focuses on describing the propagation law of pressure fluctuations in elastic media, obtaining the sound pressure field distribution characteristics by solving the Helmholtz equation. The solid mechanics module, based on continuum mechanics theory, focuses on analyzing the constitutive relations and stress-strain responses of the battery cell stack structure. By constructing a multi-physics bidirectional coupling algorithm, the interaction between the mechanical behavior and acoustic properties of the lithium-ion battery cell stack structure under ultrasonic excitation can be characterized, thereby establishing a complete acoustic-mechanical coupling model.

[0051] To ensure that the model accurately reflects the actual working conditions, the following key elements need to be optimized during the parameter setting stage: First, establish the multi-field coupled constitutive equations of material properties and calibrate the elastic modulus and acoustic attenuation coefficients of the positive and negative electrode materials; second, improve the interface contact algorithm to accurately describe the acoustic impedance matching characteristics of the electrode-diaphragm interface; finally, use the iterative correction method to verify the convergence of the boundary conditions, and achieve dynamic optimization of the model parameters by comparing the error distribution of experimental data and simulation results.

[0052] The implementation of the "acoustic-solid interaction, transient" multiphysics interface can be obtained by solving the following partial differential equation:

[0053] For the total pressure field; It is a monopole source; It is a dipole source; Background pressure; For local pressure; This represents the total pressure field.

[0054] By constructing a cross-medium coupling interface, the continuous transmission of the sound pressure field at the fluid-solid interface is achieved, thereby establishing an acoustic-solid coupled multiphysics system and accurately reproducing the transmission characteristics of ultrasound within the battery cell. Based on a multiphysics partitioning modeling strategy, solid mechanics modules are configured in the positive and negative electrode regions, and constitutive models of lithium-ion battery electrodes are constructed based on continuum dynamics theory. The separator-electrolyte composite system is defined as a viscoelastic fluid medium, and its sound propagation characteristics are characterized using the modified Navier-Stokes equations. A pressure acoustics module covers the separator-electrolyte composite system, and the sound pressure gradient distribution in each region is obtained synchronously by solving the time-domain wave equation.

[0055] In the construction of the numerical simulation system for ultrasonic backscattering of bubble defects in lithium iron phosphate batteries, this application has completed the discretization of the geometric model and the multiphysics coupling configuration, and has initially established the basic framework for numerical simulation. To achieve a complete reproduction of the sound wave propagation process, a transient sound pressure excitation load still needs to be applied to the boundary of the computational domain. This application adopts the typical operating mode of a piezoelectric transducer, whose energy conversion mechanism is a nonlinear coupling process of electrical energy and mechanical vibration. Based on the backscattering detection principle of a single-probe transmitter and receiver in the same location, the vertical upper boundary of the numerical model is defined as the wave energy input interface.

[0056] The ultrasonic excitation signal is a time-harmonic wave signal modulated by a Gaussian envelope, and its mathematical expression can be described as follows: in, The signal is an ultrasonic pulse excitation signal. To adjust the amplitude of the signal, The steepness of the signal is related to its bandwidth. The duration of the signal. The center frequency of the signal.

[0057] In a two-dimensional acoustic numerical model for detecting bubble defects in lithium-ion batteries, the system employs a single-probe coaxial configuration to achieve spatiotemporal synchronization of excitation and reception. This scheme loads a transient acoustic pressure excitation packet onto the boundary source, synchronously acquiring the acoustic pressure disturbance response of discrete nodes, and reconstructing the backscattered acoustic field characteristics of the medium through spatial integration. This numerical experiment uses the finite-difference time-domain method to solve the acoustic wave equation, setting the carrier frequency range to 5-10 MHz and the pulse duration to 10... -6 The Gaussian-modulated broadband signal of s is used as the excitation source.

[0058] S203: Study on the Influence of Ultrasonic Frequency on the Sensitivity of Internal Bubble Detection: The study on the influence of ultrasonic frequency on the sensitivity of internal bubble detection includes: Based on the simulation calculation model established above, parametric scanning analysis of bubble parameters is performed to observe the variation law of its echo signal. By changing the ultrasonic frequency, the optimal frequency suitable for the model was explored, and the dependence of the propagation characteristics of ultrasonic signals in the multiphase medium of the battery on the excitation frequency was studied. Under controlled conditions of bubble distribution and size (bubble radius of 10 μm), ultrasonic backscattering simulation tests were conducted to analyze the ultrasonic signal transmission waveforms at different frequencies. It was observed that at a frequency of 5MHz, the amplitude was relatively small and the waveform was relatively smooth; at 10MHz, the amplitude increased and the waveform oscillation was more obvious; at 15MHz, the amplitude was the largest and the waveform oscillation was the most intense. Analysis shows that as the frequency increases, the wavelength gradually decreases, the matching degree with the bubble size improves, and the amplitude of the reflected signal increases. The conclusion is that an ultrasonic frequency of 5 MHz is suitable for detecting thicker battery structures, but the resolution is low; at 10 MHz, the resolution and penetration are more balanced, which is suitable for general detection needs; at 15 MHz, the resolution is the highest, which is suitable for detecting tiny bubble defects, but the penetration may be insufficient. The results confirm that ultrasonic parameters are highly sensitive to bubble defects, laying the foundation for subsequent research into the correlation between the depth and location of pore defects and ultrasonic parameters.

[0059] S204: Study on the Influence of Bubble Parameters on the Acoustic Properties of Lithium-ion Batteries: The study on the influence of bubble parameters on the acoustic properties of lithium-ion batteries includes: A parametric scanning method based on ultrasonic propagation theory is constructed to systematically evaluate the influence of bubble morphological parameters (such as size and distribution density) on ultrasonic signal response, with a focus on studying the response characteristics of two key parameters, bubble radius and density, to ultrasonic time-domain signals. Under ultrasonic testing conditions with a center frequency of 10MHz, the ultrasonic signal transmission waveforms of dense bubbles and sparse bubbles were compared, as well as the ultrasonic signal transmission waveforms of bubbles with different radii, to verify the feasibility of ultrasonic testing for changes in parameters such as bubble size and density inside lithium-ion batteries. Analysis revealed that the received waveform of sparse bubbles exhibits a discrete pulse sequence with smooth waveform fluctuations and distinct echo time intervals between individual bubbles. In contrast, dense bubble clusters may cause the phases of the scattered signals to cancel each other out, resulting in an overall amplitude lower than that of sparse distributions. Furthermore, the waveform fluctuates continuously with dramatic amplitude changes, making it more complex and difficult to identify the characteristics of the reflected signals. Additionally, the ultrasonic signals of dense bubbles attenuate significantly, and the waveform duration is shortened. Comparing the ultrasonic detection waveforms of bubbles with different radii (e.g., 1 μm and 10 μm), it is found that for a bubble with a radius of 10 μm, the scattering and reflection of ultrasonic waves are stronger, resulting in a greater echo signal intensity. The 1 μm bubble is in the pure Rayleigh scattering region, while the 10 μm bubble is dominated by acoustic scattering. Further analysis based on the nonlinear dynamics of the bubble is needed. The scattering intensity is directly proportional to the sixth power of the bubble radius and inversely proportional to the fourth power of the wavelength, as shown in the following equation: .

[0060] in, Let λ be the scattering intensity, r be the bubble radius, and λ be the ultrasonic wave length. The acoustic pressure waveform of a 1 μm bubble has a smaller amplitude and more gentle fluctuations, which is consistent with the characteristics of Rayleigh scattering; the acoustic pressure waveform of a 10 μm bubble has a larger amplitude and more violent fluctuations, which is consistent with the increase in scattering intensity and the increased complexity of scattering characteristics caused by the increase in the size of the scatterer. Based on the overfilling degree classification, a density-size spatial distribution model of bubble defects is established, and sound pressure-pressure distribution maps of different models are shown. A comparison of the acoustic pressure response of the spatial distribution of bubble defect density-size under multi-frequency detection was conducted, and combined with frequency domain waveform analysis. The results show that larger bubble sizes result in larger scattering cross-sections and stronger scattering intensity, leading to the strongest received backscattered signal and a higher frequency domain acoustic pressure amplitude. Conversely, smaller bubbles have relatively weaker scattering capabilities and smaller scattering cross-sections, resulting in weaker received backscattered signals and a lower frequency domain acoustic pressure amplitude, as shown in the following equation:

[0061] in, Z1 is the scattering cross-sectional area, Z2 is the acoustic impedance of the surrounding medium, λ is the wavelength of the ultrasonic wave in the surrounding medium, and r is the radius of the scattering body (bubble).

[0062] The amplitude of the echo is determined by the competition between the reflection enhancement effect and the attenuation enhancement effect. The final amplitude is related to the relationship between bubble size and wavelength, ultrasonic frequency, detection depth and bubble distribution density. When comparing high-density large bubbles with low-density small bubbles, the reflection enhancement effect is greater than the attenuation effect. Even if the density of large bubbles is high, their strong reflection may offset part of the attenuation, resulting in an amplitude that is still higher than that of low-density small bubble groups.

[0063] Example 2: This embodiment provides a specific application scenario for implementing the lithium battery full life cycle health status method. It uses the Verasonics multi-channel ultrasonic testing technology system to perform ultrasonic air coupling testing on lithium iron phosphate pouch batteries and button batteries, verifying the feasibility of the ultrasonic testing scheme and its high sensitivity to internal defects. The specific implementation process includes the following: (I) Detection System Setup Ultrasonic testing equipment: An air-coupled multi-probe transmission array system is selected, with a probe operating frequency range of 1MHz to 20MHz, to meet the needs of detecting pore defects of different depths and sizes.

[0064] Signal processing system: Equipped with a high-speed data acquisition card with a sampling rate of no less than 100MHz, used for real-time acquisition of ultrasound signals and signal processing and analysis through dedicated software.

[0065] Mechanical positioning system: Employs a high-precision robotic arm with a positioning accuracy better than 0.1mm, used to precisely control the relative position of the probe and the battery, ensuring the stability and repeatability of the testing process.

[0066] (II) Construction of the homogenization model Model simplification: Based on the periodic structural characteristics of lithium-ion batteries, the multilayer composite structure is simplified into a homogenized model of separator-positive electrode-separator-negative electrode-separator, reducing model complexity.

[0067] Parameter settings: Based on the physical properties of the actual battery materials, set parameters such as density, elastic modulus, Poisson's ratio, and sound velocity for each layer of material.

[0068] Mesh generation: Adaptive mesh generation technology is adopted, with the mesh size based on 1 / 6 of the sound wave wavelength, to ensure a balance between computational accuracy and efficiency.

[0069] (III) Detection of Porosity Defects Defect simulation: Air layers were embedded in the first, third, and fifth layers of the electrolyte layer of a lithium-ion battery to simulate pore defects at different depths.

[0070] Ultrasonic excitation: A harmonic excitation signal based on Hanning window modulation is used, with a center frequency of 400kHz and a spectrum broadening of 0.5MHz. Dynamic displacement excitation is applied by a probe array symmetrically arranged along the interface between the positive and negative electrodes of the battery.

[0071] Signal acquisition and analysis: Acquire transmitted ultrasonic signals, analyze the amplitude attenuation of the time-domain signal and the spectral changes of the frequency-domain signal, and determine the presence and depth of porosity defects based on sound pressure and sound attenuation data.

[0072] (iv) Experimental verification This invention employs the Verasonics Vantage 256 multi-channel ultrasonic testing technology research system. This system features independent control of each channel's transmission / reception, arbitrary waveform excitation, and RF data acquisition, supports multiple imaging modes, and is suitable for various research scenarios. The system's technical specifications are as follows: Number of channels: 256; Signal transmission frequency range: 0.5~20MHz; Signal transmission delay: 4.0ns; Programmable pulse signal amplitude: 3~190Vpp; Received signal frequency: 1~50MHz; A / D converter: 14-bit, sampling rate up to 62.5MHz; Anti-aliasing filter cutoff frequency: programmable to 10, 15, 20 and 30 MHz; Digital filter: Two-stage independent, user-programmable, symmetrical RF digital filter; Data acquisition and storage rate: up to 1,000,000 f / s; Supported transducer types: L11-5V, L22-14VX, GE9L-D, L22-8VCMUT, etc. In this experiment, the center frequency was set to 6.25MHz, and an L11-5V array transducer was used, with the following parameters: Number of array elements: 128; Center-to-center distance: 0.3mm; Estimated focal length: 18mm; Sensitivity: -52±3dB; (V) Ultrasonic backscattering detection experiment and result analysis 1. Experiment on soft-pack lithium iron phosphate batteries Experimental Design: Five groups of pouch lithium iron phosphate batteries were selected as experimental subjects, and a controlled experimental design was adopted: Control group (Group A): 2 groups of defect-free batteries; Experimental group (Group B): 3 groups of batteries with defects at different locations; Each sample was subjected to layered detection on both the front and back sides, and data was collected from three detection planes along the vertical direction: the upper layer, the middle layer, and the lower layer.

[0073] Test results: Defect-free battery test results ( Figure 5 (where a, b, and c represent the upper, middle, and lower front layers, respectively): The internal layered structure of the lithium iron phosphate battery is clearly visible, with relatively clear layering, verifying the high sensitivity of ultrasonic testing to the internal structure of lithium-ion batteries.

[0074] Defective battery test results ( Figure 6 (where a, b, and c represent the top, middle, and bottom front layers, respectively): It can be clearly seen that the top layer of battery B1, the middle layer of battery B2, and the middle and bottom layers of battery B3 have irregularities and wrinkles in the internal material, indicating that ultrasonic testing can effectively identify defects inside the battery.

[0075] 2. Button lithium iron phosphate battery experiment Experimental Design: Three groups (Group C) of artificially prepared button lithium iron phosphate batteries were used, and ultrasonic backscattering air coupling tests were performed on their front and back sides respectively.

[0076] Battery structural parameters: Outer shell diameter: 20mm; Gasket diameter: 15.8mm, thickness: 0.5mm; Shrapnel diameter: 15.4mm, thickness: 1.1mm; Test results (such as) Figure 7 (as shown) Inspection results for C1, C2, and C3 (front and back): Due to the different diameters of the button battery gasket, spring, and casing, there is uneven material distribution on both sides. Ultrasonic testing clearly shows this material difference, indicating that ultrasonic testing is also applicable to the internal inspection of button batteries.

[0077] (vi) Experimental Conclusions The above experiments verify that the ultrasonic testing method of this invention can effectively detect the layered structure and defects inside different types of lithium iron phosphate batteries. Experimental results show that: High sensitivity: Ultrasonic testing can clearly show the internal layered structure of the battery and is highly sensitive to irregularities and wrinkles in the internal materials.

[0078] Multi-type applicability: This method is applicable not only to pouch cells but also to button cells, demonstrating its broad applicability to quality assessment of multiple types of lithium batteries.

[0079] Defect identification capability: Ultrasonic testing can effectively identify the location and morphology of defects inside the battery, providing a reliable detection method for quality control and safety monitoring of lithium-ion batteries.

[0080] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0081] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for monitoring the health status of a lithium battery throughout its entire lifecycle, characterized in that, The health status monitoring method includes: Emit ultrasonic waves to the lithium battery under test and receive response signals caused by the battery's internal structure and / or defects; The response signal is processed to identify defects inside the lithium battery; The defects include static defects and dynamic defects, and the state of the defects is determined based on the characteristic parameters corresponding to the defects.

2. The method for monitoring the health status of a lithium battery throughout its entire life cycle according to claim 1, characterized in that, The identification of the static defects includes: A non-contact air-coupled multi-probe array was used to collect ultrasonic data from different depths inside the battery. Based on the homogenization theory of solid mechanics, characteristic parameters sensitive to pore depth are extracted from the waveform data. The feature parameters sensitive to pore depth are input into a preset depth-parameter mapping relationship to determine the depth location of pore defects.

3. The method for monitoring the health status of a lithium battery throughout its entire life cycle according to claim 1, characterized in that, The identification of the dynamic defects includes: During the charging and discharging process of lithium batteries, an ultrasonic backscattering probe is used to emit ultrasonic excitation and receive backscattering signals caused by bubbles and structural inhomogeneities inside the battery. Based on the acoustic theory of porous media, time-domain and frequency-domain characteristic parameters related to bubble morphology and distribution are extracted from the backscattered signal. The time-domain and frequency-domain characteristic parameters are input into a preset bubble state-signal response model to determine the evolution state of gas generation defects inside the battery.

4. The method for monitoring the health status of a lithium battery throughout its entire life cycle according to claim 2, characterized in that, The acquisition of ultrasonic wave data from different depths inside the battery includes: An air-coupled multi-probe transmission array system is adopted, with transmitting and receiving probes symmetrically arranged on both sides of the positive and negative electrode interface of the lithium-ion battery to form a multi-channel detection layout. The sound source is driven by a harmonic excitation signal with a center frequency of 400kHz and a bandwidth of 0.5MHz. Based on the established multi-channel detection layout, transmitted and reflected waveform data from different depths inside the battery are collected synchronously through multiple channels. The sound pressure time-domain waveform and energy attenuation characteristics are recorded to obtain preliminary ultrasonic waveform data. The obtained preliminary ultrasonic waveform data is preprocessed, including DC removal, bandpass filtering, time window selection, and signal normalization, to obtain the ultrasonic waveform data.

5. The method for monitoring the health status of a lithium battery throughout its entire life cycle according to claim 2, characterized in that, A multi-physics homogenized finite element model was constructed. Based on the homogenization theory of solid mechanics, feature extraction and inversion analysis were performed on the ultrasonic shape data to obtain key feature parameters that are sensitive to the depth of porosity defects. By comparing the key feature parameters of defective and defect-free models, the feature parameters most sensitive to pore depth and location are identified; among them, the key feature parameters include: sound pressure amplitude, transmitted initial wave energy, sound attenuation, and waveform distortion ultrasound.

6. The method for monitoring the health status of a lithium battery throughout its entire life cycle according to claim 2, characterized in that, The extracted feature parameters are input into a preset depth-feature parameter mapping model. Combined with the acoustic propagation law of the multi-layer composite structure and the homogenization correction factor, the depth location and relative scale of the pore defects are determined, thus obtaining a high-precision static identification result of the pore defects inside the lithium-ion battery.

7. The method for monitoring the health status of a lithium battery throughout its entire life cycle according to claim 5, characterized in that, Feature extraction and inversion analysis are performed on the ultrasonic wave data to obtain key feature parameters sensitive to the depth of porosity defects, specifically including: A homogenized finite element model of multiphysics field coupling of solid mechanics, pressure acoustics, and fluid-structure interaction is established. In the model, the electrolyte region is treated according to the acoustic domain, the electrode and encapsulation material are modeled according to the solid mechanics domain, and the interface conditions are at the interface between solid and fluid, satisfying the displacement-pressure continuity condition. The periodic layered structure of lithium-ion batteries is modeled and simplified to construct a homogenized equivalent model; Typical pore defects are introduced into the homogenized equivalent model, and the pore density, size and distribution characteristics are defined to simulate the influence of pores at different depths on sound pressure propagation, thus completing the finite element simulation. Based on finite element simulation results and measured signals, the transmitted wave energy, sound pressure attenuation rate, time-of-flight offset and backscattering amplitude at different depths are compared to extract the set of composite feature parameters most sensitive to pore depth.

8. The method for monitoring the health status of a lithium battery throughout its entire life cycle according to claim 3, characterized in that, During the charge-discharge cycle of a lithium-ion battery, an air-coupled or point-focusing ultrasonic backscattering probe is used to emit a broadband ultrasonic excitation signal and simultaneously receive backscattered signals generated by the electrode / separator interface and internal bubble groups; the excitation frequency is controlled within 0.3–0.5 MHz; the time window for signal acquisition covers the entire charging cycle, and voltage, current, and electrochemical state quantities are recorded in real time.

9. The method for monitoring the health status of a lithium battery throughout its entire life cycle according to claim 3, characterized in that, Based on the acoustic scattering model of porous media, the bubble group is equivalent to a porous domain with randomly distributed porosity. Multidimensional features sensitive to bubble morphology and distribution are extracted from the backscattered signal, including time-domain features, frequency-domain features and statistical features. The multidimensional features are tracked over time to form an acoustic evolution curve, i.e., an acoustic feature vector, that changes with the charging state.

10. The method for monitoring the health status of a lithium battery throughout its entire life cycle according to claim 3, characterized in that, Determining the evolution of gas generation defects inside the battery includes: Construct or call a bubble state-signal response model based on finite element simulation and experimental calibration, where the input is the extracted acoustic feature parameters and the output is the bubble state index; The real-time extracted feature parameters are input into the response model to identify the following states: bubble initiation, bubble aggregation, and bubble rupture or migration; combined with the response model output and the electrochemical stage determination results, the evolution trend and severity of gas generation defects inside the battery are determined, and dynamic health status is monitored. The electrochemical stage determination results are: electrochemical parameters and indicators related to the state of the lithium battery during charging and discharging; including the battery's state of charge, internal resistance, charging and discharging current, and temperature data.

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