Lithium metal battery in-situ time space thermal monitoring system based on distributed optical fibers

Abstract

The invention discloses a lithium metal battery in-situ time space thermal monitoring system and method based on a distributed optical fiber, and the system employs a distributed optical fiber sensing technology (OFDR) to combine with a spiral arrangement structure of a single mode fiber (SMF), converts one-dimensional temperature data into two-dimensional plane distribution, and achieves the precise monitoring of an internal temperature field of a battery. Besides, by introducing a super-resolution algorithm based on an SRGAN model, a low-resolution temperature image is converted into a high-resolution image, details are reconstructed, and the spatial resolution and the time resolution are improved. The method integrating physical sensing and the super-resolution algorithm can reduce the thermal runaway risk, prolong the service life of the battery, realize more uniform lithium deposition and prolong the cycle life of the battery, thereby providing powerful technical support for safe operation and performance optimization of the lithium metal battery. In addition, a hot spot generation mechanism can be explored, and the effectiveness of a battery protection strategy is verified. According to the invention, real-time, lossless and high-precision large-measurement-area super-resolution monitoring can be provided for the lithium metal battery.

Description

Technical Field

[0001] The invention relates to battery thermal monitoring technology, and in particular to an in-situ spatial-temporal thermal monitoring system for lithium metal batteries based on distributed optical fibers. Background Art

[0002] As the global transition to clean energy accelerates, the demand for innovative energy storage technologies continues to increase. Lithium metal electrodes are emerging as a transformative solution, with a theoretical capacity an order of magnitude higher than that of traditional lithium-ion batteries. This significant progress has made lithium metal batteries (LMBs) central to achieving the high energy density required for future technologies. However, the practical application of such batteries is hindered by key safety issues, and their performance degradation and safety show strong temperature dependence, especially the risk of thermal runaway, which can lead to serious fires and explosions. Therefore, it is crucial to implement rigorous thermal monitoring throughout the life cycle of LMBs. By reducing safety risks while leveraging the high capacity of lithium metal, these efforts will promote the development of next-generation high-energy-density, resource-efficient battery systems to help the clean energy transition.

[0003] With the advancement of battery mechanism research and thermal management technology, researchers have gradually realized that the internal temperature of the battery is the most critical parameter that determines thermal runaway. At present, the internal temperature monitoring technologies of lithium metal batteries include electrochemical impedance spectroscopy (EIS), ultrasonic imaging, fiber Bragg grating method, and optical frequency domain reflection method.

[0004] Electrochemical impedance spectroscopy is a "quasi-steady-state frequency domain measurement method" that measures electrical parameters such as current, potential, and resistance to obtain the electrochemical impedance spectrum of the battery. Through the electrochemical impedance spectrum curve, the battery temperature monitoring at the macro level can be obtained. This method is real-time and the measurement results are easy to process. The disadvantage is that external measurement can only measure the overall temperature of the battery, cannot detect local hot spots, and is a single-point detection with a small detection area.

[0005] Ultrasonic imaging refers to the use of ultrasonic technology to monitor batteries. By exciting and receiving ultrasonic signals on the battery surface, using the changes in signal characteristics caused by changes in battery performance and structural differences during the propagation of ultrasonic guided waves inside the battery, the relationship between ultrasonic transmission parameters and battery status is established, thereby obtaining information on the internal temperature of the battery. This method has high sensitivity, low cost, easy use, and fast speed, but this method is based on fixed-point monitoring outside the battery. For commercial soft-pack batteries, it is impossible to detect the heterogeneity of the entire battery.

[0006] The fiber Bragg grating method is made by exposing the fiber core laterally to strong ultraviolet light with a periodic pattern, and the change in the central wavelength of the Bragg reflected light is used to determine the change in the corresponding physical quantity inside the battery. This method has high resolution and can achieve in-situ battery temperature monitoring. Although this method can be positioned, temperature monitoring can only be performed in areas where the optical fiber has been specially treated, and distributed monitoring cannot be achieved.

[0007] Optical frequency domain reflectometry (OFDR) uses the backscattering of Rayleigh in the optical fiber due to the microscopic inhomogeneity of the refractive index. The system consists of a linear tunable laser, an interferometer structure (reference arm and measurement arm), a photodetector, and a data acquisition instrument. By analyzing the frequency in the spectrum, the position of the scattering point in the sensing fiber can be determined, thereby achieving fiber positioning. The wavelength shift generated based on the Rayleigh scattering pattern can be used to obtain the change in the corresponding physical quantity at that location. This method has high sensitivity and is distributed, but the number of detectable points is still limited.

[0008] Compared with electrochemical impedance spectroscopy and ultrasonic imaging, fiber Bragg grating method and optical frequency domain reflection method have the advantages of in-situ, non-destructive, multi-point and distributed. However, in the current monitoring technology, the number of detectable points is still very low relative to the electrode surface area, which is one-dimensional temperature data. Therefore, the precise operation and distributed temperature monitoring of the entire lithium metal electrode throughout the battery life cycle has not been solved.

[0009] Missing data can be filled using regression analysis, interpolation methods, and Bayesian inference. However, these traditional interpolation methods are prone to loss or blurring of details. In recent years, machine learning-based super-resolution techniques have been applied to convert low-resolution images into high-resolution images while reconstructing details.

[0010] It should be noted that the information disclosed in the above background technology section is only used for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0011] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology and provide an in-situ spatial and temporal thermal monitoring system and method for lithium metal batteries based on distributed optical fiber.

[0012] To achieve the above object, the present invention adopts the following technical solutions:

[0013] An in-situ spatial and temporal thermal monitoring system for lithium metal batteries based on distributed optical fiber, comprising:

[0014] A single-mode optical fiber (SMF), connected to the inside of the battery, wherein the single-mode optical fiber (SMF) has a spiral arrangement structure so that the optical fiber is distributed along a two-dimensional plane, and is used to realize temperature monitoring in a physical space;

[0015] An OFDR system for capturing temperature data of a one-dimensional continuous set of points along the single mode optical fiber (SMF), the OFDR system comprising:

[0016] A linear tunable laser for transmitting an optical signal;

[0017] An interferometer structure, including a reference arm and a measuring arm, for measuring a reflected light signal propagating along the optical fiber;

[0018] Photoelectric detector and data acquisition instrument, used for collecting optical signal data and converting it into electrical signal data;

[0019] A data processing unit is used to convert the one-dimensional temperature data captured by the OFDR system into two-dimensional plane distribution data corresponding to the spiral arrangement structure of the single-mode optical fiber (SMF) to achieve accurate monitoring of the temperature distribution inside the battery.

[0020] Furthermore, the data processing module includes a super-resolution algorithm module for realizing super-resolution processing of temperature data and improving the spatial resolution of temperature monitoring. The super-resolution algorithm module includes:

[0021] The SRGAN model is used for machine learning to achieve super-resolution of temperature mapping. The SRGAN model includes:

[0022] A generator network, preferably a ResNet block, for generating super-resolution images;

[0023] Discriminator network, a CNN structure, is used to evaluate high-resolution and super-resolution images.

[0024] Furthermore, the generator network includes a deep network consisting of five ResNet blocks, each ResNet block enhances information flow through skip connections, wherein each ResNet block contains 64 convolution kernels of size 9×9, a step size of 1, and a padding of 4; the discriminator network is a convolutional neural network (CNN) using a leaky ReLU activation function; the output layer includes a dense block followed by a sigmoid function, which is used to perform binary scoring on high-resolution and super-resolution images to evaluate image quality.

[0025] Further, the system includes an external temperature experimental device for constructing a training data set, the device comprising:

[0026] A plurality of ceramic heating elements of different shapes connected to a single-mode optical fiber (SMF) arranged in a spiral to simulate the temperature distribution inside the battery;

[0027] An infrared camera is used to collect temperature data of the ceramic heating element to form a data set for training a super-resolution algorithm model; wherein the temperature of the ceramic heating element is controlled by a power supply, and the data set is used for machine learning to achieve super-resolution of temperature mapping.

[0028] Furthermore, the temperature change video captured by the infrared camera is used to record the temperature distribution on the battery surface; frames are extracted from the video as images, which are synchronized with the data acquisition time of the OFDR system to form an original data set; wherein the original data set is processed by data enhancement technology, and the data enhancement technology includes random rotation, reflection, translation, scaling, brightness and contrast adjustment, Gaussian blur, noise addition and cropping.

[0029] Furthermore, it also includes:

[0030] Battery testing systems to perform capacity and voltage readouts of batteries to monitor the electrochemical performance of the batteries;

[0031] A computer, including the data processing unit, for collecting and processing data from the battery testing system and the infrared camera, and managing data acquisition and analysis of the OFDR system;

[0032] Scanning electron microscopy (SEM), used to observe the anode morphology after battery cycling to evaluate the physical state and structural changes of the battery;

[0033] The computer synchronizes data from the OFDR system, infrared camera, and battery testing system, and performs data analysis and image processing.

[0034] Furthermore, the data processing unit identifies areas without temperature readings and fills the data of these areas with preset room temperature values, such as 22°C.

[0035] Further, a lithium anode protection strategy is applied to the monitored lithium metal battery, and the lithium anode protection strategy includes:

[0036] Pyramid patterned lithium anode, which increases the surface area and improves the ion transport path by forming a pyramid structure on the surface of the lithium anode;

[0037] Copper mesh patterned lithium anode, by creating micro patterns on the surface of lithium anode to guide the uniform distribution and deposition of lithium ions using micro patterns;

[0038] PLA-coated lithium anodes, which improve thermal stability by applying a protective coating on the surface of lithium anodes to utilize their chemical and mechanical barrier properties;

[0039] Monitoring was performed by the OST-SRTM system to evaluate the effectiveness of different lithium anode protection strategies.

[0040] Furthermore, the pyramid patterned lithium anode is formed by using a pyramid-shaped mold made by high-precision 3D printing technology to emboss a microstructure pattern on the lithium anode. The mold size is 50 mm×50 mm×0.5 mm, and the height, width, and spacing of a single pyramid are 50 μm, 50 μm, and 40 μm, and a pressure of 2 MPa is applied during the embossing process.

[0041] The copper mesh patterned lithium anode uses a 250-mesh copper mesh to apply a pressure of 1 MPa to the lithium surface, and performs two presses to improve the uniformity of surface modification, the first press in the direct direction, and the second press after rotating the mesh 45 degrees;

[0042] The PLA-coated lithium anode is prepared by dissolving PLA in a DMSO solution, stirring at 130° C. for 5 hours and stirring at 80° C. for 24 hours to obtain a uniform milky white solution, and then preparing a PLA solution with a mass ratio of 1.2%. The solution is then scraped onto the surface of the lithium metal to form a PLA coating.

[0043] A method for in-situ spatiotemporal thermal monitoring of a lithium metal battery uses the in-situ spatiotemporal thermal monitoring system for a lithium metal battery based on distributed optical fiber.

[0044] The present invention has the following beneficial effects:

[0045] The present invention provides a system and method for in-situ spatial-temporal thermal monitoring of lithium metal batteries based on distributed optical fibers. The innovative monitoring system of the present invention can provide real-time, non-destructive, high-precision temperature monitoring for lithium metal batteries, covering a large measurement area, and has super-resolution capabilities. The system utilizes the high sensitivity and distributed characteristics of distributed optical fiber sensing technology (OFDR), combined with the design of the spiral arrangement structure of single-mode optical fiber (SMF), to convert one-dimensional temperature data into a two-dimensional plane distribution, thereby realizing accurate monitoring of the temperature field inside the battery. Furthermore, by introducing a super-resolution algorithm based on the SRGAN model, the system can convert low-resolution temperature images into high-resolution images, reconstruct details, and improve spatial resolution and temporal resolution. The method of integrating physical sensing and super-resolution algorithms of the present invention can not only explore the mechanism of hot spot generation and verify the effectiveness of battery protection strategies, but also reduce the risk of thermal runaway, extend battery life, achieve more uniform lithium deposition, and improve battery cycle life, thereby providing strong technical support for the safe operation and performance optimization of lithium metal batteries. These technical features work together to greatly improve the safety and energy density of lithium metal batteries, laying a solid foundation for the development of sustainable energy applications.

[0046] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Schematic diagram of the OST-SRTM system of an embodiment of the present invention. (a) The structure of the LMB soft-pack battery cell and the position of the SMF in the battery cell. (b) The Rayleigh backscattering signal (RBS) along the optical fiber and the principle of 1D temperature position. (c) The conversion of temperature from one dimension to two dimensions. (d) The application of super-resolution algorithm in temperature distribution. (e) The dendrite growth process of the lithium anode (top) and the temperature map of the lithium electrode after 50 cycles (from left to right: bare lithium, pyramid-shaped lithium, copper mesh patterned lithium, polylactic acid PLA-lithium).

[0048] Figure 2 Schematic diagram of the experimental device for monitoring thermal events in the battery cell according to an embodiment of the present invention. (a) Experimental setup ① Battery test system for capacity and capacity readout, ② Infrared camera for external temperature data acquisition, ③ OFDR system for optical signal monitoring, ④ Laptop computer for all data acquisition and processing, ⑤ Scanning electron microscope (SEM) image for observing the anode morphology after cycling. (b) Relationship and role of optical, electrical and thermal signal processing. (c) Anode morphology simulation. (d) Lithium anode protection strategies: pyramid pattern, copper mesh pattern, PLA coating.

[0049] Figure 3 The schematic diagram of the super-resolution OFDR temperature data processing based on SRGAN in the embodiment of the present invention. (a) Dataset constructed by OFDR and infrared thermal imaging. (b) Structure of the SRGAN model. (c) Changes in verification loss, PSNR, and SSIM during training.

[0050] Figure 4 The results of verifying the effectiveness of the lithium anode protection strategy of the present invention are shown in Figure 1. (a) Rate performance, (b) Bare lithium, pyramid lithium, copper lithium and PLA-Li button cells at 1-5 mA cm -2 (c) Resistance of bare lithium and PLA-Li at different cycle numbers. (d) Cycling performance of bare lithium, pyramid lithium, copper mesh patterned lithium and PLA-Li soft pack batteries. (e) and (f) Super-resolution images and images of different cells at the 10th, 30th and retention cycles (48th, 56th, 60th, 70th, respectively). Box plot. (g) Bare Li, pyramid-shaped Li, Cu mesh patterned Li, and PLA-Li after 50 cycles SEM images (from top to bottom). (h) Radar images of different monitoring methods (① real-time, ② non-destructive, ③ precise, ④ equipment volume, ⑤ measurement area). DETAILED DESCRIPTION

[0051] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0052] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element. In addition, connection can be used for fixing as well as for coupling or communication.

[0053] It should be understood that the orientation or position relationship indicated by terms such as "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0054] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0055] See also Figures 1 to 3 The embodiment of the present invention provides an in-situ spatial thermal monitoring system for lithium metal batteries based on distributed optical fibers, including a single-mode optical fiber (SMF), an OFDR system, and a data processing unit. The single-mode optical fiber (SMF) is used to form a sensor and is connected to the inside of the battery. The single-mode optical fiber (SMF) has a spiral arrangement structure so that the optical fiber is distributed along a two-dimensional plane, and is used to realize temperature monitoring in physical space. The OFDR system is used to capture the temperature data of a one-dimensional continuous point set along the single-mode optical fiber (SMF). The OFDR system includes a linear tunable laser, an interferometer structure, a photodetector, and a data acquisition instrument. The linear tunable laser is used to emit an optical signal; the interferometer structure includes a reference arm and a measuring arm, which are used to measure the reflected light signal propagating along the optical fiber; the photodetector and the data acquisition instrument are used to collect optical signal data and convert it into electrical signal data. The data processing unit is used to convert the one-dimensional temperature data captured by the OFDR system into two-dimensional plane distribution data corresponding to the spiral arrangement structure of the single-mode optical fiber (SMF), thereby realizing accurate monitoring of the temperature distribution inside the battery.

[0056] In a preferred embodiment, the data processing module includes a super-resolution algorithm module for implementing super-resolution processing of temperature data and improving the spatial resolution of temperature monitoring. Figure 3 b, the super-resolution algorithm module includes: an SRGAN model for machine learning to achieve super-resolution of temperature mapping, the SRGAN model includes: a generator network composed of ResNet blocks for generating super-resolution images; a discriminator network, a CNN structure, for evaluating high-resolution (HR) and super-resolution (SR) images. Figure 3 As shown in b, in a preferred embodiment, the generator network includes a deep network consisting of five ResNet blocks, each ResNet block enhances information flow through skip connections to prevent gradient disappearance, wherein the convolution layer parameters contained in each ResNet block may be "n64k9s1p4", i.e., 64 convolution kernels of size 9×9, a step size of 1, and a padding of 4; the discriminator network is a convolutional neural network (CNN), which uses a leaky ReLU activation function (ρ=0.2) to avoid neuron death caused by negative output and ensure the effectiveness of network training; the output layer includes a dense block followed by a sigmoid function, which is used to perform binary scoring on high-resolution (HR) and super-resolution (SR) images to evaluate image quality.

[0057] In some embodiments, the system further comprises an external temperature experimental device for constructing a training data set, the device comprising: ceramic heating elements of different shapes, the elements being connected to a single-mode optical fiber (SMF) arranged in a spiral, for simulating the temperature distribution inside the battery; an infrared camera for collecting temperature data of the ceramic heating element to form a data set for training a super-resolution algorithm model; wherein the temperature of the ceramic heating element is controlled by a power supply, and the data set is used for machine learning to achieve super-resolution of temperature mapping. In a preferred embodiment, the temperature change video collected by the infrared camera is used to record the temperature distribution on the surface of the battery; frames are extracted from the video as images, which are synchronized with the data acquisition time of the OFDR system to form an original data set; wherein the original data set is processed by data enhancement technology to enhance the generalization ability of the model, and the data enhancement technology includes random rotation, reflection, translation, scaling, brightness and contrast adjustment, Gaussian blur, noise addition and cropping to avoid overfitting and improve the accuracy and robustness of the super-resolution algorithm.

[0058] In some embodiments, it also includes: a battery testing system for performing capacity and voltage readout of the battery to monitor the electrochemical performance of the battery; a computer (such as a laptop), which includes the data processing unit, for collecting and processing data from the battery testing system and the infrared camera, and managing data acquisition and analysis of the OFDR system; a scanning electron microscope (SEM) for observing the anode morphology after battery cycling to evaluate the physical state and structural changes of the battery; wherein the computer synchronizes the data of the OFDR system, the infrared camera and the battery testing system, and performs data analysis and image processing to ensure the accuracy and integrity of the experimental data.

[0059] In a preferred embodiment, the data processing unit identifies areas without temperature readings and fills the data of these areas with a preset room temperature value, such as 22° C., to ensure the integrity and continuity of the temperature distribution data.

[0060] In some embodiments, a lithium anode protection strategy is applied to the monitored lithium metal battery, the lithium anode protection strategy comprising:

[0061] (1) Pyramid patterned lithium anode, by forming a pyramid structure on the surface of the lithium anode to increase the surface area and improve the ion transport path, thereby improving battery performance. Specifically, in a preferred embodiment, the pyramid patterned lithium anode is formed by using a pyramid-shaped mold made by high-precision 3D printing technology to emboss a microstructure pattern on the lithium anode, the mold size is 50mm×50mm×0.5mm, the height of a single pyramid is 50μm, the width is 50μm, the spacing is 40μm, and a pressure of 2MPa is applied during the embossing process.

[0062] (2) Copper mesh patterned lithium anode, by creating micro patterns on the surface of the lithium anode to guide the uniform distribution and deposition of lithium ions using the micro patterns, the electrochemical performance can be enhanced. Specifically, in a preferred embodiment, the copper mesh patterned lithium anode uses a 250 mesh copper mesh to apply a pressure of 1 MPa to the lithium surface, and performs two presses to improve the uniformity of the surface modification, the first press in the direct direction, and the second press after rotating the grid 45 degrees.

[0063] (3) PLA-coated lithium anode, by applying a protective coating on the surface of the lithium anode to improve thermal stability by utilizing its chemical and mechanical barrier properties. Specifically, in a preferred embodiment, the PLA-coated lithium anode is prepared by dissolving PLA in a DMSO solution, stirring at 130°C for 5 hours and stirring at 80°C for 24 hours to obtain a uniform milky white solution, preparing a PLA solution with a mass ratio of 1.2%, and scraping the solution onto the surface of the lithium metal to form a PLA coating.

[0064] The effectiveness of the above lithium anode protection strategies can be evaluated by monitoring with the OST-SRTM system of the embodiment of the present invention.

[0065] An embodiment of the present invention also provides a method for in-situ spatial and temporal thermal monitoring of lithium metal batteries, using the distributed optical fiber-based in-situ spatial and temporal thermal monitoring system for lithium metal batteries.

[0066] The innovative in-situ spatial and temporal thermal monitoring and method of lithium metal batteries of the present invention can provide real-time, non-destructive, high-precision temperature monitoring for lithium metal batteries, cover a large measurement area, and have super-resolution capabilities. The system utilizes the high sensitivity and distributed characteristics of distributed fiber optic sensing technology (OFDR), combined with the design of the spiral arrangement structure of single-mode optical fiber (SMF), to convert one-dimensional temperature data into a two-dimensional plane distribution, thereby achieving accurate monitoring of the temperature field inside the battery. Furthermore, by introducing a super-resolution algorithm based on the SRGAN model, the system can convert low-resolution temperature images into high-resolution images, reconstruct details, and improve spatial and temporal resolution. The method of integrating physical sensing and super-resolution algorithms of the present invention can not only explore the mechanism of hot spot generation and verify the effectiveness of battery protection strategies, but also reduce the risk of thermal runaway, extend battery life, achieve more uniform lithium deposition, and improve battery cycle life, thereby providing strong technical support for the safe operation and performance optimization of lithium metal batteries. These technical features work together to greatly improve Lithium metal battery Safety and energy density lay a solid foundation for the development of sustainable energy applications Base.

[0067] The specific embodiments of the present invention are further described below.

[0068] A system integrating physical sensing and super-resolution algorithmic methods realizes operational spatiotemporal super-resolution thermal monitoring (OST-SRTM) of lithium metal batteries (LMBs). OFDR is a distributed fiber optic sensing technology based on Rayleigh backscattering (RBS) with a spatial resolution of several sub-millimeters. A helical structure ( Figure 1 ac), converting the one-dimensional (1D) continuous point set into discrete points distributed on a two-dimensional (2D) plane. Then, we use a super-resolution algorithm ( Figure 1 d). The lithium anode was patterned using pyramid imprinting and copper mesh, and an artificial solid electrolyte interface layer was formed by polylactic acid (PLA) coating ( Figure 1e). The effectiveness of these lithium anode protection strategies can be evaluated with high accuracy using the developed system, rather than disassembling the battery after cycling to employ destructive techniques such as scanning electron microscopy. By applying the protection strategies and our developed system, we observed extended battery life, lower temperature rise, more uniform temperature distribution, and fewer occurrences of hot spots during the battery capacity decay cycle. These all lead to more uniform lithium deposition, longer battery cycle life, and reduced risk of thermal runaway. This novel monitoring approach greatly improves the safety and energy density of LMBs for sustainable energy applications by combining state-of-the-art optical techniques with energy storage diagnostics and creating a powerful framework for evaluating thermal management technologies.

[0069] In order to study the temperature distribution and uneven lithium deposition inside the soft pack battery, the experimental setup for battery signal acquisition and processing is as follows: Figure 2 As shown in a and b. Li / LFP pouch cells were assembled in a dry room. Bare lithium, Li or PLA-Li (65×60mm 2 , thickness 100μm), separator and LFP cathode (65×60mm 2 , mass loading 400mg) are layered together. The SMF with mold is then bonded to the back of the copper current collector. Finally, after injecting the electrolyte, the entire battery is encapsulated with an aluminum-plastic film. The electrolyte is 1.0M LiPF6, EC / DMC / DEC (1:1:1, v:v). One end of the optical fiber extends from the battery and is connected to the OFDR system, while the other end is curled into the mold. During the cycling process, the battery is connected to the battery testing system. One end of the optical fiber is connected to the OFDR test system to monitor the internal temperature, and the external temperature is monitored by an infrared camera. In order to promote Li + The uniformity of deposition provides three lithium anode protection strategies ( Figure 2 d). The first two are based on surface patterns: (1) Pressing a pyramid-shaped mold into lithium: A pyramid-shaped mold was made by high-precision 3D printing technology. The mold design size is 50mm×50mm×0.5mm. The characteristics of a single pyramid on the mold are 50μm in height, 50μm in width, and 40μm in spacing between pyramids. In order to imprint the pyramid structure onto the lithium anode, a pressure of 2MPa was applied, thereby embedding a microstructure on the lithium surface. Pyramid pattern (2) Rolling the copper mesh to create the micro pattern: Use 250 meshThe copper mesh applies a pressure of 1 MPa to the lithium surface. In order to improve the uniformity of surface modification, two imprintings were performed: initially in the direct direction and then a second imprinting after rotating the grid 45 degrees. (3) Coating of polylactic acid: Dissolve an appropriate amount of PLA in DMSO solution, stir at 130°C for 5 hours to dissolve, and stir continuously at 80°C for 24 hours to obtain a uniform milky white solution. Prepare a PLA solution with a concentration of 1.2% (mass ratio) according to the content of PLA. Scrape the 1.2% PLA solution onto the lithium metal surface with a knife to obtain a lithium metal flake with a PLA coating. The OST-SRTM system is used to perform more precise spatiotemporal monitoring of different lithium anodes.

[0070] External temperature experiments were conducted by connecting ceramic heating elements of different shapes to helically arranged optical fibers. At the same time, infrared cameras were used to collect data to construct a training dataset. Using this dataset, we applied machine learning to achieve super-resolution temperature mapping. Figure 3 As shown in a, T-shaped, circular, and two-point ceramic heating elements are placed under the SMF. The temperature of the elements is controlled by a power supply. Temperature data is recorded simultaneously using an OFDR system and an infrared camera. The OFDR system captures data in a one-dimensional format and then processes it into a spiral structure. Areas without temperature readings are filled with room temperature values ​​(22°C). At the same time, the infrared camera records a video of the temperature changes in the same area. The video frames are framed into images and synchronized with the data acquisition time of the OFDR system to form the original dataset. In order to avoid overfitting due to the limited diversity of the dataset, we use data augmentation techniques. The images in the original dataset are transformed using random rotation, reflection, translation, scaling, brightness and contrast adjustment, Gaussian blur, noise addition, and cropping. These modifications generate a more diverse and robust training dataset. A network is built based on the SRGAN model, as shown in Figure 3 As shown in b. The generator network is a deep network consisting of 5 ResNet blocks, which is designed to enhance the information flow between layers through skip connections and prevent the gradient from vanishing as the depth of the network increases. For example, "n64k9s1p4" refers to 64 convolution kernels of size 9×9, stride 1, and padding 4. The discriminator network is a CNN in which Leaky ReLU is used as the activation function to avoid dead neurons from negative outputs. At the end of the network, a dense block is followed by a sigmoid function for binary classification to score high-resolution (HR) and super-resolution (SR) images. In the optimal model of the present invention, we achieved a PSNR of 25dB and a SSIM of 0.7 on the test set, successfully converting the one-dimensional temperature data of the OFDR system into a super-resolution two-dimensional temperature map.

[0071] The OST-SRTM system was used to verify the protection method. High-load commercial LiFePO4 was used as the cathode and paired with different lithium metal anodes to evaluate the protection effect during the entire cycle and monitor the temperature change. By comparing the cycle stability and capacity retention of different soft-pack batteries, it was found that PLA-Li had the best electrochemical performance. The response spectrum of SMF to temperature was tested to observe the internal structure of the battery. The temperature change trend of the battery can be measured and the electrochemical and temperature characteristics can be evaluated simultaneously. No. The capacity decreased at 48 cycles, while the cells treated with pyramid pattern, Cu pattern, and PLA showed capacity decreases at 56, 60, and 70 cycles ( Figure 4 The system we designed measures the spatiotemporal temperature maps of four batteries ( Figure 4 e, f). In the first 10 cycles, the capacity and performance of the battery remained stable, and no obvious hot spots were observed. The box plot combined with the scatter density plot illustrates the central tendency and dispersion of the temperature distribution of each electrode type after cycling. Bare lithium exhibited the widest temperature range (from -5°C to 8°C) and a relatively high median, indicating a significant temperature increase during cycling. The temperature distribution of the protected anode was more concentrated and narrower, indicating that the protection strategy can partially suppress the temperature change. As the cycle progressed, heat accumulation became obvious. The pyramid-type and Cu-type Li had a smaller temperature increase and stable temperature, and PLA-Li had the smallest temperature increase and the most stable distribution. In the capacity retention cycle, the protected anode showed a lower average temperature rise (3.1°C, 2.6°C, and 0.7°C) compared with bare lithium (5.3°C), and there were fewer hot spot areas in the later capacity degradation cycles. The scatter density plot of bare lithium showed large temperature fluctuations, indicating an uneven distribution and the presence of multiple hot spots. PLA-Li exhibited the best thermal management capabilities, making it suitable for applications requiring high thermal stability. After 100 cycles, cells that retained larger capacities showed more heat accumulation; eventually, the temperatures of all cells converged. This suggests that under normal cycling conditions, as the battery capacity decreases, the remaining capacity continues to affect the internal temperature. Nondestructive methods have been developed to monitor the state of batteries and can be divided into electrical, acoustic, and optical. Most electrical monitoring methods focus on single points and external indicators such as impedance, voltage, current, and thermal conductivity. Acoustic methods convert a single point into a two-dimensional representation by scanning. Optical methods have become the most promising technology for monitoring changes in distributed temperature and strain inside batteries due to their advantages such as anti-electromagnetic interference, high sensitivity, corrosion resistance, and multi-point monitoring capabilities. Compared with other optical sensors, the OST-SRTM system we developed has in-situ observation, two-dimensional space-time (1 frame / 3 seconds), and high resolution (16 points / cm 2 , super resolution up to 1551 points / cm 2 ) and other advantages.

[0072] In some alternative examples, optical fibers of different shapes may be used for data two-dimensionalization; Cu and pyramids with different parameters may be used for negative electrode protection.

[0073] In summary, the present invention proposes a lithium metal battery in-situ spatiotemporal thermal monitoring system and method, which effectively solves the challenge of distributed thermal sensing inside the battery. The design of the system includes two core parts: first, based on OFDR technology, a modeled helical structure optical fiber is designed, which not only reduces pressure but also expands the sensing range; second, the SRGAN super-resolution machine learning algorithm based on a self-built data set is developed, which significantly improves the sensing resolution.

[0074] The main innovative contributions of this invention include the following aspects: 1. A novel method based on spiral structure is proposed. Structured data 2 Dimensional processing technology enables the system to convert one-dimensional temperature data into two-dimensional plane 1. The distributed optical fiber technology is used to detect the temperature changes inside the battery more accurately. 2. A temperature sensing dataset based on helical structured optical fiber and heating ceramics was constructed, which provided a basis for the training and verification of super-resolution algorithms. 3. A super-resolution temperature monitoring machine learning algorithm based on SRGAN was implemented, which can improve the spatial resolution of temperature monitoring and provide a new technical means for battery thermal management. 4. Lithium negative electrode protection technologies based on Cu patterning, pyramid patterning, and PLA coating were developed. These technologies can effectively reduce the occurrence of hot spots during battery capacity decay and improve battery safety and performance. 5. The distributed optical fiber technology was applied to the temperature acquisition of lithium metal soft-pack batteries to achieve accurate monitoring of temperature distribution in time and space.

[0075] Thanks to the distributed monitoring and high sensitivity combined with OFDR technology, the two-dimensional data achieved by the spiral structure, and the super-resolution capability of the machine learning algorithm based on the SRGAN model, the embodiment of the present invention can perform real-time, non-destructive, and high-precision monitoring of the temperature distribution of the lithium anode, covering a large measurement area, and has super-resolution capability. The inventors found that the existence of local hot spots is a key factor leading to thermal runaway and affecting lithium deposition. The pyramid, copper mesh pattern and PLA strategy used in the embodiments of the present invention can effectively reduce the occurrence of these hot spots. Since even small temperature changes can have a significant impact on battery performance, the present invention is of great significance for the precise thermal management of lithium metal batteries. The present invention not only promotes the development of battery diagnostic technology, but also provides a solid foundation for the development of safer and more efficient energy storage solutions.

[0076] The above content is a further detailed description of the present invention in combination with specific / preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, without departing from the concept of the present invention, it can also make several substitutions or modifications to these described embodiments, and these substitutions or modifications should be regarded as belonging to the protection scope of the present invention. In the description of this specification, the description of the reference terms "an embodiment", "some embodiments", "preferred embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in combination with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In the absence of mutual contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples. Although the embodiments of the present invention and their advantages have been described in detail, it should be understood that various changes, substitutions and modifications can be made herein without departing from the scope of protection of the patent application.

Claims

1. A distributed optical fiber-based lithium metal battery in-situ temporal and spatial thermal monitoring system, characterized in that: include: A single-mode optical fiber (SMF), connected to the inside of the battery, wherein the single-mode optical fiber (SMF) has a spiral arrangement structure so that the optical fiber is distributed along a two-dimensional plane, and is used to realize temperature monitoring in a physical space; An OFDR system for capturing temperature data of a one-dimensional continuous set of points along the single mode optical fiber (SMF), the OFDR system comprising: A linear tunable laser for transmitting an optical signal; An interferometer structure, including a reference arm and a measuring arm, for measuring a reflected light signal propagating along the optical fiber; Photoelectric detector and data acquisition instrument, used for collecting optical signal data and converting it into electrical signal data; A data processing unit is used to convert the one-dimensional temperature data captured by the OFDR system into two-dimensional plane distribution data corresponding to the spiral arrangement structure of the single-mode optical fiber (SMF) to achieve accurate monitoring of the temperature distribution inside the battery.

2. The distributed optical fiber-based in-situ spatial-temporal thermal monitoring system for lithium metal batteries according to claim 1, characterized in that: The data processing module includes a super-resolution algorithm module, which is used to implement super-resolution processing of temperature data and improve the spatial resolution of temperature monitoring. The super-resolution algorithm module includes: The SRGAN model is used for machine learning to achieve super-resolution of temperature mapping. The SRGAN model includes: A generator network, preferably a ResNet block, for generating super-resolution images; Discriminator network, a CNN structure, is used to evaluate high-resolution and super-resolution images.

3. The distributed optical fiber-based in-situ spatial-temporal thermal monitoring system for lithium metal batteries according to claim 2, characterized in that: The generator network includes a deep network consisting of five ResNet blocks, each ResNet block enhances information flow through skip connections, wherein each ResNet block contains 64 convolution kernels of size 9×9, a step size of 1, and a padding of 4; the discriminator network is a convolutional neural network that uses a Leaky ReLU activation function; the output layer contains a dense block followed by a sigmoid function, which is used to perform binary scoring on high-resolution and super-resolution images to evaluate image quality.

4. The distributed optical fiber-based in-situ spatial-temporal thermal monitoring system for lithium metal batteries according to any one of claims 2 to 3, characterized in that: Further comprising an external temperature experimental apparatus for constructing a training data set, the apparatus comprising: Multiple ceramic heating elements connected to a single-mode optical fiber (SMF) arranged in a spiral to simulate the temperature distribution inside the battery; An infrared camera is used to collect temperature data of the ceramic heating element to form a data set for training a super-resolution algorithm model; wherein the temperature of the ceramic heating element is controlled by a power supply, and the data set is used for machine learning to achieve super-resolution of temperature mapping.

5. The distributed optical fiber-based in-situ spatial-temporal thermal monitoring system for lithium metal batteries according to claim 4, characterized in that: The temperature change video collected by the infrared camera is used to record the temperature distribution on the battery surface; frames are extracted from the video as images, which are synchronized with the data acquisition time of the OFDR system to form an original data set; wherein the original data set is processed by data enhancement technology, and the data enhancement technology includes random rotation, reflection, translation, scaling, brightness and contrast adjustment, Gaussian blur, noise addition and cropping.

6. The distributed optical fiber-based in-situ spatial-temporal thermal monitoring system for lithium metal batteries according to any one of claims 1 to 5, characterized in that: Also includes: Battery testing systems to perform capacity and voltage readouts of batteries to monitor the electrochemical performance of the batteries; A computer, including the data processing unit, for collecting and processing data from the battery testing system and the infrared camera, and managing data acquisition and analysis of the OFDR system; Scanning electron microscopy (SEM), used to observe the anode morphology after battery cycling to evaluate the physical state and structural changes of the battery; The computer synchronizes data from the OFDR system, infrared camera, and battery testing system, and performs data analysis and image processing.

7. The in-situ spatial-temporal thermal monitoring system for lithium metal batteries based on distributed optical fiber according to any one of claims 1 to 6, characterized in that: The data processing unit identifies areas without temperature readings and fills the data of these areas with a preset room temperature value, such as 22°C.

8. The distributed optical fiber-based in-situ spatial-temporal thermal monitoring system for lithium metal batteries according to any one of claims 1 to 7, characterized in that: Applying a lithium anode protection strategy to the monitored lithium metal battery, the lithium anode protection strategy comprising: Pyramid patterned lithium anode, which increases the surface area and improves the ion transport path by forming a pyramid structure on the surface of the lithium anode; Copper mesh patterned lithium anode, by creating micro patterns on the surface of lithium anode to guide the uniform distribution and deposition of lithium ions using micro patterns; PLA-coated lithium anodes, which improve thermal stability by applying a protective coating on the surface of lithium anodes to utilize their chemical and mechanical barrier properties; Monitoring was performed by the OST-SRTM system to evaluate the effectiveness of different lithium anode protection strategies.

9. The distributed optical fiber-based in-situ spatial-temporal thermal monitoring system for lithium metal batteries according to claim 8, characterized in that: The pyramid patterned lithium anode is a pyramid-shaped mold made by high-precision 3D printing technology, and a microstructure pattern is embossed on the lithium anode. The mold size is 50mm×50mm×0.5mm, and the height, width and spacing of a single pyramid are 50μm, 50μm and 40μm, and a pressure of 2MPa is applied during the embossing process; The copper mesh patterned lithium anode uses a 250-mesh copper mesh to apply a pressure of 1 MPa to the lithium surface, and performs two presses to improve the uniformity of surface modification, the first press in the direct direction, and the second press after rotating the mesh 45 degrees; The PLA-coated lithium anode is prepared by dissolving PLA in a DMSO solution, stirring at 130° C. for 5 hours and stirring at 80° C. for 24 hours to obtain a uniform milky white solution, and then preparing a PLA solution with a mass ratio of 1.2%. The solution is then scraped onto the surface of the lithium metal to form a PLA coating.

10. A method for in-situ spatial thermal monitoring of a lithium metal battery, characterized in that: An in-situ spatial and temporal thermal monitoring system for lithium metal batteries based on distributed optical fiber as described in any one of claims 1 to 9 is used.

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