Solid-state battery and method of designing and manufacturing the same

Abstract

This invention provides a method for designing and manufacturing an all-solid-state battery. A set of input data is provided to generate a simulation chamber containing electrode active material particles and solid electrolyte particles in a randomly selected discrete space. A compression simulation chamber is then generated such that each particle is in contact with at least one adjacent particle. The compression simulation chamber data is processed to obtain the relative tortuosity of the SE particles. The above steps are repeated to prepare a database containing multiple sets of input parameters and their corresponding relative tortuosities. A set of target input parameters is selected from the database, and an all-solid-state lithium battery is fabricated accordingly.

Description

[0001] Cross-references to related applications

[0002] This application claims the priority of U.S. Patent Application No. 63 / 554,110, filed February 15, 2024, and U.S. Patent Application No. 19 / 048,171, filed February 7, 2025, the entire disclosure of which is incorporated herein by reference. Technical Field

[0003] This invention relates to solid-state batteries and methods for manufacturing solid-state batteries. Background Technology

[0004] Secondary batteries

[0005] Rechargeable batteries have gradually become an ideal power source for various electronic devices, such as automobiles, computers, mobile phones, tools, electric scooters, bicycles, electric vehicles, energy storage systems, drones, and other equipment. Among rechargeable batteries, lithium-ion batteries have attracted much attention due to their ability to achieve a favorable balance between voltage and energy density. In addition to their performance advantages, lithium-ion rechargeable batteries can also contribute to addressing climate change by enabling transportation electrification and promoting the integrated use of renewable energy. These batteries help reduce greenhouse gas emissions by powering electric vehicles and storing electricity generated from intermittent renewable energy sources such as solar and wind power. Furthermore, the long cycle life and high energy density of lithium-ion batteries can support the development of smart grids and distributed energy systems, potentially improving overall energy efficiency and reducing dependence on fossil fuels. Previously, lithium rechargeable batteries contained liquid electrolytes, which typically contained lithium salts dissolved in organic solvents. However, there is growing interest in developing all-solid-state lithium rechargeable batteries as an alternative to conventional liquid electrolyte systems. All-solid-state batteries have potential advantages in terms of safety, stability, and energy density. Despite these potential benefits, the development of practical all-solid-state lithium rechargeable batteries still faces several significant challenges.

[0006] Challenges of all-solid-state rechargeable batteries

[0007] One challenge in all-solid-state battery design is achieving and maintaining a sufficient lithium-ion diffusion coefficient within the solid electrolyte material. Furthermore, during battery discharge and charging, some components (such as electrodes) may undergo volume changes (e.g., expansion and contraction). These volume changes can generate mechanical stress or lead to contact failures between components within the battery structure. Contact failures between battery components result in degraded charge-discharge performance and overall battery capacity decay. Researchers and engineers in the energy storage field are actively working to address these challenges. Their efforts primarily focus on developing novel materials and battery designs that achieve favorable lithium-ion diffusion coefficients while also accommodating the mechanical stresses associated with battery cycling. Improving the interfacial stability within all-solid-state batteries remains an important research direction. Overcoming the current limitations of all-solid-state battery systems holds promise for significantly improving energy storage capabilities across a wide range of applications.

[0008] Since lithium metal or lithium alloys can be used as cathode materials, the advantage lies in the significant improvement in energy density relative to the battery's mass and volume. However, although the theoretical energy density of all-solid-state batteries is expected to be 10% to 20% higher than that of existing lithium-ion batteries, various challenges remain in the actual manufacture of such batteries. Specifically, the cathode typically comprises a cathode active material, a solid electrolyte, a conductive material, and a binder. To increase capacity, the contact area between the solid electrolyte and the cathode active material must be increased, primarily to maximize lithium-ion migration. To achieve this, research must first be conducted to optimize the particle size and ratio of the cathode active material and the solid electrolyte. While the optimal anode structure can be obtained experimentally, there are limitations to finding all possible combinations through experimentation.

[0009] Do not acknowledge existing technology

[0010] The discussions in this section are intended to provide background information in relation to the present invention and do not constitute an admission of prior art. Summary of the Invention

[0011] Technical issues

[0012] This invention provides a method for optimizing electrode structures through modeling and manufacturing all-solid-state batteries accordingly.

[0013] Technical solution

[0014] One aspect of the present invention provides a method for manufacturing an all-solid-state lithium battery. First, a set of input parameters is provided, including: the weight percentage (SE_wt%) and density (SE_density) of a solid electrolyte material comprising spherical solid electrolyte (SE) particles, and the weight percentage (EAM_wt%) and density (EAM_density) of an electrode active material comprising spherical electrode active material (EAM) particles.

[0015] Then, the volume percentages of EAM (EAM_vol) and solid electrolyte (SE_vol) are provided as follows: EAM_vol = (EAM_wt% / EAM_density) / (EAM_wt% / EAM_density + SE_wt% / SE_density), SE_vol = (SE_wt% / SE_density) / (EAM_wt% / EAM_density + SE_wt% / SE_density). The probability value for EAM is provided as follows: (EAM_vol / EAM_pvol) / (EAM_vol / EAM_pvol + SE_vol / SE_pvol), where EAM_pvol is the average volume of spherical EAM particles, and SE_pvol is the average volume of spherical SE particles; this probability value represents the number of spherical SE particles corresponding to each spherical EAM particle.

[0016] Furthermore, simulation box data representing the discrete spaces divided by the probability values ​​of EAM particles is generated using simulation box data. Each simulation box contains spherical EAM particles and spherical SE particles in a randomly selected discrete space, and the simulation box data indicates whether each discrete space is occupied by one of the spherical EAM particles or one of the spherical SE particles. The simulation box data is then processed to generate compressed simulation box data, which represents simulation boxes compressed along the vertical axis in the up-down direction but not compressed in any other direction, such that after compression, each spherical EAM particle and spherical SE particle is in contact with at least one adjacent particle. Further processing of the compressed simulation box data generates adjacency matrix data representing connected spherical SE particles, where each connected spherical SE particle is in contact with or overlaps with at least one adjacent spherical SE particle.

[0017] The relative tortuosity of the spherical SE particles inside the compression simulation chamber is obtained as follows: Adjacency matrix data is processed to generate path data, which determines paths roughly from top to bottom, passing through the centers of connected spherical SE particles, with each path extending from the top-connected spherical SE particle to the bottom-connected spherical SE particle; the path data is processed to determine and store target path data representing the target path; length data is extracted, representing the target path length from the center of the top-connected spherical SE particle on the target path to the center of the bottom-connected spherical SE particle on the target path; Euclidean distance data is extracted, representing the Euclidean distance between the top-connected and bottom-connected spherical SE particles in the target path; and the target path length and Euclidean distance are processed to obtain the relative tortuosity.

[0018] Repeat the above steps to prepare a database containing multiple sets of input parameters and their corresponding relative tortuosities. Using the corresponding relative tortuosities, select the input parameters for the target set from the database.

[0019] Then, all-solid-state lithium batteries were fabricated using the input parameters of the target group.

[0020] In some implementations, a set of input parameters may also include one or more of the following: particle size information of spherical EAM particles, particle size information of spherical SE particles, weight percentage and density of carbon material, and weight percentage and density of binder.

[0021] In some embodiments, the all-solid-state lithium battery includes a first electrode, a second electrode, and an SE layer; the fabrication steps of the all-solid-state lithium battery include: fabricating the first electrode using input parameters of a target group; providing the second electrode; providing carbon material and binder using input parameters of the target group; and fabricating the SE layer using input parameters of the target group.

[0022] In some implementations, the particle size information of the spherical EAM particles includes at least one of the average diameter or particle size distribution of the spherical EAM particles.

[0023] In some implementations, the particle size information of the spherical SE particles includes at least one of the average diameter or particle size distribution of the spherical SE particles.

[0024] In some implementations, a set of input parameters includes the particle size distribution of spherical EAM particles and the particle size distribution of spherical SE particles.

[0025] In some implementations, dimensional data representing the size of spherical EAM particles and spherical SE particles in the simulation chamber is generated using the particle size distribution of spherical EAM particles and spherical SE particles.

[0026] In some implementations, the particle size distribution of spherical EAM particles and the particle size distribution of spherical SE particles are each selected from a group consisting of a normal distribution, a log-normal distribution, a skewed normal distribution, a skewed distribution, and a bimodal distribution.

[0027] In some implementations, the adjacency matrix data represents an adjacency matrix, wherein the adjacency matrix is ​​a Z×Z matrix, and Z is the number of spherical SE particles; for every two adjacent spherical SE particles that are in contact or overlap, the adjacency matrix data has a value of "1" in the adjacency matrix, wherein the sum of the radii of the two adjacent spherical SE particles that are in contact or overlap is greater than or equal to the overlap criterion; and for every two adjacent spherical SE particles that are not in contact or overlap, the adjacency matrix data has a value of "1" in the adjacency matrix, wherein the sum of the radii of the two adjacent spherical SE particles that are not in contact or overlap is less than the overlap criterion.

[0028] In some implementations, the overlap criterion is 100% to 105% of the Euclidean distance between two adjacent spherical SE particles that are in contact or overlap.

[0029] In some implementations, the overlap criterion is 102% of the Euclidean distance between two adjacent spherical SE particles that are in contact or overlap.

[0030] In some implementations, the set of input parameters also includes additional input parameters selected from the group consisting of: at least one of the Young's modulus, Poisson's ratio, coefficient of friction, or coefficient of restitution of the SE material; at least one of the Young's modulus, Poisson's ratio, coefficient of friction, or coefficient of restitution of the EAM; the size of the simulation chamber; the number of particles on each side of the simulation chamber; and combinations of the above parameters.

[0031] In some implementations, the process of processing the simulation chamber data to generate compressed simulation chamber data includes processing pressure data representing a predetermined pressure applied to the simulation chamber.

[0032] In some implementations, the total force applied to the simulation chamber is calculated by multiplying a predetermined pressure by the cross-sectional area of ​​the simulation chamber.

[0033] In some implementations, the force on each particle is calculated as follows: the total force is decomposed into a first force acting on all spherical EAM particles and a second force acting on all spherical SE particles as follows: first force = total force × EAM_vol, second force = total force × SE_vol; the first force is divided by the total number of spherical EAM particles; and the second force is divided by the total number of spherical SE particles.

[0034] In some implementations, EAM is the positive electrode active material.

[0035] In some implementations, at least one of the porosity of the first electrode, EAM utilization, or SE utilization is calculated.

[0036] In some implementations, EAM utilization is calculated as follows: processing adjacency matrix data to generate basic SE data representing connected spherical SE particles of the foundation, wherein each connected spherical SE particle of the foundation is in contact with or overlaps with an adjacent connected spherical SE particle of the foundation, and at least one connected spherical SE particle of the foundation is in contact with the bottom of the simulation chamber; processing adjacency matrix data to generate EAM data representing connected spherical EAM particles, wherein each connected spherical EAM particle is in contact with at least one connected spherical SE particle of the foundation; processing EAM data to generate first volume data representing the total volume of the connected spherical EAM particles; processing simulation chamber data to generate second volume data representing the total volume of all spherical EAM particles; and dividing the total volume of the connected spherical EAM particles by the total volume of all spherical EAM particles.

[0037] In some embodiments, the porosity of the first electrode is calculated by multiplying the predetermined height of the simulation chamber by the cross-sectional area of ​​the simulation chamber to obtain the total volume of the first electrode; by adding the volume of the EAM to the volume of the SE material to obtain the total volume of the solid in the first electrode; by subtracting the total volume of the solid from the total volume of the first electrode to obtain the void volume; and by dividing the void volume by the total volume of the first electrode.

[0038] Another aspect of the invention provides an all-solid-state battery manufactured by the method provided herein. Yet another aspect of the invention provides an electric vehicle comprising the all-solid-state battery provided herein.

[0039] Exemplary Implementation

[0040] These and other features of the present invention will be understood through the following detailed description and will become more apparent through exemplary embodiments of the invention. Furthermore, those skilled in the art will readily understand that the objects and advantages of the present invention can be achieved through the technical means and combinations thereof described in the appended claims.

[0041] The overview is not restrictive.

[0042] It should be understood that the present invention is not limited to the examples outlined in the invention description. Several other aspects will also be described and illustrated herein. Attached Figure Description

[0043] Figure 1 A flowchart of the code script during the simulation process of one embodiment of the present invention is shown.

[0044] Figure 2The simulation box generated during the preprocessing stage of one embodiment of the present invention is shown.

[0045] Figure 3 An exemplary particle size distribution of a solid electrolyte is shown.

[0046] Figure 4 The diagram illustrates a compression simulation chamber generated during the processing stage of one embodiment of the invention.

[0047] Figure 5 Connected solid electrolyte (SE) particles are shown.

[0048] Figure 6 The diagram shows connected SE particles and connected positive electrode / electrode active material (CAM or EAM) particles.

[0049] Figure 7 Different paths of connected SE particles are shown.

[0050] Figure 8 The SE path in three-dimensional space is shown.

[0051] Figure 9 Exemplary pellet cells with different simulation scenarios are shown.

[0052] Figures 10A to 12B Examples of various particle size distributions are shown. Figure 10A , Figure 11A and Figure 12A ) and the corresponding simulation box obtained ( Figure 10B , Figure 11B and Figure 12B ).

[0053] Figure 13 This is an example of a solid-state battery implementation method.

[0054] Figures 14A to 14C The simulation box is shown. Detailed Implementation

[0055] Examples and Implementation

[0056] The subject matter of the invention will be described and discussed in more detail with reference to the accompanying drawings and specific embodiments and examples, wherein some, but not all, embodiments of the invention are shown. Throughout the text, the same numerals refer to the same elements or components unless otherwise stated. The subject matter of the invention can be implemented in many different forms and should not be construed as limited to the specific embodiments set forth herein; rather, these embodiments are provided to enable the invention to meet applicable legal requirements. In fact, those skilled in the art to which the subject matter of the invention pertains will conceive of many modifications and other embodiments of the subject matter of the invention. Therefore, it should be understood that the subject matter of the invention is not limited to the specific embodiments disclosed, and modifications and other embodiments are intended to be included within the scope of the appended claims.

[0057] definition

[0058] "One", "one", and "the (that)"

[0059] As used herein, the singular form of a word includes the plural meaning unless the context clearly indicates otherwise. The plural includes the singular form, and vice versa. Therefore, the terms “a,” “an,” and “the” generally include the plural form of the corresponding term. For example, although the invention is described using terms such as “a layer,” “a substrate,” “a battery,” etc., more than one of the aforementioned components and other components (including combinations thereof) may be used.

[0060] "about"

[0061] The term “about” indicates and covers the value indicated and the range above or below that value.

[0062] "Including", "Basically composed of", and "Constituted of"

[0063] The term "comprising" should be interpreted inclusively, not exclusively. Similarly, the terms "including," "containing," and "or" should also be interpreted inclusively unless the context explicitly prohibits such interpretation. The disclosure of embodiments defined by the term "comprising" also includes the disclosure of embodiments "consisting of" and "composed of" the disclosed components. The phrase "consisting of" excludes any unstated elements, steps, or components.

[0064] "and / or"

[0065] The term “and / or” used in the context of “X and / or Y” should be interpreted as “X, Y or X and Y”.

[0066] "Up" and "above"

[0067] As used herein, the terms “on,” “coated on,” “formed on,” “deposited on,” “set on,” etc., indicate coating, forming, covering, depositing, or setting in contact with a surface below or above. On the other hand, the terms “above,” “coated above,” “formed above,” “deposited above,” “set above,” etc., indicate coating, forming, covering, depositing, or setting on or above a surface, but not necessarily in contact with that surface. For example, a layer formed “coated” “above” a substrate layer can be in direct contact with the substrate without intermediate materials; however, this phrase does not preclude the presence of one or more other layers of the same or different composition between the formed layer and the substrate layer.

[0068] Markush Group

[0069] As used herein, the term "the combination thereof" in any Markush-type representation refers to a combination or mixture of one or more elements selected from the group of elements disclosed in the Markush-type representation, and means that one or more elements selected from that group exist. The term "the combination thereof" includes all possible combinations of the elements referred to by the term.

[0070] "between"

[0071] As used in this article, the expression “between” includes endpoint values.

[0072] Numerical range

[0073] Furthermore, all numerical ranges herein should be understood to include all integers or fractions within that range. Moreover, any numerical range referenced herein is intended to encompass all subranges falling within that range, and these numerical ranges should be interpreted as supporting claims for any numerical value or subset of numerical values ​​within that range. For example, the disclosure of "1 to 10" should be understood to support ranges such as "1 to 8", "3 to 7", "1 to 9", "3.6 to 4.6", "3.5 to 9.9", etc. When ranges are given, any endpoint values ​​of these ranges and / or any numerical value within the range may be combined with the ranges of this invention.

[0074] "Including", "for example", and "such as"

[0075] As used herein, terms such as “including,” “for example,” and “like” mean “including / for example / like but not limited to.”

[0076] Combination of implementation methods

[0077] As used herein, the term "example" (especially when listed below) is illustrative only and should not be considered exclusive or comprehensive. Any implementation disclosed herein may be combined with any other implementation disclosed herein unless expressly stated otherwise.

[0078] Particle size

[0079] As used herein, particle size refers to the average particle size (D) measured by a microscope (e.g., optical microscope, electron microscope, scanning electron microscope (SEM), transmission electron microscope (TEM), atomic force microscope (AFM), confocal microscope, X-ray microscope, cryo-electron microscope, Raman microscope, or fluorescence microscope). 50 Particle size can be the diameter of spherical particles, or the length of ellipsoidal or other irregularly shaped particles in the direction of maximum size. As used herein, “D50” refers to the particle size at which 50% of the particles have a smaller diameter.

[0080] Overview of the simulation process

[0081] Methods of designing and manufacturing batteries through simulation

[0082] This invention provides a method for designing and manufacturing all-solid-state batteries by using discrete element modeling and LAMMPS software to simulate the behavior of all-solid-state cell-type batteries.

[0083] Discrete Element Modeling

[0084] Discrete element modeling (DEM) is a numerical technique for simulating and analyzing the behavior of systems composed of discrete, interacting particles. DEM treats each particle as an individual entity whose interactions follow physical laws. In DEM, particles can be represented as spheres, ellipsoids, or custom shapes; however, in the method presented in this paper, particles are represented as spheres. Each particle possesses its own physical properties, such as size, density, and stiffness. Particle-particle and particle-boundary interactions are modeled using contact mechanics, which considers forces such as normal and tangential forces, friction, cohesion, and damping. Newton's second law of motion is applied to each particle to calculate its time-varying acceleration, velocity, and position. Contact models (e.g., the Hertz-Mindlin model) define how mechanical interactions occur between particles. The simulation uses explicit time integration stepwise ("timestepping") to solve the equations of motion. DEM captures detailed particle-scale behavior, enabling modeling of complex particle shapes and interactions, and allowing the study of dynamic phenomena such as breakup, separation, and mixing. However, DEM calculations are computationally intensive, require careful calibration of material and interaction parameters, and are sensitive to time step size and numerical stability issues.

[0085] LAMMPS software

[0086] LAMMPS (Large-scale Atomic / Molecular Massively Parallel Simulator) is an open-source molecular dynamics (MD) simulation software. This versatile software can simulate a wide variety of physical systems, including atomic, molecular, particulate, and mesoscopic systems. Due to its efficient parallel computing capabilities, LAMMPS is particularly suitable for large-scale simulations. LAMMPS can model atomic-scale systems (e.g., metals, polymers, biomolecules) and mesoscopic or particulate systems (e.g., powders, colloids). It is compatible with various potentials (e.g., Lennard-Jones, EAM, Tersoff, harmonic, and particle interaction models). LAMMPS has dedicated DEM packages (e.g., the GRANULAR package) that enable the simulation of particulate systems. The software supports frictional, cohesive, and non-cohesive particle contact models. LAMMPS itself does not include visualization tools, but its output data is compatible with tools such as OVITO, VMD, and Paraview for post-processing.

[0087] Script file for simulation process

[0088] like Figure 1As shown, the simulation process begins with generating particle positions, types, and sizes, which can be achieved using a Python script named "main.py". This script stores the generated data in a file named "write.txt". Additionally, "main.py" generates a LAMMPS input script "in.pour", which defines the simulation type, specifies the interparticle forces to be applied, and lists other simulation parameters. Another, simpler Python script is used to prepare the .pbs job files required for submission to the supercomputer. A separate script, "run.py", automatically submits multiple jobs simultaneously (e.g., approximately 60 jobs), enabling efficient management of the computational workload. After the required files are transferred to the supercomputer, the "run.py" script is executed from the terminal. This script submits the job files to the supercomputer and instructs the supercomputer to run the corresponding LAMMPS simulation. The results are stored in a file named "dump.pour", which contains particle data for each time step, including normalized positions in the range of 0 to 1. After the simulation is complete, the data is post-processed. The last time step of the simulation data is extracted from the "dump.pour" file. The normalized particle positions are then converted into actual distances measured in micrometers for subsequent calculations. This conversion allows us to determine whether two particles are in contact by comparing the distance between their centers to the sum of their radii. Two main post-processing scripts are used: "Tortcalc.py" calculates tortuosity by identifying the shortest or intermediate path from the top to the bottom of the system, and "Procedit.py" calculates porosity and the percentage of CAM (cathode active material) or EAM (electrode active material) particles connected to the solid electrolyte network. CAM is used below as a general example of EAM. It should be understood that the methods presented herein can be used for any type of EAM or general electrode material.

[0089] Multiple simulations

[0090] To account for random variations in particle size distribution, multiple simulations are performed using different random number seeds, and the results are averaged. This process allows for more accurate prediction of the overall behavior of the sheet-fed solar cell. The method also allows for adjustment of the particle size distribution, which is crucial for evaluating performance metrics such as CAM utilization, tortuosity, and porosity. These metrics can also be used to estimate the ionic conductivity in the simulated electrode system.

[0091] Target CAM content

[0092] The target CAM content in the electrode can be initially set to a predetermined value (e.g., 85 wt%). Although the assumption of "uniform CAM particle size" is not entirely accurate, differences in particle size can be compensated for by balancing larger and smaller particles. The preprocessing stage involves iterating the model until the preprocessed weight is approximately 85%. During these iterations, the target weight needs to be adjusted until the final CAM content stabilizes within the target range of 84-86 wt%.

[0093] Asymmetric particle size distribution

[0094] However, when the particle size follows a skewed normal distribution, the particle size distribution is asymmetric. For example, the 75th and 25th percentile particle size distributions are asymmetrical. This introduces additional variability into the system. The particle packing process during the processing stage is less regular than in the case of a symmetric distribution, leading to greater uncertainty and variability in the final result. Through repeated adjustments and fine-tuning, and multiple iterations of the system, the final weight distribution can be made to approach 85% CAM.

[0095] Provide framework

[0096] The results of each simulation iteration are stored and analyzed to track changes in CAM content before and after processing. These results demonstrate the behavior of various combinations of particle size distributions and CAM content. Although achieving complete consistency within, for example, the 84–86% range is challenging, this method ultimately provides a reliable framework for simulating and optimizing the composition of all-solid-state battery electrodes.

[0097] Improved modeling

[0098] Furthermore, the modeling method for this sheet-fed battery accurately reflects real-world conditions. Electrode dimensions are modeled on a larger scale (100 μm in height), and the size ratio of the positive electrode active material to the solid electrolyte is more than twice that of previous studies. This improved modeling method more accurately simulates the behavior of actual materials used in all-solid-state battery development. Comparing these simulation results with experimental data further validates the model and provides insights into how to adjust the electrode composition to maximize energy density. This information can then be used to design and manufacture the battery.

[0099] Preprocessing stage

[0100] Preprocessing Overview

[0101] The preprocessing step generates a digital characterization of the electrode using randomly mixed materials, such as... Figure 2 As shown. This process determines the number and size of particles for each material, constructing a digital simulation model of the randomly mixed materials, i.e., a simulation chamber containing the mixed materials, such as... Figure 2 As shown. In addition, key parameters such as force and effective properties are calculated and passed to subsequent processing stages.

[0102] Input parameters

[0103] The preprocessing input parameters include two categories: material properties and simulation parameters. For all materials, including solid electrolytes (SE), cathode active materials (CAM), conductive materials (CM), and binders, the required properties are density and weight percentage. Specifically, for SE and CAM, particle size distribution is characterized by parameters such as D5, D50, and D95, distribution types such as normal distribution, log-normal distribution, skewed skewed normal distribution, bimodal distribution, and discretization. Additional mechanical properties for SE and CAM include Young's modulus, Poisson's ratio, coefficient of friction, and coefficient of restitution. Simulation parameters include the size of the simulation chamber, the degree of discretization (defined as the number of particles on each side of the simulation chamber), and the applied pressure.

[0104] Discretization

[0105] Discretization of the simulation box refers to dividing the simulation domain into smaller, more manageable subdomains (cells or meshes) to efficiently perform computations (e.g., tracking particles, calculating forces, solving equations, etc.). This technique is widely used in DEM and MD. In particle-based methods such as DEM or MD, discretization can help identify adjacent particles more efficiently. Dividing the simulation box into smaller subdomains (e.g., bins or voxels) only requires examining particle interactions within the same or adjacent subdomains. Subdomains enable localized calculations of forces or properties (e.g., density, stress, pressure), reducing computational costs. In parallel simulations, subdomains can be assigned to different processors, allowing for efficient use of computational resources. The simulation box can be divided into uniform cubic or rectangular cells of equal size. In LAMMPS, the simulation box is divided into uniform cells for efficient calculation of particle interactions. Each particle is assigned to a single mesh cell. Only particle interactions within the same or adjacent cells are considered.

[0106] Preprocessing methods

[0107] Preprocessing begins by calculating the volumes of CAM and SE based on their respective densities and the input weight percentages. Using the calculated volumes, a random number generator initialized with a predetermined seed value is used to determine whether each particle should be assigned to CAM or SE. This assignment is based on the CAM / SE volume ratio and the average particle size (D50) of each material. Subsequently, a second random number generator, combined with the cumulative distribution function of the predetermined particle size distribution, is used to determine the size of each particle. For skewed normal or other asymmetric particle size distributions, particle size determination involves more complex methods. Specifically, an iterative guess-and-check method is used to solve the inverse problem, ensuring that the material distribution conforms to the target CAM weight percentage.

[0108] Random number generator

[0109] Random number generators (RNGs) are tools or algorithms used to generate sequences of numbers with no apparent pattern. These numbers can be truly random (based on physical phenomena) or pseudo-random (calculated using deterministic algorithms). RNGs are indispensable in simulation, statistical sampling, cryptography, machine learning, and various numerical techniques. True random number generators (TRNGs) utilize physical processes (such as radioactive decay or thermal noise) to generate randomness. Pseudo-random number generators (PRNGs) use deterministic algorithms to generate seemingly random sequences of numbers. A seed value is required to initialize the sequence; quasi-random number generators generate low-biased sequences for specific applications (such as Monte Carlo integration). In LAMMPS, RNGs are used to initialize particle position, velocity, or other stochastic processes. The seed value determines the repeatability of the simulation results.

[0110] Output

[0111] The output of the preprocessing step is a digital simulation of the material and a randomly mixed structure. Furthermore, the process provides calculated parameters such as forces and effective properties, which are subsequently used in later simulation or processing stages.

[0112] Discretized mesh

[0113] like Figure 2 As shown, the simulation chamber can contain tens of thousands of particles arranged in a discretized grid of predetermined size. This grid not only facilitates the correct distribution of particles but also introduces randomness into the initial positions of the particles, thereby simulating the random mixing of materials in a real-world scenario. The process employs a simplified Monte Carlo method using an RNG. Each grid space is processed individually using the RNG to determine the particle type (e.g., CAM or SE). A second random number generator assigns particle sizes based on a predetermined particle size distribution.

[0114] Monte Carlo Method

[0115] The Monte Carlo method is a computational approach that relies on repeated random sampling to solve problems that are, in principle, deterministic. It is particularly suitable for problems involving uncertainty, complex integrals, or multi-degree-of-freedom systems. This method uses random numbers or stochastic processes to explore possible outcomes. Statistical analysis is then used to output the results to estimate the final conclusion. A simplified Monte Carlo method extracts the core principles: random sampling and averaging. First, the problem to be estimated is defined. Then, an RNG is used to generate input values ​​within the desired range or domain. The function value corresponding to each random input is calculated. Finally, the result is estimated using the average of the calculated values.

[0116] Example

[0117] For example, if you want CAM to be 85% by weight, you would analyze the density and composition of the mixture (including CAM, SE, CM, and binder), resulting in approximately 70% CAM volume. Calculate an intermediate value to correlate the CAM volume percentage with the CAM average particle size distribution and the SE average particle size distribution. For example, the intermediate value could be denoted as "probability_CAM", which equals: (CAM_vol / CAM_pvol) / (CAM_vol / CAM_pvol + SE_vol / SE_pvol). For general electrode active materials (EAM), the intermediate value "probability_EAM" equals (EAM_vol / EAM_pvol) / (EAM_vol / EAM_pvol + SE_vol / SE_pvol). CAM volume (denoted as CAM_vol) or EAM volume (denoted as EAM_vol) represents the proportion of CAM or EAM volume to the total solid volume excluding pores. This is directly derived from the target CAM or EAM weight percentage and the known densities of the individual materials in the mixture. CAM_pvol or EAM_pvol refers to the average volume of spherical CAM or EAM particles, and SE_pvol refers to the average volume of spherical SE particles. This probability value represents the number of spherical SE particles corresponding to each spherical CAM or EAM particle.

[0118] Volume calculation

[0119] The calculation of CAM_vol begins by dividing the target CAM weight by its density: CAM_vol = (CAM_wt% / CAM_density) / (CAM_wt% / CAM_density + SE_wt% / SE_density). For general EAM, EAM_vol = (EAM_wt% / EAM_density) / (EAM_wt% / EAM_density + SE_wt% / SE_density). The above calculation provides the volume of the CAM or EAM, which is then normalized relative to the total solid volume (CAM_vol + SE_vol) or (EAM_vol + SE_vol). The expected particle volume (CAM_pvol or EAM_pvol) of the CAM or EAM is based on the mathematical expectation of the particle size distribution of the CAM or EAM. For example, for a symmetrical distribution (e.g., a normal distribution), this is equivalent to the volume of a single particle, using Equation 4 / 3ðr. 3 (where r is the average particle radius, i.e., D50) is calculated.

[0120] Particle distribution

[0121] Considering the size difference between CAM and SE particles, to achieve 70% CAM by volume, the probability of assigning a particle as CAM is approximately 3.17%. This means that each CAM particle corresponds to approximately 30 SE particles. Mesh filling is performed iteratively, processing each point in the discrete space, starting from (0,0,0) and systematically progressing in the order of (0,0,1), (0,1,0), (1,0,0), etc., until the entire simulation box is filled.

[0122] Particle size distribution definition

[0123] The second iteration of this process refines the particle size distribution. Instead of using a single particle type boundary, multiple boundaries are introduced to correspond to discrete particle size ranges. For example, as... Figure 3 As shown, if CAM and SE each have 10 discrete particle sizes, then 9 boundaries must be set for each type, and the particle size distribution determines the critical values ​​of these boundaries. At the end of the process, the simulation chamber is filled with tens of thousands to hundreds of thousands of particles, each assigned a specific size and type. The total volume of CAM and SE within the simulation chamber is then calculated. If the final CAM volume percentage deviates from the expected 70% ± 1%, the entire process is repeated. Due to the inherent randomness of random number generation, multiple iterations will eventually converge to the target particle distribution.

[0124] Determine the critical value or boundary

[0125] Simulation setup involves determining the critical values ​​or boundaries of the discrete particle size distribution. In standard scenarios such as dice rolling, the probability is uniformly distributed across all results, but in the case of this simulation, a Gaussian (bell curve) distribution is used, requiring adjustments to the positions of these boundaries to accurately reflect the problem. Discretizing the continuous distribution into intervals optimizes the number of intervals to balance performance and accuracy. For example, 10 intervals were found to achieve optimal performance, providing a good balance between computation time and accuracy. A larger number of intervals does not significantly improve results but increases computation time due to the greater number of particle combinations.

[0126] Simulated boundary

[0127] The simulated boundaries in the x and y directions were set to selected sizes, such as 35 micrometers, to reduce computation time. Initially, larger boundaries (e.g., 150 micrometers) were used, but the results were similar, and smaller boundary sizes significantly reduced computation time, as the size reduction of approximately 4.3 times (150² / 35²) resulted in a processing time reduction of approximately 18 times. These boundaries are periodic, meaning that if a particle crosses the boundary in the x direction, it will reappear on the opposite side, effectively reducing computational complexity and maintaining the conservation of matter and momentum. The height of the simulated boundaries is more variable and was determined through iterative testing to ensure that the total particle weight meets the target value (e.g., 100 mg). The height was adjusted based on trial and error until the weight reached an acceptable range; small fluctuations may be observed due to the random nature of particle packing.

[0128] Generate output file

[0129] Once the CAM and SE distributions are validated, two output files will be generated. The first file contains the Cartesian coordinates (x, y, z), particle type, density, and radius of all particles, in a format adapted to the LAMMPS software input. The second file is a LAMMPS script that defines the simulation boundaries, specifies paired particle interactions, and provides instructions for applying forces to the particles within a specified number of time steps.

[0130] Force calculation

[0131] The total force applied to the system is calculated by multiplying the cross-sectional area of ​​the simulation chamber by the applied stress. The force on each particle is distributed based on the volume ratio of CAM to SE. These forces are further distributed among all particles of each type to ensure accurate reflection of particle interactions. The calculated forces and particle properties are contained in the LAMMPS input file, which forms the basis for subsequent processing stages.

[0132] Repeated simulation

[0133] The simulation process is repeated hundreds of times using different input parameters (such as target CAM percentage and particle size distribution). For each test, two files are generated: a particle data file and a corresponding LAMMPS script. An additional Python script prepares the job file, specifying details such as the server used, resource allocation, and execution time for each simulation.

[0134] Processing stage

[0135] Molecular dynamics simulation

[0136] The processing phase utilizes the molecular dynamics simulation software LAMMPS to simulate the manufacturing stresses applied to the material. LAMMPS's particle package is specifically designed to simulate interparticle interactions. The Hertz / material pair_style in LAMMPS is employed to model interparticle forces based on Hertzian (nonlinear) mechanics, where the normal stiffness constant (Kn) and tangential stiffness constant (Kt) are determined based on the material's Young's modulus. The simulation is run under periodic boundary conditions with a time step of 5e-05, continuing until the system reaches equilibrium, meaning all particles are stable and their velocities are negligible. Typically, this requires approximately 250,000 time steps. Adjacent pages are pre-allocated to optimize the simulation by reducing computation time after the initial phase, although this may slow down the initial simulation. In LAMMPS, an adjacent page refers to a module in the code used to identify neighboring particles of a given particle in the simulation.

[0137] Execute simulation

[0138] Use the SCP (Secure Copy) command in the terminal to transfer the necessary simulation files from your local machine to the supercomputer. After the necessary files are transferred, execute the simulation on the supercomputer using scripts generated during the preprocessing phase. These scripts instruct the supercomputer to run specific tests within LAMMPS, aiding in the analysis of different constructs and behaviors.

[0139] Simulation box suppression

[0140] like Figure 3 As shown, the simulation in the processing stage generates a digital representation of a compression simulation chamber subjected to pressure along a vertical axis between the top and bottom of the chamber. As a result, the chamber expands in two additional dimensions. The chamber is compressed until each CAM (or EAM) particle and SE particle contacts at least one adjacent particle, and the particles within the chamber reach equilibrium. The pressure applied in the simulation mimics the pressures actually used in manufacturing sheet batteries, such as approximately 375 MPa. This step may also involve some post-processing. For example, post-processing can determine the height at which a sample of the same weight as in the actual experiment is produced, and this processing can generate a compression simulation chamber with the same height.

[0141] Quality modeling

[0142] The processing phase also involves precise modeling of the mass of the sheet battery cross-section. This process begins by determining the actual weight of the experimentally prepared sheet battery, whose radius and mass are known; for example, a sheet battery with a radius of 500 micrometers could have a mass of 22.7 mg. Based on this experimental data, the corresponding mass of the calculated cross-section of the simulation chamber is calculated. For example, a 35 × 35 micrometer cross-section could correspond to approximately 0.035 mg. After determining the target mass of the cross-section, the simulation chamber is filled with simulated particles as described above. This process continues until the total simulated mass slightly exceeds the target value (e.g., reaching 0.036 mg). After the simulation chamber is compressed, the total mass of the particles within the simulation chamber is calculated until it reaches the target mass, and any remaining particles not included in the target mass (e.g., the particles at the very top of the simulation chamber) are removed from the simulation. To maintain consistency with the composition of actual sheet batteries, unmodeled mass components such as carbon and binder are also calculated.

[0143] Particle interactions

[0144] LAMMPS employs simplified models (e.g., Hooke's Law) to describe interparticle interactions. In LAMMPS, the primary interparticle forces are modeled as spring-like interactions, meaning the force is proportional to the displacement. Unlike Hooke's Law, the Hertzian contact model used in this invention considers a degree of nonlinearity, making it more suitable for interparticle interactions. The force applied to each particle in the simulation is determined by the pressure applied across the entire sample. For example, if a pressure of 100 MPa is applied to a 35 μm × 35 μm sample, the corresponding total force is calculated by multiplying the pressure by the area. This total force is then distributed among the particles within the system, with the amount of force allocated to each type of particle (e.g., CAM particles, EAM particles, or SE particles) determined based on the volume fraction of the active material.

[0145] Simulation optimization

[0146] To optimize the simulation, it is crucial to carefully select an appropriate number of particles and determine the corresponding simulation size. Choosing too few particles can lead to significant bias because it fails to adequately represent the physical system under study. In these cases, the interactions between particles may be insufficient, resulting in the failure to capture key phenomena such as collective behavior or phase transitions. Conversely, overestimating the number of particles can cause another type of problem. When too many particles are included, the forces exerted on each individual particle are diluted. This underestimation of forces can severely impact simulation accuracy, producing misleading results regarding the system's dynamics.

[0147] Velocity Verlet Algorithm

[0148] In LAMMPS, the Velocity Verlet algorithm is used for time integration to update particle positions and velocities. The Velocity Verlet algorithm is a numerical method for integrating equations of motion in molecular dynamics simulations. It is widely recognized for its accuracy and efficiency in calculating positions and velocities at each time step. Its particular advantages lie in its stability and ease of implementation, making it suitable for systems with a large number of interacting particles. The algorithm calculates new positions based on the particle's current position and velocity, and then updates the velocity based on the forces acting on the particle. Simpler integration methods may introduce numerical instability, which the Velocity Verlet algorithm minimizes. The algorithm is time-reversible, a valuable property in physical simulations.

[0149] Equilibrium

[0150] Because particles interact with each other, their motion is influenced by various forces, including interparticle forces, external pressure, and potentially thermal fluctuations. These forces determine how particles collide, bounce, and rearrange, ultimately affecting the system's evolution. The system gradually evolves towards an equilibrium state, characterized by a net equilibrium of forces acting on the particles, resulting in a stable particle composition. Throughout the simulation, the potential energy of the system is monitored due to the interactions and rearrangements of particles under the influence of forces. The simulation continues until the system's potential energy reaches a near-constant value, indicating that the particles have stabilized in an equilibrium configuration. This equilibrium state signifies that the particles have reached a minimum energy configuration, and their motion becomes increasingly regular and predictable. This stable state is crucial for obtaining meaningful conclusions regarding the properties and behavior of the studied material (particulate materials, molecular systems, or other complex aggregates).

[0151] Preprocessing steps

[0152] The processing phase also involves preprocessing steps, including running various Python scripts to generate the necessary files, transferring files between the local machine and the supercomputer, and executing the simulation via a terminal. These preprocessing steps are crucial for setting up the simulation environment and ensuring efficient use of computing resources.

[0153] Post-processing stage

[0154] Post-processing of CAM utilization and tortuosity

[0155] Post-processing calculates key parameters such as CAM utilization and tortuosity. After the LAMMPS processing stage, the first step is to extract key particle information, including particle type (determining whether the particle is CAM or solid electrolyte SE based on density) and the size and position of each particle. Then, an adjacency matrix is ​​constructed. For each particle, each element in the matrix indicates whether two particles are connected or overlap. If the sum of the radii of two particles is greater than their Euclidean distance, a value of 1 is assigned, indicating connection or overlap; otherwise, a value of 0 is assigned. Alternatively, an overlap criterion can be used. For example, if the sum of the radii of two particles is greater than 102% of their Euclidean distance, the two particles are connected or overlap and are therefore assigned a value of 1.

[0156] Python files

[0157] The LAMMPS dump file is read using Python to extract particle information from the last time step. This stage requires preparing two types of files: normalized files, where the simulation and particle positions are scaled to 0 to 1 for visualization purposes in software such as OVITO; and non-normalized files, preserving micrometer-level dimensions, for computational purposes.

[0158] Calculation of CAM utilization rate

[0159] CAM utilization calculation involves identifying SE particles connected to the bottom of the simulation chamber that have z=0 or whose z-coordinate is less than their radius. Starting from this point, continuity is propagated using the adjacency matrix. A search is performed through a pre-constructed adjacency matrix to check which SE particles are in contact with particles connected to the bottom of the simulation chamber. This process iterates through all SE particles until the number of connected SE particles no longer increases, thus identifying the connected SE mass. Figure 5 As shown, the yellow particles are connected SE particles, with the bottom row of particles in contact with the bottom of the simulation chamber; the green particles are non-connected particles. After identifying the connected SE masses, the CAM particles in contact with these masses are then identified. Figure 6 As shown, purple CAM particles are connected to adjacent SE particles, while red particles are not connected. The volume of the connected CAM particles is calculated and compared to the total volume of all CAM particles. This ratio represents the percentage of "active" CAM particles and defines CAM utilization.

[0160] Tortiness estimation

[0161] Tortuosity estimation begins with connectivity analysis. Tools are created to measure connectivity using adjacency matrices (to identify contacting particles) and k-dimensional trees from the SciPy library (for efficient spatial queries). SE particles near the top and bottom of the simulation chamber are identified using predefined thresholds (e.g., 1 micrometer, adjustable). Paths through the centers of connected SE particles are traced. Only paths through connected or overlapping particles are considered, where the sum of particle radii is greater than or equal to the inter-particle distance or overlap criterion (e.g., 102% of the distance). Figure 7 As shown, the yellow particles from top to bottom are identified as connected SE particles, and the path from top to bottom through the center of these particles is represented by a green line. This can be done for all paths. Or, as... Figure 8 As shown, a specific number (e.g., 1000) of random paths are selected from all possible paths in the three-dimensional space from the top to the bottom of the simulation chamber to determine the target path, i.e., the shortest path through the connected SE particles or the median path of the selected paths. The relative tortuosity is obtained by dividing the shortest path length or the median path length by the Euclidean distance between the starting and ending points.

[0162] Porosity calculation

[0163] To calculate porosity, the simulation height is iteratively adjusted to match the mass of the experimental material. For example, if the experiment uses 22.7 mg of material, the simulation is adjusted until it is close to that value. The total system volume is determined by multiplying the simulation height by the cross-sectional area. The solid volume is calculated as the sum of the volumes of all particles. The porosity is then obtained by subtracting the solid volume from the total volume to get the pore volume, and then dividing the pore volume by the total volume to calculate the fractional porosity.

[0164] Integration of post-processing steps

[0165] By integrating these post-processing steps, parameters such as CAM utilization and tortuosity can be accurately quantified, which helps to gain a deeper understanding of the behavior of sheet batteries.

[0166] Simulated code script

[0167] Preprocessing and processing scripts

[0168] like Figure 1As shown, all processes in the preprocessing stage are handled by the main.py script. This script generates the position, type, and size of all particles and stores them in a file named "write.txt". It also generates the LAMMPS script "in.pour", which defines the simulation type, interparticle forces, and other parameters. Another, simpler Python script is used to prepare the necessary .pbs files, which are the job files required to submit LAMMPS simulation tasks to the supercomputer. Additionally, a script named run.py can automatically submit multiple jobs simultaneously. After all files are transferred to the supercomputer, the run.py script is run from the terminal. This script submits each jobs.pbs file and instructs the supercomputer to run the LAMMPS simulations defined in the "in.pour" file. After the simulation takes 8 hours to 2 days to complete, the system generates a dump file "dump.pour" containing particle data for each time step, including normalized positions (ranging from 0 to 1).

[0169] Post-processing script

[0170] In the post-processing stage, data from the last time step in the "dump.pour" file is read. The initially normalized coordinates are then converted to micrometers for calculation. This conversion determines whether two particles are in contact by checking if the distance between them is less than the sum of their radii. There are two main post-processing scripts. One is "Tortcalc.py," which calculates the paths of connected solid electrolyte (SE) particles and identifies the shortest or median path between the top and bottom layers. The path length is then divided by the Euclidean distance between these two points to determine the tortuosity. The other script is "Procedit.py," which calculates porosity, identifies SE particles connected to the bottom layer, and all particles connected to these SE particles. The percentage of CAM (cathode active material) particles connected to this SE mass is then calculated to determine the CAM utilization.

[0171] Multiple simulations

[0172] Repetition and Average

[0173] To account for the randomness in the hybridization process, the method is repeated in multiple simulations. The results of these simulations are averaged to more reliably estimate the behavior of the chip battery. Figure 9 As shown, the green area represents a sheet-type battery, while the blue boxes indicate different simulations. Multiple simulations are performed on each input by changing the seed value generated by the random number generator. This method also allows adjustment of the particle size distribution to calculate key parameters such as CAM utilization, tortuosity, and porosity, which can be used to estimate ionic conductivity. Figures 10A to 12B Examples of various particle size distributions and the corresponding simulation chambers are shown.

[0174] Target CAM content

[0175] Regarding the target CAM content, an initial preselected content (e.g., 85 wt%) is set, assuming uniform CAM particle size. While this assumption is not entirely accurate, the particle size distribution can compensate for this, with larger and smaller particles balancing each other. After processing, the CAM content remains near the preselected value. For example, when the target value is 85%, the CAM content can be 84-86 wt%.

[0176] skewed normal distribution

[0177] In the case of a skewed normal distribution, the particle size distribution is asymmetric. For example, the 75th percentile particle size distribution is asymmetric to the 25th percentile particle size distribution. This introduces more uncertainty during particle packing and may affect the final particle distribution. Therefore, multiple iterations are required to adjust the initial estimates so that the pre-processed weight is close to 85%.

[0178] Iterative process

[0179] The iterative process can follow the following pattern: - C5.3 Target weight: 85.4% → Final weight: Approximately 79-81% - C7.5 Target weight: 85.3% → Final weight: Approximately 76-78% - C10 target weight: 85.3% → Final weight: approximately 74-76% Through repeated adjustments, more accurate initial estimates can be found for the preprocessing stage. These values ​​can then be further fine-tuned to bring the processed CAM weight percentage closer to 85%.

[0180] Difficult to stabilize combination

[0181] Some combinations struggled to maintain stability within the 84-86% range; the results for each combination are recorded for reference. After multiple rounds of adjustments, the values ​​converged as follows: C3S0.3: 86.4% → 84.9% C5.3S0.79: 85.9% → 85.7% C7.5S0.3: 88.0% → 82.7% C10S1.3: 88.5% → 85.4% Ultimately, the results show that it is difficult to make the values ​​fall exactly within the 84-86% range. The combined results can be saved, and the original target weight can be retained from the preprocessed CAM data. Note that the original target weight for different combinations may vary between 91% and 95.75%, exceeding the 85% target value under a symmetrical distribution.

[0182] Trial and error

[0183] The process is largely trial and error: initial estimates are provided to quickly obtain pre-processed weights, and then simulations are performed to generate the final values. Different combinations of variables will produce different results, and due to the non-normal distribution, the stacking behavior cannot be fully predicted in advance.

[0184] Application of simulation results

[0185] Selection of electrode composition

[0186] Using a DEM in LAMMPS, the tortuosity, porosity, and CAM utilization of a cathode containing active materials, solid electrolyte, and conductive materials were simulated, and then calculated for different compositions and particle sizes. This helps guide the selection of electrode compositions that maximize energy density.

[0187] contrast

[0188] This modeling approach differs from previous studies in several ways. The electrode size is modeled on a larger scale, with a height of 100 μm, and the size ratio of the positive electrode active material to the solid electrolyte is more than twice that of previous studies.

[0189] Validated by experimental data

[0190] These simulation results were also compared with experimental data from sheet battery testing to verify the validity of the results.

[0191] All-solid-state batteries and their preparation and application

[0192] Manufacturing batteries

[0193] After the simulation is complete, the electrode composition can be selected based on the simulation results, and an all-solid-state lithium battery can be fabricated accordingly. The battery comprises a first electrode, a second electrode, and an SE layer. The all-solid-state lithium battery can be fabricated through the following steps: fabricating the first electrode using the input parameters of the target group; providing the second electrode; providing carbon materials and binders using the input parameters of the target group; and fabricating the SE layer using the input parameters of the target group. More detailed explanations are provided below.

[0194] Battery

[0195] Another aspect of the invention provides a solid-state battery designed and manufactured using the methods provided herein. A more detailed description follows.

[0196] Battery Applications

[0197] Another aspect of the invention provides for the application of the solid-state batteries described herein. A non-limiting example is an electric vehicle incorporating the all-solid-state batteries provided herein.

[0198] Additional features

[0199] Other aspects of the invention are provided below. Additional features, embodiments, and examples discussed below will apply to the various aspects of the invention discussed above. However, in the event of any conflict between the information discussed above and the information discussed below, the information above shall prevail.

[0200] Solid-state lithium-ion batteries

[0201] Solid-state batteries can be charged and discharged multiple times for an electrical load. A solid-state battery consists of electrodes (positive and negative) and an electrolyte that allows lithium ions to move between the electrodes. Unlike conventional liquid electrolyte batteries, solid-state batteries do not contain any flowing liquid. A circuit is formed between the electrodes, allowing current to flow between them. During charging of a lithium-ion rechargeable battery, lithium ions are released from the positive electrode and inserted into the active material of the negative electrode. During discharging of a lithium-ion rechargeable battery, lithium ions are released from the negative electrode and inserted into the active material of the positive electrode. Energy transfer occurs as lithium ions migrate back and forth between the electrodes.

[0202] Solid-state battery structure

[0203] The present invention provides a solid-state battery 100, which includes a positive electrode 102, a negative electrode 104, and a solid electrolyte layer 106 between the positive electrode 102 and the negative electrode 104. The above is merely an exemplary example, and the solid-state battery 100 does not necessarily include all of these components. For example, in some configurations (e.g., an anode-less system), the negative electrode 104 may be omitted.

[0204] Optional extra layers

[0205] The solid-state battery 100 may optionally include one or more additional layers, such as a separator layer, a protective layer, a suppression layer, a solid electrolyte interface layer, or a combination thereof.

[0206] protective layer

[0207] For example, a protective layer can be disposed between electrodes 102, 104 and the solid electrolyte layer 106. The protective layer can also mitigate dendrite formation (especially on the negative electrode side), thereby improving the overall cycle life and safety of the battery. In some cases, the protective layer can help improve the interfacial stability between the electrode and the electrolyte, potentially reducing adverse side reactions. Furthermore, the protective layer can enhance the mechanical properties of the electrode-electrolyte interface, which can help maintain good contact during cycling.

[0208] Protective layer material

[0209] The protective layer can contain materials such as lithium phosphate, lithium titanate, or lithium lanthanum zirconium oxide (LLZO), which help prevent undesirable side reactions at the electrode-electrolyte interface. Other options for the protective layer material include, but are not limited to, lithium niobium oxide (LiNbO3), lithium tantalum oxide (LiTaO3), lithium aluminum titanium phosphate (LATP), lithium aluminum germanium phosphate (LAGP), lithium silicate, and lithium boron oxide.

[0210] membrane layer

[0211] In some configurations of all-solid-state batteries 100, a separator layer may also be included. These separator layers can provide additional mechanical support for the battery structure while still allowing efficient ion migration. The separator layer can also be designed as a gradient structure, with properties optimized for contact with the positive and negative electrode materials. For example, such a gradient structure can have different porosities, compositions, or surface properties along the thickness direction of the separator layer. In some aspects, the surface of the separator layer can be functionalized with ion-conducting groups or coatings to improve lithium-ion migration efficiency at the electrode-separator interface. The separator layer can also be designed as a multilayer structure, such as sandwiching a high-mechanical-strength core layer between ion-conducting outer layers; this structure is achieved by integrating different materials optimized for specific functions. The separator layer can also be designed to have self-healing capabilities, for example, by reforming bonds after being subjected to mechanical stress, which helps prevent short circuits caused by dendrite growth.

[0212] Diaphragm material

[0213] Conventional liquid electrolyte batteries typically employ porous polymer separators, while solid-state batteries can use thin ceramic or glass-ceramic layers as separators. Materials used for this purpose can include LLZO, lithium aluminum titanium phosphate (LATP), or lithium aluminum germanium phosphate (LAGP). Other separator materials suitable for solid-state batteries include lithium oxynitride phosphate (LiPON), lithium lanthanum titanate (LLTO), and garnet-type lithium materials (e.g., Li6BaLa2Ta2O). 12 ), sulfide materials (e.g., Li) 10 GeP2S 12 ) and polymer-ceramic composite materials (e.g., composites of polyethylene oxide (PEO) and ceramic fillers).

[0214] Solid-state battery cells

[0215] Figure 13 A cell 101 of an all-solid-state battery 100 according to one embodiment is shown. The cell 101 includes a positive electrode 102, a negative electrode 104, and a solid electrolyte layer 106 between the positive electrode 102 and the negative electrode 104. The cell 101 may optionally include one or more additional layers, such as a separator layer, a protective layer, a suppression layer, a solid electrolyte interface layer, or a combination thereof.

[0216] Cell structure

[0217] like Figure 13 As shown, the solid-state battery 100 may include a single cell 101. In other instances, the solid-state battery 100 may include multiple cells, such as at least two cells, at least three cells, or at least four cells. Connecting cells in series can increase the voltage of the solid-state battery 100, and connecting cells in parallel can increase the ampere-hour capacity of the solid-state battery 100.

[0218] Battery size

[0219] The width of cell 101 is w1, the length is 11, and the thickness is t1.

[0220] Cell thickness

[0221] The thickness t1 of cell 101 can be or approximately any value within the following range: approximately 100 μm to approximately 5000 μm, for example, approximately 100 μm, 110 μm, 120 μm, 130 μm, 140 μm, 150 μm, 160 μm, 170 μm, 180 μm, 190 μm, 200 μm, 210 μm, 220 μm, 230 μm, 240 μm, 250 μm, 260 μm, 270 μm, 280 μm, 290 μm, 300 μm, 310 μm, 320 μm, 330 μm, 340 μm, 350 μm. m, 360μm, 370μm, 380μm, 390μm, 400μm, 410μm, 420μm, 430μm, 440μm, 450μm, 460μm, 470μm, 480μm, 490μm, 500μm, 510μm, 520μm, 530μm, 540μm, 550μm, 560μm, 570μm, 580μm, 590μm, 600μm, 610μm, 62 0μm, 630μm, 640μm, 650μm, 660μm, 670μm, 680μm, 690μm, 700μm, 710μm, 720μm, 730μm, 740μm, 750μm m, 760μm, 770μm, 780μm, 790μm, 800μm, 810μm, 820μm, 830μm, 840μm, 850μm, 860μm, 870μm, 880μm, 890μm, 900μm, 910μm, 920μm, 930μm, 940μm, 950μm, 960μm, 970μm, 980μm, 990μm, 1000μm, 1100μm, 1200μm, 1300μm, 1400μm, 1500μm, 1600μm, 1700μm, 1800μm, 1900μm, 2000μm, 3000μm, 4000μm or 5000 μm. In some embodiments, the thickness t1 of the cell 101 can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​within a range of about 100 μm to about 5000 μm (e.g., about 100 μm to about 5000 μm or about 100 μm to about 1000 μm).

[0222] Aspect Ratio of Width

[0223] The width w1 of the battery cell 101 can be substantially greater than the thickness t1 of the battery cell 101. In some embodiments, the aspect ratio of the width w1 to the thickness t1 can be at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 110, at least 120, at least 130, at least 140, at least 150, at least 160, at least 170, at least 180, at least 190, at least 200, at least 210, at least 220, at least 230, at least 240, at least 250, or at least 260. At least 270, at least 280, at least 290, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1000, at least 2000, at least 3000, at least 4000, at least 5000, at least 6000, at least 7000, at least 8000, at least 9000, or at least 10000.

[0224] Aspect Ratio of Length

[0225] The length l1 of the battery cell 101 can be substantially greater than the thickness t1 of the battery cell 101. In some embodiments, the aspect ratio of the length l1 to the thickness t1 can be at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 110, at least 120, at least 130, at least 140, at least 150, at least 160, at least 170, at least 180, at least 190, at least 200, at least 210, at least 220, at least 230, at least 240, at least 250, or at least 260. At least 270, at least 280, at least 290, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1000, at least 2000, at least 3000, at least 4000, at least 5000, at least 6000, at least 7000, at least 8000, at least 9000, or at least 10000.

[0226] positive electrode

[0227] The positive electrode 102 corresponds to one polarity (e.g., positive polarity) of the solid-state battery 100. During the discharge process of the solid-state battery 100, the positive electrode 102 constitutes the positive electrode. The positive electrode 102 is suitable for the diffusion of lithium ions between the current collector 108 and the solid electrolyte layer 106. The positive electrode 102 is electrically connected to the current collector 108.

[0228] Positive position

[0229] In some embodiments, the positive electrode 102 is formed above and in direct contact with the current collector 108. In other embodiments, another functional layer may be provided between the positive electrode 102 and the current collector 108.

[0230] Positive electrode material

[0231] The positive electrode 102 enables reversible insertion and extraction of lithium ions. For example, the positive electrode 102 may contain only a positive electrode active material. In other examples, the positive electrode 102 may optionally contain one or more of conductive carbon, solid electrolyte material, and binder. Optionally, the positive electrode 102 may also contain additives, such as oxidation stabilizers, reduction stabilizers, flame retardants, heat stabilizers, antifogging agents, thickeners, plasticizers, ionic conductivity enhancers, binders (described in detail below), dispersants, wetting agents, tackifiers, crosslinking agents, colorants, etc., or combinations thereof.

[0232] Examples of additives

[0233] Examples of these additives include: butylated hydroxyanisole (BHA) or butylated hydroxytoluene (BHT) as oxidative stabilizers; ascorbic acid or sodium sulfite as reducing stabilizers; aluminum hydroxide or magnesium hydroxide as flame retardants; phenolic compounds or phosphites as heat stabilizers; polyethylene glycol or silica nanoparticles as antifogging agents; carboxymethyl cellulose (CMC) or xanthan gum as thickeners; dibutyl phthalate or triethyl citrate as plasticizers; ceramic fillers or ionic liquids as ionic conductivity enhancers; polyvinylpyrrolidone or sodium dodecyl sulfate as dispersants; polysorbate or poloxamer as wetting agents; silanes or titanates as tackifiers; peroxides or aziridines as crosslinking agents; and carbon black or metal oxides as colorants.

[0234] Positive electrode active material

[0235] Positive electrode active materials may include: lithium cobalt oxide (LiCoO2), lithium nickel oxide (LiNiO2), and Li[Ni a Co b Mn c M 1 d O2 (where M) 1 For any element selected from the group consisting of Al, Ga, In, or combinations thereof, 0.3≤a<1.0, 0≤b≤0.5, 0≤c≤0.5, 0≤d≤0.1, and a+b+c+d=1), Li (Li e M 2 f-e-f M 3 f’ )O2-g A g (where 0≤e≤0.2, 0.6≤f≤1, 0≤f'≤0.2, 0≤g≤0.2, M) 2 Including manganese and at least one element selected from the group consisting of Ni, Co, Fe, Cr, V, Cu, Zn, and Ti, M 3 The element is selected from at least one element chosen from the group consisting of Al, Mg, and B, and A is selected from at least one element chosen from the group consisting of P, F, S, and N, or the above-mentioned compound having one or more transition metals substituted; lithium manganese oxide, for example, with the chemical formula Li 1+h Mn 2-h Compounds represented by O4 (where 0 ≤ h ≤ 0.33), LiMnO3, LiMn2O3, LiMnO2, etc.; lithium copper oxide (Li2CuO2); vanadium oxides, such as LiV3O8, V2O5, or Cu2V2O7; and compounds with the chemical formula LiNi. 1-i M 4 i O2 (where M) 4 =Co, Mn, Al, Cu, Fe, Mg, B or Ga, 0.01≤i≤0.3) represents nickel-site lithium nickel oxide; chemical formula LiMn 2-j M 5 j O2 (where M) 5 =Co, Ni, Fe, Cr, Zn or Ta, 0.01≤j≤0.1) or Li2Mn3M 6 O8 (where M) 6 Lithium manganese composite oxides represented by Fe, Co, Ni, Cu or Zn; LiMn2O4 in which lithium is partially substituted by alkaline earth metal ions; disulfides; LiFe3O4, Fe2(MoO4)3, etc., or combinations thereof.

[0236] Phosphate materials

[0237] Besides the aforementioned positive electrode active materials, the positive electrode can also contain other types of materials. For example, lithium iron phosphate (LiFePO4) can be used as a positive electrode active material due to its excellent thermal stability and long cycle life. Other materials include lithium manganese iron phosphate (LiMn...). x Fe 1-x Other phosphate materials such as lithium vanadium phosphate (LiVOPO4), lithium titanium phosphate (LiTi2(PO4)3), lithium nickel phosphate (LiNiPO4), fluorophosphates (such as LiVPO4F or LiFeSO4F) or lithium cobalt phosphate (LiCoPO4) can also be used.

[0238] Layered oxide materials

[0239] Positive electrode active materials can also include layered oxide materials with various compositions, such as Li(Ni) 1-x- y Co x Mn y O2 (NCM) or Li(Ni) 1-x-y Co x Al y O2 (NCA), in which the proportions of Ni, Co, Mn, and Al can be adjusted to optimize performance characteristics. For example, NCM811 (LiNi 0.8 Co 0.1 Mn 0.1 High-nickel-content NCM materials, such as those containing O2, can be used to achieve higher energy densities. In some cases, the cathode active material can contain spinel structures (e.g., LiNi). 0.5 Mn 1.5 O4), this structure enables high-voltage operation. Alternatively, materials with a lithium hydroxyphosphorus iron oxide structure (such as LiFeSO4F or LiVPO4F) can be used, which have the potential for high energy density and good thermal stability.

[0240] Composite or hybrid cathode materials

[0241] Composite or mixed cathode materials containing two or more active materials can also be used. For example, a mixture of layered oxides and spinel materials can be used to balance energy density and power performance. As another example, lithium iron phosphate can be mixed with one or more of the above-mentioned cathode active materials. In some embodiments, the cathode active material may contain a surface-modified version of the above-mentioned compounds, the surface modification being designed to improve stability, conductivity, or other performance indicators.

[0242] New materials

[0243] The positive electrode active material can also include novel materials, such as disordered rock salt structures (e.g., Li3NbO4-like materials), lithium-rich anti-perovskite structures (e.g., Li3OCl), cationic disordered oxides (e.g., Li-Mn-VO systems), or high-entropy oxides. These materials can provide an ideal combination of high capacity and structural stability. In some cases, dopants or substituents can be added to the positive electrode active material to further modulate its electrochemical properties.

[0244] Particle properties of positive electrode active materials

[0245] The positive electrode active material can be in granular form. The particle size of the positive electrode active material can be from approximately 1 nm to approximately 1000 μm, for example, approximately any of the following values: 10 nm, 20 nm, 30 nm, 40 nm, 50 nm, 60 nm, 70 nm, 80 nm, 90 nm, 100 nm, 110 nm, 120 nm, 130 nm, 140 nm, 150 nm, 160 nm, 170 nm, 180 nm, 190 nm, 200 nm, 210 nm, 220 nm, 230 nm, 240 nm, 250 nm, 260 nm, 270 nm, 280 nm, 290 nm, 300 nm, 310 nm, 320 nm, 330 nm, 340 nm, 350 nm, 360 nm, 370 nm, 380 nm, 390 nm, 400 nm, 410 nm, 420 nm, 430 nm, 440 nm, 450 nm, 460 nm. nm, 470 nm, 480 nm, 490 nm, 500 nm, 550 nm, 600 nm, 650 nm, 700 nm, 750 nm, 800 nm, 850 nm, 900 nm, 950 nm, 1000 nm, 5 μm, 10 μm, 15 μm, 20 μm, 25 μm, 30 μm, 35 μm, 40 μm, 45 μm, 50 μm, 55 μm, 60 μm, 65 μm, 70 μm, 75 μm, 80 μm, 85 μm, 90 μm, 95 μm, 100 μm, 110 μm, 120 μm, 130 μm, 140 μm, 150 μm, 160 μm, 170 μm, 180 μm, 190 μm, 200 μm, 210 μm, 220 μm, 230 μm, 240 μm, 250 μm, 260 μm, 270 μm, 280 μm, 290 μm, 300 μm, 310 μm, 320 μm, 330 μm, 340 μm, 350 μm, 360 μm, 370 μm, 380 μm, 390 μm, 400 μm, 410 µm, 420 µm, 430 µm, 440 µm, 450 µm, 460 µm, 470 µm, 480 µm, 490 µm, 500 µm, 550 µm, 600 µm, 650 µm, 700 µm, 750 µm, 800 µm, 850 µm, 900 µm, 950 µm or 1,000 μm.In some embodiments, the particle size of the positive electrode active material can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​from about 1 nm to about 1000 μm (e.g., from about 10 nm to about 1000 μm). In the positive electrode 102, the gaps between the positive electrode active material particles can be filled with a solid electrolyte material.

[0246] The content of positive electrode active material in the positive electrode

[0247] The content of the positive electrode active material in the solid-state battery 100 affects the charge and discharge capacity of the solid-state battery 100. To manufacture a high-capacity positive electrode 102, a high level of positive electrode active material can be included in the positive electrode 102. For example, based on the total weight of the positive electrode 102, the content of the positive electrode active material in the positive electrode 102 may be approximately or greater than 1 wt%, 5 wt%, 10 wt%, 15 wt%, 20 wt%, 25 wt%, 30 wt%, 35 wt%, 40 wt%, 45 wt%, 50 wt%, 55 wt%, 60 wt%, 65 wt%, 70 wt%, 75 wt%, 80 wt%, 85 wt%, 90 wt%, 95 wt%, 98 wt%, or 99 wt%. In some embodiments, the content of the positive electrode active material in the positive electrode 102 may be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​from greater than 0 to about 100 wt% (e.g., from about 40 wt% to about 98 wt%).

[0248] Conductive material in the positive electrode

[0249] There are no particular limitations on the conductive material in the positive electrode 102, as long as it is conductive and does not cause any chemical change in the corresponding solid-state battery 100. For example, the conductive material may include: graphite, such as natural graphite or artificial graphite; carbon black, such as acetylene black, Ketjen black, channel black, furnace black, lamp black, or thermally cracked carbon black; conductive fibers, such as carbon fibers or metal fibers; carbon nanotubes (CNTs), including single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs); metal powders, such as fluorocarbons, aluminum or nickel powders; conductive whiskers, such as zinc oxide or potassium titanate; conductive metal oxides, such as titanium oxide; conductive polymers, such as polyphenylene derivatives; graphene, metal nanowires (such as silver nanowires), indium tin oxide (ITO), antimony-doped tin oxide (ATO), fluorine-doped tin oxide (FTO), aluminum-doped zinc oxide (AZO), gallium-doped zinc oxide (GZO), conductive ceramics (such as titanium nitride or titanium carbide), etc., or combinations thereof.

[0250] The content of conductive material in the positive electrode

[0251] Based on the total weight of the positive electrode 102, the content of conductive material in the positive electrode 102 is or approximately 1 wt%, 3 wt%, 4 wt%, 5 wt%, 6 wt%, 7 wt%, 8 wt%, 9 wt%, 10 wt%, 11 wt%, 12 wt%, 13 wt%, 14 wt%, 15 wt%, 16 wt%, 17 wt%, 18 wt%, 19 wt%, 20 wt%, 21 wt%, 22 wt%, 23 wt%, 24 wt%, 25 wt%, 26 wt%, 27 wt%, 28 wt%, 29 wt%, or 30 wt%. In some embodiments, the content of conductive material in the positive electrode 102 can be within a range formed by selecting any two values ​​listed in the preceding sentence, for example, from approximately 1 wt% to approximately 30 wt%.

[0252] Adhesive materials

[0253] Adhesives may include a variety of adhesive polymers, such as polyvinylidene fluoride-hexafluoropropylene copolymer (PVdF-co-HFP), polyvinylidene fluoride, polyacrylonitrile, polymethyl methacrylate, polyvinyl alcohol, carboxymethyl cellulose (CMC), starch, hydroxypropyl cellulose, regenerated cellulose, polyvinylpyrrolidone, polytetrafluoroethylene, polyethylene, polypropylene, polyacrylate, ethylene propylene diene monomer (EPDM), sulfonated EPDM, styrene-butadiene rubber (SBR), fluororubber, polyacrylic acid, polyimide, polyamide-imide, polyurethane, polyethylene oxide (PEO), ethylene-vinyl acetate copolymer (PEVA), polyvinyl acetate (PVA), chitosan, guar gum (GG), xanthan gum, carrageenan, pectin, water-soluble polymers, lignin, polymers in which hydrogen atoms are replaced by lithium, sodium, or calcium, various copolymers thereof, or combinations thereof.

[0254] Other adhesive materials

[0255] In addition to the aforementioned adhesive materials, other types of adhesive materials can be used in the positive electrode to improve its performance and stability. For example, water-soluble adhesives such as sodium alginate, gelatin, or polyacrylamide can be used to improve the environmental friendliness of the electrode manufacturing process. These adhesives can also provide advantages in electrode flexibility and bond strength. In some cases, to simultaneously improve the mechanical integrity and conductivity of the electrode, conductive adhesives such as poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) or polyaniline (PANI) can be used.

[0256] Novel adhesive system

[0257] Novel adhesive systems (such as self-healing polymers or supramolecular assemblies) can be incorporated to improve the long-term stability and cycle life of the battery. Furthermore, composite adhesives combining multiple polymers or incorporating inorganic nanoparticles can be used to adjust the mechanical, thermal, and electrochemical properties of the electrodes. In some embodiments, to reduce the environmental impact of battery production and disposal, bio-derived or biodegradable adhesives, such as cellulose derivatives or chitosan, can be employed.

[0258] The content of binder in the positive electrode

[0259] Based on the total weight of the positive electrode 102, the binder content in the positive electrode 102 is or approximately 1 wt%, 2 wt%, 3 wt%, 4 wt%, 5 wt%, 6 wt%, 7 wt%, 8 wt%, 9 wt%, 10 wt%, 11 wt%, 12 wt%, 13 wt%, 14 wt%, 15 wt%, 16 wt%, 17 wt%, 18 wt%, 19 wt%, 20 wt%, 21 wt%, 22 wt%, 23 wt%, 24 wt%, 25 wt%, 26 wt%, 27 wt%, 28 wt%, 29 wt%, or 30 wt%. In some embodiments, the binder content in the positive electrode 102 can be within a range formed by selecting any two values ​​listed in the preceding sentence, for example, from approximately 1 wt% to approximately 30 wt%.

[0260] solid electrolyte materials

[0261] The solid electrolyte material in the positive electrode 102 can be configured to be the same as the material of the solid electrolyte layer 106 discussed below. The solid electrolyte material in the positive electrode 102 can be the same as or different from the material of the solid electrolyte layer 106.

[0262] Content of solid electrolyte material in the positive electrode

[0263] Based on the total weight of the positive electrode 102, the content of solid electrolyte material in the positive electrode 102 is approximately 1 wt%, 2 wt%, 3 wt%, 4 wt%, 5 wt%, 6 wt%, 7 wt%, 8 wt%, 9 wt%, 10 wt%, 11 wt%, 12 wt%, 13 wt%, 14 wt%, 15 wt%, 16 wt%, 17 wt%, 18 wt%, 19 wt%, 20 wt%, 21 wt%, 22 wt%, 23 wt%, 24 wt%, 25 wt%, 26 wt%, 27 wt%, 28 wt%, 29 wt%, or 30 wt%. In some embodiments, the content of solid electrolyte material in the positive electrode 102 can be within a range formed by selecting any two values ​​listed in the previous sentence, for example, from approximately 1 wt% to approximately 30 wt%.

[0264] Thickness of the positive electrode

[0265] The thickness t2 of the positive electrode 102 can be or approximately any value within the range of 0 to 1000 μm, such as 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100 μm, 110 μm, 120 μm, 130 μm, 140 μm, 150 μm, 160 μm, 170 μm, 180 μm, 190 μm, 200 μm, 210 μm, 220 μm, 230 μm, 240 μm, 250 μm, 260 μm, 270 μm, 280 μm, 290 μm, 300 μm, 310 μm, 320 μm, 330 μm, 340 μm, 350 μm, 360 μm, 370 μm, 380 μm, etc. μm, 390 μm, 400 μm, 410 μm, 420 μm, 430 μm, 440 μm, 450 μm, 460 μm, 470 μm, 480 μm, 490 μm, 500 μm, 510 μm, 520 μm, 530 μm, 540 μm, 550 μm, 560 μm, 570 μm, 580 μm, 590 μm, 600 μm, 610 μm, 620 μm, 630 μm, 640 μm, 650 μm, 660 μm, 670 μm, 680 μm, 690 μm, 700 μm, 710 μm, 720 μm, 730 μm, 740 μm, 750 μm, 760 μm, 770 μm, 780 μm, 790 μm, 800 μm, 810 μm, 820 μm, 830 μm, 840 μm, 850 μm, 860 μm, 870 μm, 880 μm, 890 μm, 900 μm, 910 μm, 920 μm, 930 μm, 940 μm, 950 μm, 960 μm, 970 μm, 980 μm, 990 μm, 1000 μm. In some embodiments, the thickness t2 of the positive electrode 102 can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​greater than 0 to about 1000 μm (e.g., about 10 μm to about 1000 μm).

[0266] Porosity of the positive electrode

[0267] Based on the total volume of the positive electrode 102, the porosity of the positive electrode 102 can be any value within the range of 0 to 20 volume%, such as 0 volume%, 1 volume%, 2 volume%, 3 volume%, 4 volume%, 5 volume%, 6 volume%, 7 volume%, 8 volume%, 9 volume%, 10 volume%, 11 volume%, 12 volume%, 13 volume%, 14 volume%, 15 volume%, 16 volume%, 17 volume%, 18 volume%, or any other volume% within the range of 0 to 20 volume%. In some embodiments, the porosity of the positive electrode 102 can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​within the range of 0 to 20 volume% (e.g., 0 volume% to about 18 volume%).

[0268] Lithium-ion diffusion coefficient of the positive electrode

[0269] The lithium-ion diffusion coefficient of cathode 102 can be greater than or approximately greater than 0 to 1 × 10⁻⁶. -7 cm 2 Any value within the range of / s, for example, 1×10 -14 cm 2 / s, 1×10 -13 cm 2 / s, 1×10 -12 cm 2 / s, 1×10 -11 cm 2 / s, 1×10 -10 cm 2 / s, 1×10 - 9 cm 2 / s, 1×10 -8 cm 2 / s or 1×10 -7 cm 2 / s. In some embodiments, the lithium-ion diffusion coefficient of the positive electrode 102 can be within a range formed by selecting any two values ​​listed above, or it can be greater than 0 to 1 × 10⁻⁶. - 7 cm 2 / s (e.g., 1×10) -14 cm 2 / s to approximately 1×10 -7 cm 2 Within the range of / s, select any two values ​​to form a range.

[0270] Positive current collector

[0271] The current collector 108 collects the electrical energy generated by the positive electrode 102 and supports the positive electrode 102.

[0272] Materials for positive electrode current collectors

[0273] The material of the current collector 108 is not particularly limited, as long as it enables the positive electrode 102 to attach, has suitable conductivity, and does not cause significant chemical changes in the solid-state battery 100 within the voltage range of the solid-state battery 100. For example, the current collector 108 may be made of or contain a variety of materials, such as metals, conductive carbon, or conductive ceramics, but is not limited thereto. The metal of the current collector 108 may include one or more of the following: aluminum, aluminum alloys, copper, copper alloys, nickel, nickel alloys, titanium, titanium alloys, iron, iron alloys (e.g., steel, stainless steel), silver, silver alloys, gold, platinum, palladium, chromium, molybdenum, tungsten, tantalum, niobium, zirconium, vanadium, manganese, cobalt, indium, tin, lead, bismuth, or combinations thereof, but is not limited thereto.

[0274] Geometry of the current collector

[0275] To optimize the performance of the current collector 108 and its integration with the positive electrode 102, the current collector 108 can also be configured with a variety of other geometries, and its size can be determined according to specific shape factors (e.g., bag-shaped, cylindrical, and / or prismatic shape factors).

[0276] Shape of the positive current collector

[0277] By forming a fine surface irregularity structure on the surface of the current collector 108, the adhesion between the positive electrode 102 and the current collector 108 can be improved. The current collector 108 can have various shapes, such as a membrane, sheet, foil, mesh, porous body, foam, non-woven fabric, or a combination thereof.

[0278] Examples of current collector shapes and sizes

[0279] For example, the current collector 108 can be configured as a mesh or grid, which provides enhanced mechanical support while maintaining a high surface area for electrode attachment. In some embodiments, the current collector 108 can be designed with a corrugated or wavy pattern, potentially increasing the contact area with the positive electrode material and improving overall conductivity. The current collector 108 can also be made into a perforated sheet to allow for better electrolyte permeation and ion migration. In some cases, the current collector 108 can be formed into a three-dimensional structure, such as an interconnected fiber network or honeycomb structure, which can enhance the structural integrity of the electrode assembly while promoting efficient current collection.

[0280] Thickness of the positive current collector

[0281] The thickness t3 of the current collector 108 can be or approximately any value within the range of 0 to 500 μm, such as 1 μm, 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100 μm, 110 μm, 120 μm, 130 μm, 140 μm, 150 μm, 160 μm, 170 μm, 180 μm, 190 μm, 200 μm, 210 μm, 220 μm, 230 μm, 240 μm, 250 μm, 260 μm, 270 μm, 280 μm, 290 μm, 300 μm, 310 μm, 320 μm. The thickness t3 of the current collector 108 can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​greater than 0 to 500 μm (e.g., from about 5 μm to about 500 μm). The values ​​are 330 μm, 340 μm, 350 μm, 360 μm, 370 μm, 380 μm, 390 μm, 400 μm, 410 μm, 420 μm, 430 μm, 440 μm, 450 μm, 460 μm, 470 μm, 480 μm, 490 μm, or 500 μm. In some embodiments, the thickness t3 of the current collector 108 can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​greater than 0 to 500 μm (e.g., from about 5 μm to about 500 μm).

[0282] Method for manufacturing positive electrode

[0283] The positive electrode 102 can be obtained through various methods.

[0284] Dry powder coating process

[0285] For example, a dry powder coating process can be used to mix the positive electrode active material, conductive additives, and binder in a dry state, and then directly coat them onto the current collector 108 using electrostatic deposition or mechanical pressing. This method can reduce environmental impact by reducing the use of solvents.

[0286] 3D printing

[0287] In some cases, additive manufacturing techniques (such as 3D printing) can be used to fabricate the positive electrode 102. This method allows for precise control of the electrode structure and porosity, potentially improving electrode performance and energy density. Depending on the specific material and desired electrode performance, various 3D printing methods can be employed, including fused deposition modeling (FDM), selective laser sintering (SLS), or direct ink writing (DIW).

[0288] electrospinning

[0289] Another method for manufacturing the positive electrode 102 involves electrospinning. In this process, a solution containing a positive electrode active material, conductive additives, and a polymer binder is extruded through a nozzle under the influence of an electric field to form nanofibers. These fibers can be directly collected on the current collector 108 to form an electrode structure with high porosity and increased surface area.

[0290] Casting

[0291] In some embodiments, a casting process can be used to prepare the positive electrode 102. This technique involves spreading a slurry of electrode material onto a moving carrier film using a doctor blade, followed by drying and calendering. The resulting electrode strip can then be laminated onto the current collector 108.

[0292] Spraying

[0293] Alternatively, spraying technology can be used to prepare the positive electrode 102. In this method, a fine mist of electrode slurry is sprayed onto the current collector 108 using compressed air or ultrasonic atomization. This method can produce a thin and uniform electrode layer, and is particularly suitable for large-scale production.

[0294] Cryogenic casting

[0295] In some cases, cryogenic casting can be used to manufacture the positive electrode 102. This process involves freezing a slurry of electrode material, followed by the sublimation of ice to form a porous structure. The resulting porous electrode can then be sintered and attached to the current collector 108.

[0296] Sol-gel method

[0297] For some applications, the sol-gel method can be used to prepare the positive electrode 102. This method involves forming a colloidal suspension (sol) and then transforming it into a gel-like network containing the positive electrode active material and other components. This gel can be coated onto the current collector 108, followed by heat treatment to form the final electrode structure.

[0298] Slurry-based processes

[0299] For example, the positive electrode active material can be mixed with a solvent and optionally a binder, conductive material and dispersant to form a slurry. The slurry is then coated (e.g., applied) onto the current collector 108, followed by pressing and drying to obtain the positive electrode 102.

[0300] Coating method for positive electrode slurry

[0301] The coating of the slurry on the positive electrode 102 may include the use of techniques selected from the group consisting of: slot coating, gravure coating, spin coating, spray coating, roller coating, curtain coating, extrusion, casting, screen printing, inkjet printing, spray printing, gravure printing, heat transfer printing, letterpress printing, intaglio printing, offset printing, and combinations thereof.

[0302] Double-layer slot coating

[0303] In some embodiments, dual-slit coating (DLD) technology can be used to prepare the positive electrode 102. This method simultaneously coats two different electrode materials onto the current collector 108 through a single channel. The DLD process can create a gradient structure within the electrode, potentially optimizing both the electrochemical performance and mechanical properties of the positive electrode. Furthermore, this technology can introduce functional intermediate layers or protective coatings during electrode fabrication, thereby potentially improving the overall performance and lifespan of the battery.

[0304] Solvent for positive electrode slurry

[0305] Solvents used for forming the positive electrode 102 may include water and / or organic solvents such as N-methylpyrrolidone (NMP), dimethylformamide (DMF), acetone, dimethylacetamide, dimethyl sulfoxide (DMSO), isopropanol, and combinations thereof. The amount of solvent used should be sufficient to dissolve and disperse the electrode components (e.g., positive electrode active material, binder, and conductive material) considering factors such as slurry coating thickness, productivity, and combinations thereof. Other solvents that may be used include ethanol, methanol, propanol, butanol, ethyl acetate, methyl ethyl ketone, tetrahydrofuran, diethyl ether, and toluene.

[0306] Solvent-free methods

[0307] In some aspects of the invention, solvent-free methods (e.g., dry powder processing or melt extrusion) can be used to prepare the positive electrode 102, which reduces the use of liquid solvents and can provide environmental and cost advantages.

[0308] Dispersant for positive electrode slurry

[0309] The dispersant used for forming the positive electrode 102 may include aqueous dispersants and / or organic dispersants, such as N-methyl-2-pyrrolidone. Other available dispersants may include polyvinylpyrrolidone (PVP), carboxymethyl cellulose (CMC), sodium dodecyl sulfate (SDS), Triton X-100, polyethylene glycol (PEG), polyacrylic acid (PAA), and various surfactants such as polysorbate or poloxamer.

[0310] Drying technology of positive electrode slurry

[0311] The slurry for cathode 102 can be dried by irradiation with heat, an electron beam (E-beam), gamma rays, or ultraviolet light (G-line, H-line, I-line), or a combination thereof, causing the solvent to evaporate. For example, the slurry can be vacuum dried at room temperature. Although the solvent is removed by evaporation through the drying step, other components do not evaporate and remain intact to form cathode 102.

[0312] Other drying techniques

[0313] In addition to the drying techniques mentioned above, other methods can also be used to dry the positive electrode 102, such as infrared (IR) drying, microwave drying, or freeze drying.

[0314] Combination of drying technologies

[0315] In some implementations, a combination of various drying techniques can be used, such as using convection heating followed by vacuum drying, to optimize the drying process and ensure complete solvent removal while maintaining the integrity of the electrode structure.

[0316] Negative electrode overview

[0317] The negative electrode 104 corresponds to a polarity different from that of the positive electrode 102 in the solid-state battery 100 (e.g., negative polarity). During the discharge process of the solid-state battery 100, the negative electrode 104 constitutes the negative electrode. The negative electrode 104 is suitable for the diffusion of lithium ions between the current collector 110 and the solid electrolyte layer 106.

[0318] negative electrode position

[0319] The negative electrode 104 is electrically connected to the current collector 110. In some embodiments, the negative electrode 104 is formed above the current collector 110 and is in direct contact with the current collector 110. In other embodiments, another functional layer may be provided between the negative electrode 104 and the current collector 110.

[0320] Anode-free system

[0321] In some embodiments, as described above, the all-solid-state battery 100 can employ an anode-less system. In this structure, the negative electrode 104 can be omitted, and lithium metal can be directly deposited onto the current collector 110 during charging. This method can improve the energy density of the battery, eliminate the need for a separate negative electrode material, and also reduce the overall thickness of the battery structure.

[0322] negative electrode material

[0323] The negative electrode 104 enables reversible insertion and extraction of lithium ions. For example, the negative electrode 104 may contain only the negative electrode active material. In other examples, the negative electrode 104 may contain conductive particles, binders, or combinations thereof.

[0324] Additives for the negative electrode

[0325] Optionally, the negative electrode 104 may also contain additives such as oxidation stabilizers (such as butylated hydroxyanisole, butylated hydroxytoluene, propyl gallate, tert-butylhydroquinone), reduction stabilizers (such as ascorbic acid, sodium sulfite, isoascorbic acid, sodium metabisulfite), flame retardants (such as aluminum hydroxide, magnesium hydroxide, ammonium polyphosphate, melamine cyanurate), heat stabilizers or light stabilizers (such as phenolic compounds, phosphites, hindered amine light stabilizers, ultraviolet absorbers such as benzophenone or benzotriazole), antifogging agents (such as polyethylene glycol, silica nanoparticles, glycerol, sorbitol), thickening agents (such as carboxymethyl cellulose, xanthan gum), etc., or combinations thereof.

[0326] Other additives for the negative electrode

[0327] In addition, conductive additives such as carbon black, graphene or carbon nanotubes can be added to improve conductivity, while binder modifiers such as styrene-butadiene rubber or polyacrylic acid can improve adhesion and mechanical stability. Functional additives such as fluoroethylene carbonate or vinylene carbonate can also be included to promote the formation of a stable solid electrolyte interface layer on the surface of the negative electrode.

[0328] Materials of the negative electrode active material

[0329] The negative electrode active material is made of or contains the following various materials: such as alkali metals, alkaline earth metals, Group 3B metals, transition metals, metalloids, their alloys, conductive carbon, etc., or combinations thereof, but not limited thereto. In some embodiments, the negative electrode active material may contain silicon, silicon alloy, lithium, lithium alloy, conductive carbon, etc., or combinations thereof, but not limited thereto. In some embodiments, the lithium alloy is made of a lithium alloy containing silicon, chlorine, etc. or combinations thereof, or contains these materials. A lithium metal thin film can be used as the negative electrode active material.

[0330] Other materials of the negative electrode active material

[0331] The negative electrode active material may include carbonaceous materials such as artificial graphite, natural graphite, graphitized carbon fiber, amorphous carbon, etc.; metal materials capable of forming alloys with lithium such as Si, Al, Sn, Pb, Zn, Bi, In, Mg, Ga, Cd, Si alloy, Sn alloy, Al alloy, etc.; metal oxides capable of doping and de-doping lithium ions such as SiO x (0 < x < ²), SnO₂, vanadium oxides or lithium vanadium oxides; and composite materials containing the above metal materials and carbonaceous materials such as Si-C composite materials or Sn-C composite materials.

[0332] Carbonaceous materials

[0333] Carbon-based materials can include low-crystallinity carbon, high-crystallinity carbon, or combinations thereof. Representative examples of low-crystallinity carbon are soft carbon or hard carbon; representative examples of high-crystallinity carbon are high-temperature calcined carbon, such as amorphous, plate-like, sheet-like, spherical, or fibrous natural or artificial graphite, kish graphite, pyrolytic carbon, mesophase pitch-based carbon fibers, mesophase carbon microspheres, mesophase pitch, coke derived from petroleum or coal tar pitch, or combinations thereof.

[0334] Metal-carbon composite materials

[0335] Alternatively, according to some aspects of the invention, the negative electrode 104 may comprise a metal-carbon composite negative electrode material, such as a silver-carbon mixture or composite, wherein silver particles are incorporated between amorphous and / or crystalline carbon particles. Silver is used here as an example, but other metals, including, for example, tin and / or zinc, may also be used. Silicon may be used instead of silver.

[0336] Other materials for negative electrode active materials

[0337] In addition to the materials mentioned above, the negative electrode active material may also include: titanium compounds (such as lithium titanate (Li4Ti5O)). 12 Materials such as titanium dioxide (TiO2) or molybdenum oxide (MoO2) exhibit excellent cycle stability and high-rate performance. Other promising materials include transition metal oxides, such as molybdenum oxide (MoO2). x ), iron oxide (FeO) x ) or nickel oxide (NiO) x These materials can provide high theoretical capacity. In some cases, composite materials combining different active materials can be used, such as silicon-graphite composites or tin-carbon composites, to fully utilize the advantages of multiple materials while mitigating their individual limitations.

[0338] Dendrite formation

[0339] When the negative electrode 104 is composed of or contains lithium or a lithium alloy, dendrites may form on the negative electrode 104. Dendrites are metallic lithium structures formed when excess lithium ions accumulate on the surface of the negative electrode 104. The formed dendrites may damage the solid electrolyte layer 106, reduce the capacity of the solid-state battery 100, and / or otherwise cause other adverse performance issues in the solid-state battery 100. Dendrite formation is a major challenge for lithium-ion batteries because these structures can grow through the electrolyte, potentially leading to short circuits and safety risks. The growth rate and morphology of dendrites can be affected by factors such as current density, temperature, and the properties of the electrolyte-electrode interface.

[0340] Advantages of solid electrolytes in suppressing dendrite formation

[0341] Solid electrolytes offer several advantages over liquid electrolytes in suppressing dendrite formation. The mechanical strength of solid electrolytes can provide a physical barrier to prevent lithium metal penetration, thus helping to inhibit dendrite growth. Furthermore, the uniform ion distribution in solid electrolytes can promote more uniform lithium deposition, reducing the likelihood of localized dendrite nucleation. Some solid electrolytes can also form stable interfaces with lithium metal anodes, further suppressing dendrite formation. However, while solid electrolytes can significantly reduce the risk of dendrite growth, they cannot completely eliminate it. Current research aims to develop advanced solid electrolyte materials with enhanced dendrite suppression capabilities.

[0342] Shape of negative electrode active material

[0343] The negative electrode active material can be in particulate form or in continuous monomer form (e.g., thin film or sheet).

[0344] Particle size

[0345] In embodiments where the negative electrode active material is in particulate form, the particle size of the negative electrode active material can be any value in the range of about 10 nm to about 1000 μm, such as about 10 nm, 15 nm, 20 nm, 25 nm, 30 nm, 35 nm, 40 nm, 45 nm, 50 nm, 55 nm, 60 nm, 65 nm, 70 nm, 75 nm, 80 nm, 85 nm, 90 nm, 95 nm, 100 nm, 110 nm, 120 nm, 130 nm, 140 nm, 150 nm, 160 nm, 170 nm, 180 nm, 190 nm, 200 nm, 210 nm, 220 nm, 230 nm, 240 nm, 250 nm, 260 nm, 270 nm, 280 nm, 290 nm, 300 nm, 310 nm, 320 nm, 330 nm, 340 nm, 350 nm, 360 nm, 370 nm, etc. nm, 380 nm, 390 nm, 400 nm, 410 nm, 420 nm, 430 nm, 440 nm, 450 nm, 460 nm, 470 nm, 480 nm, 490 nm, 500 nm, 510 nm, 520 nm, 530 nm, 540 nm, 550 nm, 560 nm, 570 nm, 580 nm, 590 nm, 600 nm, 610 nm, 620 nm, 630 nm, 640 nm, 650 nm, 660 nm, 670 nm, 680 nm, 690 nm, 700 nm, 710 nm, 720 nm, 730 nm, 740 nm, 750 nm, 760 nm, 770 nm, 780 nm, 790 nm, 800 nm, 810 nm, 820 nm, 830 nm, 840 nm, 850 nm, 860 nm, 870 nm, 880 nm, 890 nm, 900 nm, 910 nm, 920 nm, 930 nm, 940 nm, 950 nm, 960 nm, 970 nm, 980 nm, 990 nm, 1000 nm, 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 15 μm, 20 μm, 25 μm, 30 μm, 35 μm, 40 μm, 45 μm, 50 μm, 55 μm, 60 μm, 65 μm, 70 μm, 75 μm, 80 μm, 85 μm, 90 μm, 95 μm, 100 μm, 110 μm, 120 μm, 130 μm, 140 μm, 150 μm, 160μm, 170 μm, 180 μm, 190 μm, 200 μm, 210 μm, 220 μm, 230 μm, 240 μm, 250 μm, 260 μm, 270 μm, 280 μm, 290 μm, 300 μm, 310 μm, 320 μm, 330 μm, 340 μm, 350 μm, 360 μm, 370 μm, 380 μm, 390 μm, 400 μm, 410 μm, 420 μm, 430 μm, 440 μm, 450 μm, 460 μm, 470 μm, 480 μm, 490 μm, 500 μm, 510 μm, 520 μm, 530 μm, 540 μm, 550 μm, 560 μm, 570 μm, 580 μm, 590 μm, 600 μm, 610 μm, 620 μm, 630 μm, 640 μm, 650 μm, 660 μm, 670 μm, 680 μm, 690 μm, 700 μm, 710 μm, 720 μm, 730 μm, 740 μm, 750 μm, 760 μm, 770 μm, 780 μm, 790 μm, 800 μm, 810 μm, 820 μm, 830 μm, 840 μm, 850 μm, 860 μm, 870 μm, 880 μm, 890 μm, 900 μm, 910 μm, 920 μm, 930 μm, 940 μm, 950 μm, 960 μm, 970μm, 980 μm, 990 μm, or 1000 μm. In some embodiments, the particle size of the negative electrode active material can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​within the range of about 10 nm to about 1000 μm (e.g., about 10 nm to about 1000 μm).

[0346] Content of negative electrode active material in negative electrode

[0347] The content of the negative electrode active material in the solid-state battery 100 affects the charge and discharge capacity of the solid-state battery 100. To manufacture a high-capacity negative electrode 104, a high level of negative electrode active material can be included in the negative electrode 104. For example, based on the total weight of the negative electrode 104, the content of the negative electrode active material in the negative electrode 104 may be approximately or greater than 70% by weight, 80% by weight, 90% by weight, 95% by weight, 98% by weight, 99% by weight, or 100% by weight. In some embodiments, the content of the negative electrode active material in the negative electrode 104 may be within a range formed by selecting any two values ​​listed in the preceding sentence, for example, approximately 70% by weight to approximately 100% by weight.

[0348] Materials of binder in negative electrode

[0349] Adhesives may include a variety of adhesive polymers, such as polyvinylidene fluoride-hexafluoropropylene copolymer (PVdF-co-HFP), polyvinylidene fluoride, polyacrylonitrile, polymethyl methacrylate, polyvinyl alcohol, carboxymethyl cellulose (CMC), starch, hydroxypropyl cellulose, regenerated cellulose, polyvinylpyrrolidone, polytetrafluoroethylene, polyethylene, polypropylene, polyacrylate, ethylene propylene diene monomer (EPDM), sulfonated EPDM, styrene-butadiene rubber (SBR), fluororubber, polyacrylic acid, polymers in which hydrogen atoms are replaced by lithium, sodium or calcium, various copolymers thereof, or combinations thereof.

[0350] Examples of materials for binders in negative electrodes

[0351] In addition to the adhesives mentioned above, other adhesives suitable for the negative electrode may include polyimide, polyamide-imide, polyurethane, polyethylene oxide (PEO), ethylene-vinyl acetate copolymer (PEVA), polyvinyl acetate (PVA), alginate, chitosan, guar gum, xanthan gum, carrageenan, pectin, gelatin, lignin, and various water-soluble polymers or their derivatives. In some cases, to simultaneously improve adhesion and conductivity within the negative electrode, conductive polymers such as polypyrrole, polyaniline, or poly(3,4-ethylenedioxythiophene) (PEDOT) may be used as adhesives.

[0352] The content of binder in the negative electrode

[0353] Based on the total weight of the negative electrode 104, the binder content in the negative electrode 104 can be or approximately 0 wt%, 1 wt%, 2 wt%, 3 wt%, 4 wt%, 5 wt%, 6 wt%, 7 wt%, 8 wt%, 9 wt%, 10 wt%, 11 wt%, 12 wt%, 13 wt%, 14 wt%, 15 wt%, 17 wt%, 18 wt%, 19 wt%, 20 wt%, 21 wt%, 22 wt%, 23 wt%, 24 wt%, 25 wt%, 26 wt%, 27 wt%, 28 wt%, 29 wt%, or 30 wt%, or any other weight percentage within the range of 0 to 30 wt%. In some embodiments, the binder content in the negative electrode 104 can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​within the range of 0 to 30 wt% (e.g., from about 0 wt% to about 30 wt%).

[0354] Thickness of the negative electrode

[0355] The thickness of the negative electrode 104 can be or approximately 10 μm, 15 μm, 20 μm, 25 μm, 30 μm, 35 μm, 40 μm, 45 μm, 50 μm, 55 μm, 60 μm, 65 μm, 70 μm, 75 μm, 80 μm, 85 μm, 90 μm, 95 μm, or 100 μm. In some embodiments, the thickness t4 of the negative electrode 104 can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​from 10 μm to about 100 μm (e.g., from about 10 μm to about 100 μm or from about 10 μm to about 20 μm).

[0356] Porosity of the negative electrode

[0357] Based on the total volume of the negative electrode 104, the porosity of the negative electrode 104 can be approximately 0% by volume, 1% by volume, 2% by volume, 3% by volume, 4% by volume, 5% by volume, 6% by volume, 7% by volume, 8% by volume, 9% by volume, 10% by volume, 11% by volume, 12% by volume, 13% by volume, 14% by volume, 15% by volume, 16% by volume, 17% by volume, or 18% by volume, or any other volume percentage within the range of 0 to 18%. In some embodiments, the porosity of the negative electrode 104 can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​within the range of 0% to approximately 18% by volume.

[0358] Lithium-ion diffusion coefficient of the negative electrode

[0359] The lithium-ion diffusion coefficient of the 104 negative electrode can be 1×10⁻⁶ or approximately 1×10⁻⁶. -14 cm 2 / s, 1×10 -13 cm 2 / s, 1×10 -12 cm 2 / s, 1×10 -11 cm 2 / s, 1×10 -10 cm 2 / s, 1×10 -9 cm 2 / s, 1×10 -8 cm 2 / s or 1×10 -7 cm 2 / s. In some embodiments, the lithium-ion diffusion coefficient of the negative electrode 104 can be within a range formed by selecting any two values ​​listed above, or it can be within a range of 1×10. -14 cm 2 / s to approximately 1×10 -7cm 2 Within the range of / s, select any two values ​​to form a range.

[0360] Negative electrode current collector

[0361] The current collector 110 collects the electrical energy generated by the negative electrode 104 and supports the negative electrode 104.

[0362] Materials for negative electrode current collectors

[0363] The material of the current collector 110 is not particularly limited, as long as it enables the negative electrode 104 to attach, has suitable conductivity, and does not cause significant chemical changes in the solid-state battery 100 within the voltage range of the solid-state battery 100. For example, the current collector 110 may be made of or contain metal or conductive carbon, but is not limited to these materials.

[0364] Metallic materials for current collectors

[0365] The metal of the current collector 110 may include one or more, or combinations thereof, selected from the group consisting of aluminum, aluminum alloys, copper, copper alloys, nickel, nickel alloys, titanium, titanium alloys, iron, iron alloys (e.g., steel, stainless steel), silver, and silver alloys, but is not limited thereto.

[0366] Shape of negative electrode current collector

[0367] By forming a fine surface irregularity on the surface of the current collector 110, the adhesion between the negative electrode 104 and the current collector 110 can be improved. The current collector 110 can have various shapes, such as a membrane, sheet, foil, mesh, porous body, foam, nonwoven fabric, or a combination thereof. In addition to the above shapes, the current collector 110 can also be configured as a honeycomb structure, perforated sheet, woven or nonwoven mesh, sintered porous body, or three-dimensional interconnected network structure. These different shapes can be adjusted to optimize the surface area, mechanical strength, and current collection efficiency of the current collector 110.

[0368] Design of negative electrode current collector

[0369] In addition, the current collector 110 can be designed to adapt to different shape factors of solid-state batteries, such as pouch cells, cylindrical cells, or prismatic cells, each of which can provide advantages in terms of packaging efficiency, thermal management, and overall battery performance.

[0370] Thickness of negative electrode current collector

[0371] The thickness t5 of the current collector 110 may be or be about 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 11 μm, 12 μm, 13 μm, 14 μm, 15 μm, 16 μm, 17 μm, 18 μm, 19 μm, 20 μm, 21 μm, 22 μm, 23 μm, 24 μm, 25 μm, 26 μm, 27 μm, 28 μm, 29 μm, 30 μm, 31 μm, 32 μm, 33 μm, 34 μm, 35 μm, 36 μm, 37 μm, 38 μm, 39 μm, 40 μm, 41 μm, 42 μm, 43 μm, 44 μm, 45 μm, 46 μm, 47 μm, 48 μm, 49μm, 50 μm, 55 μm, 60 μm, 65 μm, 70 μm, 75 μm, 80 μm, 85 μm, 90 μm, 95 μm, 100 μm, 110 μm, 120 μm, 130 μm, 140 μm, 150 μm, 160 μm, 170 μm, 180 μm, 190 μm, 200 μm, 210 μm, 220 μm, 230 μm, 240 μm, 250 μm, 260 μm, 270 μm, 280 μm, 290 μm, 300 μm, 310 μm, 320 μm, 330 μm, 340 μm, 350 μm, 360 μm, 370 μm, 380 μm, 390 μm, 400 μm, 410 μm, 420 μm, 430 μm, 440 μm, 450 μm, 460 μm, 470 μm, 480 μm, 490 μm, or 500 μm. In some embodiments, the thickness t5 of the current collector 110 can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​from about 1 μm to about 500 μm (e.g., from about 5 μm to about 500 μm).

[0372] Method for manufacturing the negative electrode

[0373] The negative electrode 104 can be obtained by a variety of methods, such as atomic deposition, extrusion, calendering, slurry processing, or combinations thereof. In addition to the methods mentioned above, several other technologies can also be used to manufacture the negative electrode 104, including dry electrode processes. These alternative methods can offer advantages in terms of environmental impact, cost-effectiveness, and scalability.

[0374] Dry powder coating

[0375] Dry powder coating can be used as an alternative to the slurry method. In this process, the negative electrode active material, conductive additives, and binder are mixed in a dry state and then directly coated onto the current collector 110 using electrostatic deposition or mechanical pressing. This method can reduce the use of solvents and potentially reduce environmental impact.

[0376] 3D printing

[0377] Additive manufacturing technologies (such as 3D printing) can be used to fabricate the negative electrode 104. Depending on the specific material and desired electrode properties, various 3D printing methods can be employed, including fused deposition modeling (FDM), selective laser sintering (SLS), or direct ink writing (DIW). This method allows for precise control over the electrode structure and porosity.

[0378] electrospinning

[0379] Electrospinning is another potential method for manufacturing the negative electrode 104. In this process, a solution containing the negative electrode active material, conductive additives, and polymer binder is extruded through a nozzle under the influence of an electric field to form nanofibers. These fibers can be directly collected on the current collector 110 to form an electrode structure with high porosity and increased surface area.

[0380] Casting

[0381] Casting can be used to prepare the negative electrode 104. This technique involves spreading an electrode material slurry onto a moving carrier film using a doctor blade, followed by drying and calendering. The resulting electrode film can then be laminated onto the current collector 110.

[0382] Spraying

[0383] Spray coating technology can be used to prepare the negative electrode 104. A fine mist of electrode slurry is sprayed onto the current collector 110 using compressed air or ultrasonic atomization. This method can produce a thin and uniform electrode layer, making it particularly suitable for large-scale production.

[0384] Cryogenic casting

[0385] Cryo casting is another potential method for manufacturing the negative electrode 104. This process involves freezing an electrode material slurry, followed by the sublimation of ice to form a porous structure. The resulting porous electrode can then be sintered and attached to the current collector 110.

[0386] Sol-gel method

[0387] In some cases, the sol-gel method can be used to prepare the negative electrode 104. This method involves forming a colloidal suspension (sol) and then transforming it into a gel-like network containing the negative electrode active material and other components. This gel can be coated onto the current collector 110, followed by heat treatment to form the final electrode structure.

[0388] vapor deposition

[0389] For some applications, physical vapor deposition (PVD) or chemical vapor deposition (CVD) techniques can be used to directly produce thin-film anodes on the current collector 110. These methods can produce highly uniform and dense electrode layers, which may be particularly advantageous for certain types of solid-state batteries.

[0390] Alloying and ball milling

[0391] Mechanical alloying and high-energy ball milling can be used to prepare composite anode materials, which are then pressed into electrodes or coated onto current collector 110 using one of the methods described above. This technique is particularly useful for preparing nanostructured or amorphous anode materials with enhanced electrochemical properties.

[0392] Slurry method

[0393] For example, the negative electrode active material can be mixed and stirred with a solvent and optionally a binder and dispersant to form a slurry. The slurry is then coated (e.g., applied) onto the current collector 110, followed by pressing and drying to obtain the negative electrode 104.

[0394] Coating method of negative electrode slurry

[0395] The coating methods for the negative electrode 104 slurry can include techniques selected from the group consisting of: slot coating, gravure coating, spin coating, spray coating, roller coating, curtain coating, extrusion, casting, screen printing, inkjet printing, spray printing, gravure printing, thermal transfer printing, letterpress printing, intaglio printing, offset printing, and combinations thereof. In addition to the above techniques, other methods for coating the negative electrode slurry onto the current collector include doctor blade coating, dip coating, and meniscus coating.

[0396] Double-layer slot coating

[0397] Alternatively, a double-slit coating technique can be used, which allows two different electrode materials to be simultaneously coated onto the current collector through a single channel. This method has the potential to create a gradient structure within the electrode, thereby optimizing both electrochemical performance and mechanical properties.

[0398] Solvent for negative electrode slurry

[0399] Solvents used for forming the negative electrode 104 may include water and / or organic solvents such as N-methylpyrrolidone (NMP), dimethylformamide (DMF), acetone, dimethylacetamide, dimethyl sulfoxide (DMSO), isopropanol, and combinations thereof. The amount of solvent used should be sufficient to dissolve and disperse the electrode components (e.g., negative electrode active material and binder) considering slurry coating thickness, productivity, etc., or combinations thereof. Other usable organic solvents include ethanol, methanol, propanol, butanol, ethyl acetate, methyl ethyl ketone, tetrahydrofuran, diethyl ether, and toluene.

[0400] Solvent-free methods

[0401] In some implementations, solvent-free methods (such as dry powder processing or melt extrusion) can be used to prepare the negative electrode 104. These methods do not require the use of liquid solvents and offer environmental and cost advantages.

[0402] Dispersant for negative electrode slurry

[0403] The dispersant used to form the negative electrode 104 may include aqueous dispersants and / or organic dispersants, such as N-methyl-2-pyrrolidone. Other examples of aqueous dispersants may include sodium dodecyl sulfate (SDS), polyvinylpyrrolidone (PVP), and carboxymethyl cellulose (CMC), while other organic dispersants may include Triton X-100, polyethylene glycol (PEG), and various surfactants such as polysorbate or poloxamer.

[0404] Dispersant-free method

[0405] In some implementations, methods that do not require dispersants can be used to prepare the negative electrode 104, such as dry powder processing or certain additive manufacturing techniques.

[0406] Drying technology of negative electrode slurry

[0407] The negative electrode 104 slurry can be dried by irradiation with heat, electron beams (E-beams), gamma rays, or ultraviolet light (G-lines, H-lines, I-lines), causing the solvent to evaporate. For example, the slurry can be vacuum dried at room temperature. Although the drying step removes the solvent through evaporation, other components do not evaporate and remain intact to form the negative electrode 104.

[0408] Other drying techniques

[0409] Besides the drying techniques mentioned above, several other methods can also be used to dry negative electrode slurries. Depending on the specific materials, production requirements, and desired electrode performance, these other techniques can offer various advantages.

[0410] Infrared (IR) drying

[0411] Infrared (IR) drying can be used to rapidly heat the electrode surface, promoting efficient solvent evaporation. This method is particularly effective for thin electrode coatings and allows for precise control of the drying process.

[0412] Microwave drying

[0413] Microwave drying is another option, which can heat the electrode material in bulk, potentially achieving uniform drying across the entire electrode thickness. In some cases, convection drying and microwave drying can be combined to simultaneously optimize drying speed and uniformity.

[0414] freeze-drying

[0415] Freeze-drying (also known as lyophilization) can be used for specific electrode formulations. The process involves freezing the slurry and then sublimating the solvent under vacuum conditions. Freeze-drying helps maintain the porous structure of the electrode, which can be advantageous for electrolyte permeation and ion migration.

[0416] Supercritical CO2 drying

[0417] Supercritical CO2 drying is an advanced technique applicable to specialized electrode materials. The method involves replacing the solvent with liquid CO2, subsequently placing the CO2 in a supercritical state, and then venting it. This method helps preserve the fine nanostructures within the electrode, and is particularly useful for aerogel electrodes.

[0418] Two-step drying

[0419] In some cases, a two-step drying process can be employed. For example, a preliminary drying process can be carried out at a lower temperature to remove the main solvent, followed by a high-temperature step to remove residual solvent and potentially initiate the desired chemical reaction within the electrode material.

[0420] Ultrasonic drying

[0421] For specific electrode formulations, ultrasonic drying can also be considered. This technique utilizes high-frequency acoustic waves to agitate solvent molecules, potentially accelerating the drying process and improving solvent removal from the porous structure within the electrode.

[0422] Overview of solid electrolyte layers

[0423] The solid electrolyte layer 106 facilitates the diffusion of lithium ions between the positive electrode 102 and the negative electrode 104. The solid electrolyte layer 106 provides a conductive pathway for the migration of charge carriers between the positive electrode 102 and the negative electrode 104. The solid electrolyte layer 106 is electrically connected to both the positive electrode 102 and the negative electrode 104.

[0424] Location of solid electrolyte layer

[0425] In some embodiments, a solid electrolyte layer 106 is formed above and in direct contact with the positive electrode 102 or the negative electrode 104. In some embodiments, the solid electrolyte layer 106 is in direct contact with both the positive electrode 102 and the negative electrode 104. In other embodiments, an additional functional layer may be provided between the solid electrolyte layer 106 and the positive electrode 102 and / or the negative electrode 104.

[0426] Materials of solid electrolyte layers

[0427] The solid electrolyte layer 106 is capable of transporting lithium ions. The material of the solid electrolyte layer 106 is not particularly limited, as long as it can adhere to adjacent layers, has suitable conductivity, and does not cause significant chemical changes in the corresponding solid-state battery 100 within its voltage range. For example, the solid electrolyte layer 106 may comprise, but is not limited to, various inorganic solid electrolytes, polymer solid electrolytes, and / or polymer gel electrolytes. Alternatively or optionally, the solid electrolyte layer 106 may comprise, but is not limited to, ceramic electrolytes, glass electrolytes, organic-inorganic hybrid electrolytes, and nanostructured electrolytes.

[0428] Inorganic solid electrolytes

[0429] Inorganic solid electrolytes can include crystalline solid electrolytes, amorphous solid electrolytes, glass-ceramic solid electrolytes, or combinations thereof, but are not limited to these. Inorganic solid electrolytes can be sulfide-based, oxide-based, or combinations thereof. Besides sulfide and oxide-based inorganic solid electrolytes, other types of inorganic solid electrolytes can include halide electrolytes, nitride electrolytes, and borate electrolytes. For example, lithium-rich anti-perovskites (LiRAP) such as lithium oxychloride (Li3OCl) and lithium oxybromooxy (Li3OBr), lithium nitride (Li3N), and lithium borohydride (LiBH4) have been studied as potential solid electrolyte materials for lithium-ion batteries.

[0430] Sulfide solid electrolytes

[0431] Sulfide solid electrolytes contain sulfur (S) and have the ionic conductivity of Group I or Group II metals in the periodic table. They can include Li-PS type glasses or Li-PS type glass ceramics.

[0432] Examples of sulfide solid electrolytes

[0433] For example, sulfide-based solid electrolytes can include lithium sulfides, silicon sulfides, germanium sulfides, and boron sulfides. Specific examples of inorganic solid electrolytes can include Li... 3.833 Sn 0.833 As 0.166 S4, Li4SnS4, Li3.25 Ge 0.25 P 0.75 S4, Li2S-P2S0, B2S3-Li2S, 3+y PO 4-x N x ), thiolated LISICON (Li 3.25 Ge 0.25 P 0.75 S4), Li2O-Al2O3-TiO2-P2O5 (LATP), Li2S-P2S5, Li2S-LiI-P2S5, Li2S-LiI-Li2O-P2S5, Li2S-LiBr-P2S5, Li2S-Li2O-P2S5 , Li2S-Li3PO4-P2S5, Li2S-P2S5-P2O5, Li2S-P2S5-SiS2, Li2S-P2S5-SnS, Li2S-P2S5-Al2S3, Li2S-GeS2, Li2S-GeS2-ZnS, Li 10 GeP2S 12 (LGPS), Li7P3S 11 , Li6PS5Cl, Li6PS5Br, Li6PS5I, Li 9.54 Si 1.74 P 1.44 S 11.7 Cl 0.3 Li 11 Si2PS 12 etc., or combinations thereof.

[0434] Doped variants

[0435] In some cases, to further improve ionic conductivity or stability, doped variants of these materials (e.g., Al-doped Li) can be employed. 10 GeP2S 12 Or Sb-doped Li6PS5Cl).

[0436] Oxide solid electrolytes

[0437] Oxide-based solid electrolyte materials contain oxygen (O) and have the ionic conductivity of Group I or Group II metals in the periodic table.

[0438] Examples of oxide-based solid electrolyte materials

[0439] Oxide-based solid electrolyte materials may include at least one selected from the group consisting of: LLTO compounds, Li6La2CaTa2O 12 Li6La2ANb2O 12 (A is Ca or Sr), Li2Nd3TeSbO 12 Li3BO 2.5 N 0.5 Li9SiAlO8, LAGP compounds, LATP compounds, Li 1+x Ti 2-x Al x Si y (PO4) 3-y (0≤x≤1, 0≤y≤1), LiAl x Zr 2-x (PO4)3(0≤x≤1, 0≤y≤1), LiTi x Zr 2-x (PO4)3 (0≤x≤1, 0≤y≤1), LISICON-type compounds, LIPON-type compounds, perovskite-type compounds, NASICON-type compounds, and LLZO-type compounds or derivatives (e.g., Al-doped Li7La3Zr2O). 12 Ta-doped Li7La3Zr2O 12 Lithium-rich anti-perovskites such as Li3OCl and Li3OBr have also been studied as potential oxide-based solid electrolytes.

[0440] Composite oxide electrolyte

[0441] In some cases, to fully utilize the advantages of different oxide systems, composite oxide electrolytes combining multiple oxide materials (such as LLZO-LATP composite materials) can be used.

[0442] Polymer solid electrolyte

[0443] Polymer solid electrolytes are complexes of electrolyte salts and polymer resins, exhibiting lithium-ion conductivity. Polymer solid electrolytes may include polyether polymers, polycarbonate polymers, acrylate polymers, polysiloxane polymers, phosphazene polymers, polyethylene derivatives, epoxyalkane derivatives, phosphate / ester polymers, polylyzed lysine, polyester sulfides, polyvinyl alcohol, polyvinylidene fluoride, polymers containing groups that can dissociate into ions, polyethylene imine (PEI), polymethyl methacrylate (PMMA), polyacrylonitrile (PAN), polyethylene succinate (PES), biopolymers (e.g., chitosan and cellulose derivatives), or combinations thereof.

[0444] Polymer resin for polymer solid electrolytes

[0445] Polymer solid electrolytes may comprise polymer resins, such as branched copolymers (comprising a polyethylene oxide (PEO) backbone copolymerized with comonomers comprising amorphous polymers such as PMMA, polycarbonate, polydimethylsiloxane (PDMS), and / or phosphazene), comb polymers, crosslinked polymer resins, polyethylene glycol (PEG), polypropylene oxide (PPO), polyacrylonitrile (PAN), polymethyl methacrylate (PMMA), polyvinylidene fluoride-hexafluoropropylene copolymer (PVDF-HFP), polyethylene oxide-polypropylene oxide copolymer (PEO-PPO), polyethylene imine (PEI), polyvinylpyrrolidone (PVP), polyvinyl alcohol (PVA), various block copolymers or graft copolymers comprising these materials, or combinations thereof.

[0446] Polymer gel electrolyte

[0447] Polymer gel electrolytes can be formed by adding an organic electrolyte containing an organic solvent and an electrolyte salt, an ionic liquid, a monomer, or an oligomer to a polymer resin or a combination thereof. Polymer resins used for polymer gels may include polyether polymers, PVC polymers, PMMA polymers, polyacrylonitrile (PAN), polyvinylidene fluoride (PVDF), polyvinylidene fluoride-hexafluoropropylene copolymer (PVDF-co-HFP), or combinations thereof.

[0448] Examples of polymer gel electrolytes

[0449] To optimize the electrochemical and physical properties of solid electrolytes, examples of polymeric gel electrolytes suitable for solid-state batteries include gel electrolytes based on polyethylene oxide (PEO), polymethyl methacrylate-ethyl acrylate copolymer (PMMA-EA), polyacrylonitrile-methyl methacrylate copolymer (PAN-MMA), polyvinyl acetate (PVAc), polyethylene glycol diacrylate (PEGDA), polyvinylpyrrolidone (PVP), polyethylene glycol methyl ether acrylate (PEGMEA), polyethylene glycol methyl ether methacrylate (PEGMEMA), polyionic liquid (PIL), polyethylene glycol-polypropylene glycol copolymer (PEG-PPG), polyvinyl alcohol-ethylene copolymer (PVA-PE), polyacrylamide (PAM), poly(2-hydroxyethyl methacrylate) (PHEMA), polyethylene glycol-polyethylene oxide copolymer (PEG-PEO), and polymethyl methacrylate (PMAA).

[0450] Electrolyte salts

[0451] The electrolyte salt is an ionizable lithium salt, which can be represented as Li + X - X -It can include anions selected from the group consisting of: F - Cl - ,Br - NO3 - N(CN)2 - BF4 - ClO4 - AlO4 - AlCl4 - PF6 - SbF6 - AsF6 - BF2C2O4 - BC4O8 - (CF3)2PF4 - (CF3)3PF3 - (CF3)4PF2 - (CF3)5PF - (CF3)6P - CF3SO3 - C4F9SO3 - CF3CF2SO3 - (CF3SO2)2N - (F2SO2)2N - CF3CF2(CF3)2CO - (CF3SO2)2CH, CF3(CF2)7SO3 - CF3CO2 - CH3CO2 - SCN - (CF3CF2SO2)2N - wait.

[0452] Examples of lithium salts

[0453] For example, lithium salts can be any one selected from the group consisting of: LiTFSI, LiCl, LiBr, LiI, LiClO4, lithium tetrafluoroborate (LiBF4), LiB 10 Cl 10Lithium hexafluorophosphate (LiPF6), LiAsF6, LiSbF6, LiAlCl4, LiSCN, LiCF3CO2, LiCH3SO3, LiCF3SO3, LiN(SO2CF3)2, LiN(SO2C2F5)2, LiC4F9SO3, LiC(CF3SO2)3, (CF3SO2)2NLi, lithium chloroborate, lower aliphatic carboxylic acid lithium salts, lithium 4-phenylborate imide, lithium bis(oxalate)borate (LiBOB), lithium difluoro(oxalate)borate (LiDFOB), lithium bis(fluorosulfonyl)imide (LiFSI), lithium 4,5-dicyano-2-(trifluoromethyl)imidazolium (LiTDI), lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), and lithium bis(fluorosulfonyl)imide (LiFSI), or combinations thereof. Electrolyte salts may include any combination of the salts described herein.

[0454] Electrolyte salt content

[0455] Based on the total weight of the solid electrolyte layer 106, the content of electrolyte salts in the solid electrolyte layer 106 can be or approximately 0 parts, 10 parts, 20 parts, 30 parts, 40 parts, 50 parts, 60 parts, 70 parts, 80 parts, 90 parts, 100 parts, 110 parts, 120 parts, 130 parts, 140 parts, 150 parts, 160 parts, 170 parts, 180 parts, 190 parts, 200 parts, 210 parts, 220 parts, 230 parts, 240 parts, 250 parts, 260 parts, 270 parts, 280 parts, 290 parts, 300 parts, 310 parts, 320 parts, 330 parts, 340 parts, 350 parts, 360 parts, 370 parts, 380 parts, 390 parts, or 400 parts. In some embodiments, the content of electrolyte salts in the solid electrolyte layer 106, based on the total weight of the solid electrolyte layer 106, can be within a range formed by selecting any two values ​​listed above, or within a range formed by selecting any two values ​​from about 0 parts to about 400 parts or from about 60 parts to 400 parts.

[0456] ionic conductivity of solid electrolyte layer

[0457] The solid electrolyte layer 106 can possess suitable reduction stability and / or ionic conductivity. Since the solid electrolyte layer 106 primarily functions to transport lithium ions between the electrodes, it can therefore possess ideal ionic conductivity, which is approximately or greater than, for example, 10⁻⁶. -7 S / cm, 10 -6 S / cm, 10 -5 S / cm or 10 -4 S / cm.

[0458] Thickness of solid electrolyte layer

[0459] The thickness t6 of the solid electrolyte layer 106 may be or be about 1 μm, 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 15 μm, 20 μm, 25 μm, 30 μm, 35 μm, 40 μm, 45 μm, 50 μm, 55 μm, 60 μm, 65 μm, 70 μm, 75 μm, 80 μm, 85 μm, 90 μm, 95 μm, 100 μm, 110 μm, 120 μm, 130 μm, 140 μm, 150 μm, 160 μm, 170 μm, 180 μm, 190 μm, 200 μm, 210 μm, 220 μm, 230 μm, 240 μm, 250 μm, 260μm, 270 μm, 280 μm, 290 μm, 300 μm, 310 μm, 320 μm, 330 μm, 340 μm, 350 μm, 360 μm, 370 μm, 380 μm, 390 μm, 400 μm, 410 μm, 420 μm, 430 μm, 440 μm, 450 μm, 460 μm, 470 μm, 480 μm, 490 μm, 500 μm, 510 μm, 520 μm, 530 μm, 540 μm, 550 μm, 560 μm, 570 μm, 580 μm, 590 μm, 600 μm, 610 μm, 620 μm, 630 μm, 640 μm, 650 μm, 660 μm, 670 μm, 680 μm, 690 μm, 700 μm, 710 μm, 720 μm, 730 μm, 740 μm, 750 μm, 760 μm, 770 μm, 780 μm, 790 μm, 800 μm, 810 μm, 820 μm, 830 μm, 840 μm, 850 μm, 860 μm, 870 μm, 880 μm, 890 μm, 900 μm, 910 μm, 920 μm, 930 μm, 940 μm, 950 μm, 960 μm, 970 μm, 980 μm, 990 μm or 1000 μm. In some embodiments, the thickness t6 of the solid electrolyte layer 106 can be within a range formed by selecting any two numbers listed above, or within a range formed by selecting any two numbers from 0 to about 1000 μm (e.g., about 5 μm to about 1000 μm, about 30 μm to about 100 μm, or about 30 μm to about 50 μm).

[0460] semi-finished products

[0461] Figure 13 The shown cell 101 may be provided as a semi-finished product. In some embodiments, the cell 101 is stored, transported, and / or delivered to distributors, customers, etc., who complete the manufacturing of a battery assembly or product containing the cell 101. In other embodiments, the cell 101 is a finished battery assembly or product.

[0462] Battery sealing

[0463] The casing 112 of the solid-state battery can be sealed to complete the manufacture of the solid-state battery 100, enabling it to function as a battery. The sealing process can employ various techniques to ensure that internal components are protected from external environmental factors and to maintain the integrity of the battery structure. For example, methods such as laser welding, ultrasonic welding, or adhesive bonding can be used to achieve an airtight seal for the casing 112. In some cases, the sealing process may also include introducing a protective atmosphere or removing air to create a vacuum environment within the casing. This sealing step helps prevent moisture ingress, as moisture can degrade the performance of sulfide-based solid electrolytes. Furthermore, the sealing process can incorporate safety features such as pressure relief mechanisms to address potential gas accumulation issues during battery operation.

[0464] After the battery is sealed

[0465] After proper sealing, the solid-state battery 100 undergoes final quality control testing, including electrical testing, leakage detection, and visual inspection. Following these tests, the solid-state battery 100 can be packaged and sold as a finished product, and can be integrated into various electronic devices, electric vehicles, energy storage systems, etc.

[0466] Battery Structure

[0467] Solid-state batteries 100 are offered in a variety of configurations to suit different applications and device requirements. In some cases, the battery can be made in a cylindrical form, which can be advantageous for certain types of portable electronic devices or automotive applications. Alternatively, solid-state batteries 100 can be made in a prismatic form, allowing for more efficient use of space in devices with rectangular form factors. In other cases, a pouch form can be adopted, offering flexibility in shape and the potential to reduce the overall weight of the battery. The pouch form is particularly suitable for solid-state batteries because it makes it easier to apply and control uniform pressure inside the battery.

[0468] Construction choice

[0469] The choice of configuration can be determined by factors such as intended use, space constraints, thermal management requirements, and manufacturing considerations. In some implementations, hybrid or custom configurations combining different forms of elements can be employed as needed. The diversity of battery form factors enables all-solid-state batteries to be integrated into a wide range of products, from small wearable devices to large energy storage systems.

[0470] Voltage

[0471] Solid state battery 100 is configured to have an output voltage of at or about 1 V, 2 V, 3 V, 4 V, 5 V, 6 V, 7 V, 8 V, 9 V, 10 V, 11 V, 12 V, 13 V, 14 V, 15 V, 16 V, 17 V, 18 V, 19 V, 20 V, 21 V, 22 V, 23 V, 24 V, 25 V, 26 V, 27 V, 28 V, 29 V, 30 V, 35 V, 40 V, 45 V, 48 V, 50 V, 55 V, 60 V, 65 V, 70 V, 75 V, 80 V, 85 V, 90 V, 95 V, 96 V, 100 V, 110 V, 120 V, 130 V, 140 V, 150 V, 160 V, 170 V, 180 V, 190 V, 200 V, 210 V, 220 V, 230 V, 240 V, 250 V, 260 V, 270 V, 280 V, 290 V, 300 V, 310 V, 320 V, 330 V, 340 V, 350 V, 360 V, 370 V, 380 V, 390 V, 400 V, 410 V, 420 V, 430 V, 440 V, 450 V, 460 V, 470 V, 480 V, 490 V, or 500 V. In some embodiments, the output voltage of the solid-state battery 100 can be within a range formed by selecting any two numbers listed above, or within a range formed by selecting any two numbers from 0 to about 500 V (e.g., from 1 V DC to about 500 V DC).

[0472] capacity

[0473] The solid-state battery 100 can be configured to have the following capacities: approximately or greater than 100 mAh / g, 110 mAh / g, 120 mAh / g, 130 mAh / g, 140 mAh / g, 150 mAh / g, 160 mAh / g, 170 mAh / g, 180 mAh / g, 190 mAh / g, 200 mAh / g, 210 mAh / g, 220 mAh / g, 230 mAh / g, 240 mAh / g, 250 mAh / g, 260 mAh / g, 270 mAh / g, 280 mAh / g, 290 mAh / g, or 300 mAh / g. In some embodiments, the capacity of the solid-state battery 100 may be within a range formed by selecting any two of the numbers listed above, or may be within a range formed by selecting any two of the numbers from 0 to 300 mAh / g or from 0 to about 300 mAh / g (e.g., from about 100 mAh / g to about 300 mAh / g).

[0474] Calculation of volume expansion rate

[0475] The solid-state battery 100 can be configured to have an ideal volumetric expansion rate. The volumetric expansion rate can be calculated by comparing the change in thickness after the first charge-discharge cycle with the initial thickness. The volumetric expansion rate is the ratio of the change in thickness to the initial thickness. The first charge-discharge cycle is performed as follows: the battery is charged at 0.1C CC-CV, cut off at 4.25V to 4.4V and then at 0.02C; then discharged at 0.1C CC, cut off at 3V. The volumetric expansion rate is calculated using Equation 1 below, where A represents the thickness before charge-discharge and B represents the thickness after charge-discharge. The thickness can be measured using a Mauser micrometer or a scanning electron microscope (SEM).

[0476] Equation 1: Volume expansion rate = [(BA) / A] × 100 C-fold The C-rate used in this article refers to the discharge rate of the battery relative to its maximum capacity. For example, a 1C rate means that the discharge current can completely discharge the battery in one hour. That is, for a battery with a capacity of 20 amp-hours, the discharge current at a 1C rate is 20 amps.

[0477] Other examples of volume expansion rate

[0478] Other methods for measuring and calculating the volume expansion rate of solid-state batteries may include volume expansion measurement methods (e.g., gas specific gravity bottle method), in-situ dilatometer method, X-ray tomography, strain gauge measurement method, optical methods (e.g., digital image correlation or laser interferometry), pressure-based methods, and electrochemical strain microscopy.

[0479] Embodiments of the present invention

[0480] The embodiments will be described more fully below to facilitate a better understanding of the invention. However, the following embodiments are for illustrative purposes only, and the scope of the invention is not limited thereto.

[0481] Example 1

[0482] Example 1.1: Input

[0483] Provide the following input value: The density of CAM is 4.7 g / cm³. 3 The Young's modulus is 195 GPa. The density of SE is 1.74 g / cm³. 3 The Young's modulus is 25 GPa. The density of both the binder and carbon is 2.0 g / cm³. 3 The sum of their masses accounts for 1% of the total mass. CAM has a normal distribution with D5 = 3 μm, D50 = 4 μm, and D95 ​​= 5 μm. SE has distribution parameters of D5 = 1 μm, D50 = 3 μm, and D95 ​​= 7.8 μm.

[0484] Example 1.2: Pretreatment

[0485] A particle size distribution matching the actual experimental distribution is generated. These particles are then randomly arranged in the simulation space to simulate the actual mixing process. To balance accuracy and computational efficiency, 10 discrete sizes are used to characterize the experimental distribution for each CAM and SE (e.g., LPSCl). After the initial particle arrangement is set, the calendering process is simulated using LAMMPS particle packs. A total force (375 MPa multiplied by the simulated cross-sectional area) is applied to the system and distributed proportionally according to the volume fraction of each component (CAM and SE). This force is then further distributed between CAM and SE particles. The interparticle contact force is calculated using a pre-built hertz / material particle contact model in LAMMPS and is described by the following equation:

[0486] Among them, Young's modulus (E) and radius (R) are effective parameters, δ ij is the overlap distance, and n is the normalized vector separating the centers of the two particles.

[0487] The simulation was performed in discrete time steps. Within each time step, the resultant force acting on each particle was calculated. Knowing the mass of each particle, its acceleration was determined using the velocity-Verlet time integration algorithm. Due to the explicit nature of the simulation, a 5×10⁻⁶ time step was used to ensure numerical stability. -5The simulation is performed in second-second (equivalent to 1 / 20,000 of a second) time steps. The simulation continues until the microstructure reaches a stable state. At this point, particle data is extracted from the LAMMPS dump file. This data is then post-processed to analyze inter-particle connectivity, thereby determining key indicators such as CAM utilization and tortuosity.

[0488] Since the Hertzian contact model does not involve plastic deformation, an overlap criterion is adopted. The overlap between "connected" particles must exceed 2% of the sum of their radii to ensure sufficient contact between particles.

[0489] To accurately characterize the particle size distribution, 10 discrete sizes were considered for each material, and each simulation used a cross-sectional area of ​​20μm×20μm to balance model complexity and computational efficiency.

[0490] The target CAM content is set to 66%. The overlap criterion is set to 100%. The simulation model is iterated until the CAM content reaches the target CAM content. The preprocessing stage generates the following in the simulation chamber: Figure 14A The particles are shown. The pressure used in the compression simulation chamber was set to 375 MPa. The calculated force on each CAM particle was 233638 pg·μm / μs², and the force on each SE particle was 315205 pg·μm / μs².

[0491] Example 1.3: Processing

[0492] The compression simulation box generated during the processing stage, such as Figure 14B As shown.

[0493] Example 1.4: Post-processing

[0494] The first target evaluation metric is CAM utilization, measured by calculating the percentage of CAM with any overlap in the connected SE mass body in contact with the current collector. Secondly, the Li content of the LPSCl pathway in the cathode composite is measured. + Transmission tortuosity.

[0495] The calculated CAM utilization rate is 94%. Tortuosity is the ratio of the tortuous path length (red line) to the Euclidean distance between particles on opposite sides (blue line), as shown in the visualization. Figure 14C As shown, the calculated tortuosity is 1.86. The calculated porosity is 12.3%.

[0496] Example 2

[0497] Using different input values, Example 1 was repeated 3 to 100 times (e.g., 10 times) to establish a database of positive electrode composition.

[0498] Example 3

[0499] Using different input values, repeat Examples 1-2 to build a database of negative electrode composition.

[0500] Example 4

[0501] Example 4.1: Selection of positive electrode composition

[0502] Select the positive electrode composition from the database established in Example 2.

[0503] Example 4.2: Selecting the negative electrode composition

[0504] Select the negative electrode composition from the database established in Example 3.

[0505] Example 4.3: Manufacturing an all-solid-state battery

[0506] All-solid-state batteries were fabricated using the electrode compositions selected in Examples 4.1 to 4.2.

[0507] Included combinations and characteristics

[0508] This specification describes various features and characteristics to facilitate understanding of the composition, structure, manufacture, function, and / or operation of the invention, including the disclosed components, coatings, and methods. It should be understood that the various features and characteristics of the invention described herein can be combined in any suitable manner, whether or not they are explicitly described as combinable in this specification. The inventors and applicant expressly intend that combinations of these features and characteristics be included within the scope of protection of the invention as described herein. Therefore, the claims may be modified to reference any feature and characteristic explicitly or inherently described in this specification, or any feature and characteristic otherwise explicitly or inherently supported in this specification, in any combination thereof. Furthermore, the applicant reserves the right to amend the claims to explicitly exclude features and characteristics that may exist in the prior art, even if such features and characteristics are not explicitly described in this specification. Therefore, any such amendments will not add new content to the specification or claims and will comply with the requirements of drafting, adequate disclosure, and addition of content.

[0509] By incorporating references

[0510] The entire contents of any patent, publication, or other document referenced in this specification are incorporated herein by reference only, unless otherwise stated, but only to the extent that the incorporated material does not conflict with the existing descriptions, definitions, statements, illustrations, or other disclosed material expressly set forth in this specification. Therefore, where necessary, the disclosure expressly set forth in this specification takes precedence over any conflicting material incorporated by reference. Any material or portion thereof incorporated by reference in this specification that conflicts with existing definitions, statements, or other disclosed material set forth herein shall be incorporated only to the extent that the incorporated material does not conflict with the existing disclosure. The applicant reserves the right to amend this specification to expressly reference any subject matter or portion thereof incorporated by reference. Amending this specification to add such incorporated subject matter will meet the requirements of specification drafting, adequate disclosure, and addition of content.

[0511] Explanation from all aspects

[0512] While various specific aspects of the invention have been described to illustrate its various aspects and / or its potential applications, it should be understood that various changes and modifications will occur to those skilled in the art. Therefore, the invention herein should be understood to be at least as broad as the scope defined by the claims, and not narrowly defined by the specific illustrative aspects provided herein.

Claims

1. A method for manufacturing an all-solid-state lithium battery, the method comprising: Provide a set of input parameters, the input parameters including: The weight percentage (SE_wt%) and density (SE_density) of the solid electrolyte material containing spherical solid electrolyte (SE) particles; and The weight percentage (EAM_wt%) and density (EAM_density) of the electrode active material containing spherical electrode active material (EAM) particles; The volume percentages (EAM_vol) of the electrode active material and the volume percentages (SE_vol) of the SE are provided below: EAM_vol= (EAM_wt% / EAM_density) / (EAM_wt% / EAM_density + SE_wt% / SE_density), SE_vol= (SE_wt% / SE_density) / (EAM_wt% / EAM_density + SE_wt% / SE_density); The probability values ​​for the EAM are provided below: (EAM_vol / EAM_pvol) / (EAM_vol / EAM_pvol + SE_vol / SE_pvol), Wherein, EAM_pvol is the average volume of the spherical EAM particle, SE_pvol is the average volume of the spherical SE particle, and the probability value represents the number of spherical SE particles corresponding to each spherical EAM particle; The probability values ​​of the EAM are used to generate simulation bin data representing simulation bins divided into discrete spaces. The simulation bins contain the spherical EAM particles and the spherical SE particles in randomly selected discrete spaces, and the simulation bin data indicates whether each discrete space is occupied by one of the spherical EAM particles or one of the spherical SE particles. The simulation box data is processed to generate compressed simulation box data, which indicates that the simulation box is compressed along the vertical axis between the top and bottom and not compressed in any other direction, such that the spherical EAM particles and spherical SE particles are each in contact with at least one adjacent particle. The data from the compression simulation chamber is processed to generate an adjacency matrix representing connected spherical SE particles, each of which is in contact with or overlaps with at least one adjacent spherical SE particle. The relative tortuosity of the spherical SE particles inside the compression simulation chamber is obtained, including: The adjacency matrix data is processed to generate path data, which is used to determine a path that passes through the center of the connected spherical SE particles in a direction roughly from top to bottom, with each path extending from the top connected spherical SE particle to the bottom connected spherical SE particle. Process path data, determine and store target path data representing the target path; and Extract length data, which represents the target path length from the center of the top-connected spherical SE particle on the target path to the center of the bottom-connected spherical SE particle on the target path; Extract Euclidean distance data, which represents the Euclidean distance between top-connected spherical SE particles and bottom-connected spherical SE particles in the target path; and The relative tortuosity is obtained by processing the target path length and the Euclidean distance; Repeat the above steps to prepare a database containing multiple sets of input parameters and their corresponding relative tortuosity; Using the corresponding relative tortuosity, input parameters for the target group are filtered from the database; and The all-solid-state lithium battery is prepared using the input parameters of the target group.

2. The method as described in claim 1, wherein, The set of input parameters also includes one or more of the following: The particle size information of the spherical EAM particles; The particle size information of the spherical SE particles; The weight percentage and density of carbon materials; and The weight percentage and density of the adhesive.

3. The method as described in claim 2, wherein, The all-solid-state lithium battery comprises a first electrode, a second electrode, and an SE layer. The steps for fabricating the all-solid-state lithium battery include: The first electrode is prepared using the input parameters of the target group; Provide the second electrode; The carbon material and the binder are provided using the input parameters of the target group; and The SE layer is prepared using the input parameters of the target group.

4. The method of claim 2, wherein, The particle size information of the spherical EAM particles includes at least one of the average diameter or particle size distribution of the spherical EAM particles.

5. The method of claim 2, wherein, The particle size information of the spherical SE particles includes at least one of the average diameter or particle size distribution of the spherical SE particles.

6. The method of claim 1, wherein, The set of input parameters includes the particle size distribution of the spherical EAM particles and the particle size distribution of the spherical SE particles.

7. The method of claim 6, further comprising generating dimensional data representing the size of the spherical EAM particles and the spherical SE particles in the simulation chamber using the particle size distribution of the spherical EAM particles and the particle size distribution of the spherical SE particles.

8. The method of claim 6, wherein, The particle size distribution of the spherical EAM particles and the particle size distribution of the spherical SE particles are each selected from a group consisting of a normal distribution, a log-normal distribution, a skewed normal distribution, a skewed distribution, and a bimodal distribution.

9. The method of claim 1, wherein, The adjacency matrix data represents an adjacency matrix, wherein, The adjacency matrix is ​​a Z×Z matrix, where Z is the number of the spherical SE particles; For every two adjacent spherical SE particles that are in contact or overlapping, the adjacency matrix data has a value of "1", where the sum of the radii of the two adjacent spherical SE particles that are in contact or overlapping is greater than or equal to the overlap criterion; and For any two adjacent spherical SE particles that do not touch or overlap, the adjacency matrix data has a value of "0", where the sum of the radii of the two adjacent spherical SE particles that do not touch or overlap is less than the overlap criterion.

10. The method of claim 9, wherein, The overlap criterion is 100% to 105% of the Euclidean distance between two adjacent spherical SE particles that are in contact or overlap.

11. The method of claim 9, wherein, The overlap criterion is 102% of the Euclidean distance between two adjacent spherical SE particles that are in contact or overlap.

12. The method of claim 1, wherein, The set of input parameters includes additional input parameters, which are selected from the following group: The SE material has at least one of Young's modulus, Poisson's ratio, coefficient of friction, or coefficient of restitution. The EAM has at least one of Young's modulus, Poisson's ratio, coefficient of friction or coefficient of restitution; The dimensions of the simulation box; The number of particles on each side of the simulation box; and The combination of the above parameters.

13. The method of claim 1, wherein, The step of processing the simulation chamber data to generate the compression simulation chamber data includes processing pressure data representing a predetermined pressure applied to the simulation chamber.

14. The method of claim 12, further comprising calculating the total force applied to the simulation chamber by multiplying the predetermined pressure by the cross-sectional area of ​​the simulation chamber.

15. The method of claim 14, further comprising calculating the force on each particle, including: The total force is decomposed into a first force acting on all spherical EAM particles and a second force acting on all spherical SE particles as follows: First force = Total force × EAM_vol Second force = Total force × SE_vol; Divide the first force by the total number of the spherical EAM particles; and Divide the second force by the total number of the spherical SE particles.

16. The method of claim 1, wherein, The EAM is a positive electrode active material.

17. The method of claim 3, further comprising calculating at least one of the porosity, EAM utilization, or SE utilization of the first electrode.

18. The method of claim 1, further comprising calculating the EAM utilization rate, including: The adjacency matrix data is processed to generate basic SE data representing connected spherical SE particles of the base, wherein each of the connected spherical SE particles of the base contacts or overlaps with the connected spherical SE particles of the adjacent base, and at least one connected spherical SE particle of the base contacts the bottom of the simulation chamber. The adjacency matrix data is processed to generate EAM data representing connected spherical EAM particles, wherein each connected spherical EAM particle is in contact with at least one basic connected spherical SE particle. The EAM data is processed to generate first volume data representing the total volume of the connected spherical EAM particles; Process the simulation box data to generate second volume data representing the total volume of all spherical EAM particles; and Divide the total volume of connected spherical EAM particles by the total volume of all spherical EAM particles.

19. The method of claim 3, further comprising calculating the porosity of the first electrode by: The total volume of the first electrode is calculated by multiplying the predetermined height of the simulation box by the cross-sectional area of ​​the simulation box; The total volume of solid in the first electrode is calculated by adding the volume of the EAM to the volume of the SE material. The void volume is calculated by subtracting the total volume of the solid from the total volume of the first electrode; and Divide the void volume by the total volume of the first electrode.

20. An electric vehicle comprising an all-solid-state battery manufactured by the method of claim 1.

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