Rechargeable battery lithium deposition detection method, system and battery system

Abstract

The present invention relates to the technical field of data mining, and in particular to a detection method, system and battery system for lithium plating in a rechargeable battery. The method comprises the following steps: obtaining and extracting the structural data of the rechargeable battery to obtain the microscopic structural data of the battery and the structural data of the battery components; analyzing the structural data of the battery components to obtain the topological structure model of the rechargeable battery; obtaining the charge and discharge cycle data of the rechargeable battery, parameterizing the microscopic structural data of the battery and the charge and discharge cycle data of the rechargeable battery, and obtaining the battery energy potential field model; simulating the topological structure model of the rechargeable battery and the battery energy potential field model to obtain the simulation data of the battery ion migration trajectory; constructing a lithium plating detection model for the simulation data of the battery ion migration trajectory; obtaining and predicting the real-time sensor data of the rechargeable battery to obtain the prediction data of the dynamic behavior of lithium ions; optimizing the real-time sensor data of the rechargeable battery to obtain the optimized charging parameters. The present invention can improve the safety of the rechargeable battery.

Description

Technical Field

[0001] The present invention relates to the technical field of data mining, and in particular to a detection method and system for lithium deposition in a rechargeable battery, and a battery system. Background Art

[0002] With the popularity of electric vehicles and portable devices, lithium-ion batteries have been widely used as energy storage devices with high energy density and long life. However, lithium-ion batteries may undergo lithium plating during use, that is, the lithium metal in lithium-ion batteries is deposited on the electrode surface during charging to form lithium dendrites or lithium particles, which may lead to a decline in battery performance, safety issues, and shorten the battery life. Traditional lithium plating detection methods usually require physical or chemical analysis after the battery is discharged, which is not only time-consuming and labor-intensive, but may also damage the battery and affect its ability to be reused or continued to be used. This reliance on performance consumption and high operational complexity limits its real-time nature and scope of application. At the same time, existing methods often find it difficult to achieve real-time monitoring of lithium plating behavior inside the battery. The lithium ion deposition process inside the battery may be dynamic, so it is necessary to be able to capture and analyze these changes in real time so that timely measures can be taken to prevent battery damage or safety accidents. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a detection method, system and battery system for lithium deposition in a rechargeable battery to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for detecting lithium deposition in a rechargeable battery comprises the following steps:

[0005] Step S1: acquiring rechargeable battery structure data, and extracting battery microstructure features and battery component structure features from the rechargeable battery structure data, thereby obtaining battery microstructure data and battery component structure data; performing topological structure analysis based on the battery component structure data, thereby obtaining a rechargeable battery topological structure model;

[0006] Step S2: acquiring the charge and discharge cycle data of the rechargeable battery, performing microscopic atomic structure modeling according to the battery microstructure data, thereby obtaining a battery microstructure model, and performing battery energy potential field parameterization on the battery microstructure model based on the charge and discharge cycle data of the rechargeable battery, thereby obtaining a battery energy potential field model;

[0007] Step S3: Model coupling is performed on the rechargeable battery topology model and the battery energy potential field model to obtain the battery energy field model, and multi-scale molecular dynamics simulation is performed according to the battery energy field model to obtain battery ion migration trajectory simulation data;

[0008] Step S4: performing charge and discharge cycle lithium ion trajectory analysis on the rechargeable battery charge and discharge cycle data and the battery ion migration trajectory simulation data, thereby obtaining lithium ion migration trajectory data, and constructing a lithium precipitation detection model based on the lithium ion migration trajectory data;

[0009] Step S5: acquiring real-time rechargeable battery sensor data, and predicting the dynamic behavior of lithium ions on the real-time rechargeable battery sensor data through a lithium plating detection model, thereby obtaining lithium ion dynamic behavior prediction data;

[0010] Step S6: Optimize charging parameters according to the lithium ion dynamic behavior prediction data and the real-time rechargeable battery sensor data to obtain optimized charging parameters, and transmit the optimized charging parameters to the rechargeable battery charging control platform to execute the charging control task.

[0011] By analyzing the microstructure and component structure of the battery, the present invention can deeply understand the physical properties and geometric shapes inside the battery, and provide basic data for subsequent model building. Based on the structural data of the battery components, the topological structure model of the battery is established, which helps to understand the connection and action relationship between the various parts inside the battery, and provides an accurate model framework for subsequent simulation. Using the battery microstructure data, a specific atomic level model is established, which can describe the atomic arrangement and chemical environment inside the battery in detail. The battery microstructure model is parameterized by the energy potential field according to the charge and discharge cycle data, and these parameters can reflect the dynamic changes of the charge and energy distribution inside the battery. The topological structure model and the energy potential field model are coupled to establish a comprehensive battery energy field model, which can describe the energy transmission and distribution inside the battery. Through multi-scale molecular dynamics simulation, the specific trajectory and dynamic changes of ion migration inside the battery can be simulated and captured in real time, so as to deeply understand the behavior of lithium ions in the battery. Based on the actual charge and discharge cycle data and ion migration trajectory simulation data, the lithium precipitation phenomenon is analyzed and identified, and the lithium precipitation detection model is constructed and optimized. By using the lithium ion migration trajectory obtained from the simulation data, the dynamic behavior of lithium ions in the battery can be accurately predicted and detected, which is helpful for real-time monitoring and management of battery performance. Through the lithium plating detection model, the real-time collected battery sensor data is analyzed and predicted, and the dynamic behavior changes of lithium ions are monitored in real time, so as to respond and adjust the charging strategy in time. Using the predicted lithium ion dynamic behavior data, the charging parameters are optimized, such as adjusting the charging current, voltage and other parameters to improve the charging efficiency and safety of the battery. The optimized charging parameters are transmitted to the charging control platform to implement the charging control task to ensure that the battery is charged with high efficiency and safety, and to extend the battery life and stability. In summary, this systematic approach can not only realize the real-time monitoring and analysis of the lithium plating behavior inside the battery, but also improve the performance and safety of the battery by optimizing the charging parameters, so as to better meet the needs of electric vehicles and portable devices for high-performance lithium-ion batteries.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: acquiring the rechargeable battery structure data, and performing battery microstructure feature extraction and battery component structure feature extraction on the rechargeable battery structure data, thereby obtaining the battery microstructure data and battery component structure data;

[0014] Step S12: performing a topological structure analysis based on the battery assembly structure data to obtain a battery topological structure model;

[0015] Step S13: classifying component materials based on the battery microstructure data, thereby obtaining battery component material data;

[0016] Step S14: performing material property analysis on the battery component material data, thereby obtaining the battery component material property data;

[0017] Step S15: mapping the material properties of the battery topology model according to the material characteristic data of the battery assembly, thereby obtaining the rechargeable battery topology model.

[0018] By acquiring the structural data of the rechargeable battery, the present invention can gain an in-depth understanding of the physical structure of the battery, including components such as electrodes and electrolytes. This is crucial for further analyzing the performance, life and safety of the battery. The topological structure analysis of the battery can help understand the layout and connection of its internal components. This is very important for optimizing battery design, improving energy efficiency and reducing energy loss. Using battery microstructure data for component material classification helps to determine the type and properties of each part of the material. This lays the foundation for subsequent material property analysis and helps to select the most suitable material for a specific application. By analyzing the characteristics of battery component materials, the electrochemical properties, stability and durability of the materials can be understood. This information is the key to optimizing battery performance and selecting appropriate materials. Mapping the battery component material property data to the topological structure model can help predict the behavior of the battery under different operating conditions. This model can be used to optimize battery design, predict performance and perform virtual testing, thereby saving development time and cost.

[0019] Optionally, step S12 is specifically:

[0020] Step S121: performing three-dimensional structural modeling on the battery assembly structural data, thereby obtaining a three-dimensional structural model of the battery assembly;

[0021] Step S122: extracting connection structure features from the three-dimensional structure model of the battery assembly, thereby obtaining connection structure data of the battery assembly;

[0022] Step S123: Associating the battery assembly connection relationship according to the battery assembly connection structure data, thereby obtaining the battery assembly connection relationship data;

[0023] Step S124: Perform a topological structure analysis based on the battery assembly connection relationship data to obtain a battery topological structure model.

[0024] The present invention can accurately express the position, shape and size of each component inside the battery by three-dimensional modeling of the structural data of the battery assembly. This provides accurate basic data for subsequent connection structure feature extraction and topological structure analysis. Extracting connection structure features from the three-dimensional structure model can describe in detail the connection mode and layout between the components inside the battery. These features are crucial to understanding the structural integrity, thermal management and current transmission of the battery. Establishing battery assembly connection relationship data can help understand the physical relationship between different components, such as how the modular structure of the battery pack affects the overall performance and reliability. This association helps to optimize the design, improve safety and reduce failure rates. Through topological structure analysis, the layout of the components inside the battery, the circuit path and the energy flow can be studied in depth. This analysis helps to discover potential optimization solutions and improve the performance, efficiency and life of the battery.

[0025] Optionally, step S2 specifically includes:

[0026] Step S21: acquiring charging and discharging cycle data of the rechargeable battery, and performing time alignment on the charging and discharging cycle data of the rechargeable battery, thereby obtaining the battery charging and discharging cycle data;

[0027] Step S22: performing atomic structure classification on the battery microstructure data to obtain an atomic structure data set;

[0028] Step S23: performing molecular dynamics simulation based on the atomic structure data set to obtain molecular dynamics simulation data;

[0029] Step S24: performing molecular dynamics coupling on the rechargeable battery topological structure model according to the molecular dynamics simulation data, thereby obtaining a battery microstructure model;

[0030] Step S25: Parameterize the battery energy potential field of the battery microstructure model based on the battery charge and discharge cycle data, so as to obtain a battery energy potential field model.

[0031] The present invention collects actual data of a rechargeable battery during the charge and discharge process. The accuracy and comparability of the data are ensured by time-aligning the charge and discharge cycle data. These data are essential for analyzing the charge and discharge behavior, capacity attenuation, and performance changes of the battery. By performing atomic structure classification on the microstructure data of the battery, the arrangement and organization of atoms in the battery material can be understood in detail. This classification helps to identify defects, interfaces, and chemical reaction sites in the material, thereby guiding the subsequent molecular dynamics simulation and the construction of the energy potential field model. Molecular dynamics simulation allows the movement and interaction of materials to be simulated at the atomic level, thereby obtaining information on the dynamic behavior and stability of the material. Through this simulation, the mechanical properties, thermodynamic properties, and charge transport mechanisms of the material can be studied, providing in-depth insights into the microscopic processes inside the battery. Coupling the results of the molecular dynamics simulation with the topological structure model of the rechargeable battery can achieve a correlation from the atomic level to the overall structure. Such a coupling analysis helps to verify the accuracy of the model, evaluate the stability and durability of the material, and optimize the design and use conditions of the material. Applying the charge and discharge cycle data to the energy potential field parameterization of the microstructure model can accurately describe the energy changes and potential energy surfaces of the internal battery materials during the charge transport process. This model can predict the battery's performance degradation mechanism, charge transfer pathways and their limiting conditions, providing a basis for optimizing the battery's electrochemical performance.

[0032] Optionally, step S25 is specifically:

[0033] Step S251: performing atomic structure characteristic analysis on the atomic structure data set to obtain atomic structure characteristic data;

[0034] Step S252: obtaining a potential field model set, and performing a model adaptability evaluation on the potential field model set based on the battery component material characteristic data and the atomic structure characteristic data, thereby obtaining model adaptability data;

[0035] Step S253: selecting the most advantageous field model from the potential field model set according to the model adaptability data, thereby obtaining an initial potential field model;

[0036] Step S254: parameterize and adjust the initial potential field model according to the battery charge and discharge cycle data, so as to obtain a battery energy potential field model.

[0037] The present invention can obtain key atomic structure characteristic data by performing a detailed analysis of the atomic structure data of the battery material. These data include information such as interatomic distance, bond length, bond angle, lattice parameters, etc., which help understand the basic structure of the material and the strength of the chemical bond. These characteristic data are crucial for the subsequent establishment of molecular dynamics models and energy potential field models because they determine the accuracy and applicability of the models. Different sets of potential field models are collected and prepared. Then, these potential field models are evaluated and compared in detail using the material characteristic data of the battery components and the obtained atomic structure characteristic data. The evaluation process includes the accuracy, computational efficiency, applicability and description ability of the model for specific battery materials. These evaluation data provide a basis for selecting the most advantageous field model, ensuring that subsequent simulations and analyses can accurately reflect the actual situation. Based on the model adaptability data obtained in the steps, the optimal potential field model is determined. Selecting the optimal model means that the structure, dynamics and chemical properties of the battery material can be accurately described while ensuring computational efficiency. The results of this step directly affect the subsequent model optimization and parameterization process, ensuring the reliability and practicality of the simulation results. The parameterization adjustment of the initial potential field model is to optimize the model according to the actual battery charge and discharge cycle data. By adjusting the parameters of the model, such as the force field constant of the potential field or other interaction terms, the model can better capture the behavior and performance changes of the battery under different charge and discharge conditions. This parameterized adjustment can improve the predictive ability of the model, make the simulation results more consistent with the experimental data, and provide effective tools and guidance for battery design and optimization.

[0038] Optionally, step S3 specifically includes:

[0039] Step S31: spatially aligning the rechargeable battery topological structure model and the battery energy potential field model to obtain a spatially aligned model set;

[0040] Step S32: performing model coupling on the spatial alignment model set to obtain a battery energy field model;

[0041] Step S33: performing multi-scale molecular dynamics simulation on the battery energy field model to obtain multi-scale molecular dynamics simulation data;

[0042] Step S34: extracting battery ion migration trajectory features from the multi-scale molecular dynamics simulation data, thereby obtaining battery ion migration trajectory simulation data.

[0043] In the present invention, the topological structure model of the rechargeable battery and the battery energy potential field model are spatially aligned. This means that the physical structure of the battery and its energy potential field are precisely aligned in space to ensure that the model can accurately describe the structure and energy distribution inside the battery. Through spatial alignment, a set of accurate model sets can be established to provide a consistent basis for subsequent simulation and analysis. The spatially aligned model set is model coupled, that is, each model is integrated into a comprehensive battery energy field model. Such coupling can comprehensively utilize the advantages of each model to provide a more comprehensive and accurate description of the battery energy field. Through coupling, the interaction between the various parts inside the battery can be comprehensively considered to further improve the fidelity and predictive ability of the model. Multiscale molecular dynamics simulation is a simulation method based on different time and length scales for studying the dynamic behavior of battery materials. Through this simulation, the structure, energy changes and reaction kinetics of battery materials can be deeply understood from the perspective of atoms and molecules. These simulation data provide detailed information on the internal processes of the battery, such as ion movement, material deformation, etc. during charging and discharging. The migration trajectory characteristics of battery ions are extracted from multiscale molecular dynamics simulation data. These data show the specific movement path and kinetic characteristics of ions inside the battery, which is the key to understanding the ion transport mechanism during battery charging and discharging. By analyzing these trajectories, the ionic conductivity and charge and discharge efficiency of battery materials can be evaluated, providing an important reference for designing more efficient and stable batteries.

[0044] Optionally, step S33 is specifically:

[0045] Step S331: setting simulation conditions based on battery charge and discharge cycle data to obtain simulation condition data;

[0046] Step S332: classifying the simulation condition data into simulation types according to the battery component material characteristic data and the atomic structure characteristic data, thereby obtaining electronic scale simulation condition data, atomic scale simulation condition data and molecular scale simulation condition data;

[0047] Step S333: performing electron-scale molecular dynamics simulation on the battery energy field model according to the electron-scale simulation condition data, thereby obtaining electron dynamics simulation data;

[0048] Step S334: performing atomic scale molecular dynamics simulation on the battery energy field model according to the atomic scale simulation condition data, thereby obtaining atomic dynamics simulation data;

[0049] Step S335: performing ion-scale molecular dynamics simulation on the battery energy field model according to the molecular-scale simulation condition data, thereby obtaining ion dynamics simulation data;

[0050] Step S336: performing scale simulation coupling on the electron dynamics simulation data, the atomic dynamics simulation data and the ion dynamics simulation data according to the battery energy field model, thereby obtaining multi-scale molecular dynamics simulation data.

[0051] The present invention uses actual data of battery charge and discharge cycles to set simulation conditions. These data reflect the behavior of the battery during actual use, such as current density, temperature change, voltage curve, etc. By setting conditions based on measured data, the authenticity and reliability of the simulation are ensured, so that subsequent simulations can better reflect the performance and characteristics of the battery in actual operation. The simulation condition data are classified according to the characteristics of battery component materials and atomic structure characteristics. The electronic scale, atomic scale and molecular scale simulation condition data obtained after classification are simulated at different scales to more accurately study the characteristics of battery materials at all levels. For example, electronic scale simulation can provide information about electronic structure and energy band characteristics; atomic scale simulation can reveal the interaction and structural dynamics between atoms; molecular scale simulation pays more attention to the interaction and dynamic behavior between molecules. Molecular dynamics simulations are performed at electronic scale, atomic scale and ion scale respectively. Through these simulations, detailed dynamic data at different scales can be obtained, including the energy state of electrons, the position and movement of atoms, the migration path of ions, etc. These data are the key to understanding the internal structure and behavior of battery materials, and help optimize the design and performance of batteries, and improve their energy density, cycle life and safety. The simulation data at the electronic scale, atomic scale and ion scale are coupled to form multi-scale molecular dynamics simulation data. Through this coupling, information at each scale can be integrated to achieve a more comprehensive and consistent understanding of battery materials and behaviors. This not only improves the accuracy of the simulation, but also helps reveal the mutual influence and synergy between different scales in the complex mechanism inside the battery.

[0052] Optionally, step S4 is specifically:

[0053] Step S41: performing lithium ion migration trajectory statistics on the battery ion migration trajectory simulation data, thereby obtaining high-frequency lithium ion migration trajectory data;

[0054] Step S42: performing simulation condition association on the high-frequency lithium ion migration trajectory data and the rechargeable battery charge and discharge cycle data, thereby obtaining lithium ion migration condition data;

[0055] Step S43: performing a timing analysis of lithium ion migration trajectories under the same conditions based on the lithium ion migration condition data and the high-frequency lithium ion migration trajectory data, thereby obtaining lithium ion migration timing data;

[0056] Step S44: performing migration trajectory statistics on the lithium ion migration time series data, thereby obtaining lithium ion migration trajectory data;

[0057] Step S45: constructing a lithium plating detection model based on the lithium ion migration condition data and the lithium ion migration trajectory data.

[0058] The present invention can obtain high-frequency lithium ion migration trajectory data by statistically analyzing the battery ion migration trajectory simulation data. These data reflect the actual migration path and frequency of lithium ions in the battery, helping to reveal the dynamic behavior inside the battery. After the high-frequency lithium ion migration trajectory data is correlated with the charge and discharge cycle data, the conditional data of lithium ion migration can be obtained. These data guide the subsequent analysis, so that the migration behavior of lithium ions can be closely related to the actual charge and discharge cycle conditions, improving the authenticity and applicability of the simulation. Using the lithium ion migration conditional data and the high-frequency migration trajectory data, the timing analysis of the lithium ion migration trajectory under the same conditions is performed. This step can generate the migration of lithium ions at different time points, including detailed information such as speed change and position change, to help understand the dynamic process inside the battery. Further statistical analysis of the lithium ion migration timing data can obtain the trajectory data of lithium ion migration. These data can not only be used to verify the accuracy of the model, but also provide important references for subsequent battery design and optimization, especially in understanding battery life, performance attenuation mechanism, etc. A lithium precipitation detection model is constructed based on lithium ion migration conditional data and migration trajectory data. This model can predict the location and amount of lithium plating in the battery, helping to detect problems or potential risks in the battery in advance, and then improve battery design or adjust usage strategies, thereby extending battery life and improving safety.

[0059] Optionally, the present specification also provides a detection system for lithium deposition in a rechargeable battery, which is used to perform the detection method for lithium deposition in a rechargeable battery as described above, and the detection system for lithium deposition in a rechargeable battery comprises:

[0060] A topological structure analysis module is used to obtain the rechargeable battery structure data, and to extract the battery microstructure characteristics and battery component structure characteristics of the rechargeable battery structure data, so as to obtain the battery microstructure data and battery component structure data; and to perform topological structure analysis based on the battery component structure data, so as to obtain the rechargeable battery topological structure model;

[0061] An energy potential field parameterization module is used to obtain the charge and discharge cycle data of the rechargeable battery, perform microscopic atomic structure modeling according to the battery microstructure data, thereby obtaining a battery microstructure model, and perform battery energy potential field parameterization on the battery microstructure model based on the charge and discharge cycle data of the rechargeable battery, thereby obtaining a battery energy potential field model;

[0062] The molecular dynamics simulation module is used to couple the rechargeable battery topology model and the battery energy potential field model to obtain the battery energy field model, and to perform multi-scale molecular dynamics simulation based on the battery energy field model to obtain battery ion migration trajectory simulation data;

[0063] The lithium ion trajectory analysis module is used to perform charge and discharge cycle lithium ion trajectory analysis on the charge and discharge cycle data of the rechargeable battery and the battery ion migration trajectory simulation data, so as to obtain the lithium ion migration trajectory data, and build a lithium precipitation detection model based on the lithium ion migration trajectory data;

[0064] The lithium ion dynamic behavior prediction module is used to obtain real-time rechargeable battery sensor data, and predict the lithium ion dynamic behavior of the real-time rechargeable battery sensor data through the lithium precipitation detection model, so as to obtain the lithium ion dynamic behavior prediction data;

[0065] The charging parameter optimization module is used to optimize the charging parameters according to the lithium-ion dynamic behavior prediction data and the real-time charging battery sensor data, so as to obtain the optimized charging parameters, and transmit the optimized charging parameters to the charging control platform of the rechargeable battery to perform the charging control task.

[0066] The detection system for lithium deposition in a rechargeable battery of the present invention can implement any detection method for lithium deposition in a rechargeable battery of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the detection method for lithium deposition in a rechargeable battery. The internal modules of the system cooperate with each other, thereby improving the battery performance of the rechargeable battery.

[0067] Optionally, the present specification also provides a battery system, including a rechargeable battery and a rechargeable battery lithium deposition detection system applied to the rechargeable battery, the rechargeable battery lithium deposition detection system being used to execute a rechargeable battery lithium deposition detection method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0069] Figure 1 It is a schematic flow chart of the steps of the detection method of lithium deposition in a rechargeable battery of the present invention;

[0070] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0071] Figure 3 Detailed step flow diagram of step S2 in the present invention.

[0072] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0073] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0074] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0075] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0076] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for detecting lithium deposition in a rechargeable battery, the method comprising the following steps:

[0077] Step S1: acquiring rechargeable battery structure data, and extracting battery microstructure features and battery component structure features from the rechargeable battery structure data, thereby obtaining battery microstructure data and battery component structure data; performing topological structure analysis based on the battery component structure data, thereby obtaining a rechargeable battery topological structure model;

[0078] In this embodiment, high-resolution scanning electron microscopy (SEM) and transmission electron microscopy (TEM) are used to obtain microstructure data of rechargeable battery materials. These data include the particle size, shape, and distribution of the electrode material and the pore structure in the electrolyte. The chemical composition of the electrode material and its distribution in the battery assembly are confirmed by X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) analysis. Image processing software such as ImageJ or Matlab can be used to process and analyze microstructure images and extract features such as particle size distribution, electrode layer thickness, etc.

[0079] Step S2: acquiring the charge and discharge cycle data of the rechargeable battery, performing microscopic atomic structure modeling according to the battery microstructure data, thereby obtaining a battery microstructure model, and performing battery energy potential field parameterization on the battery microstructure model based on the charge and discharge cycle data of the rechargeable battery, thereby obtaining a battery energy potential field model;

[0080] In this embodiment, the charge and discharge cycle data of the battery in actual use are collected through a battery management system (BMS) or a dedicated data recording device. These data include the changes in the voltage, current, and temperature of the battery over time. Using these data, combined with the battery microstructure data obtained in step S1, an atomic-level structural model of the battery is established using methods such as molecular dynamics simulation (MD) or density functional theory (DFT). The energy potential field of the battery is parameterized to describe the movement and interaction of ions in the battery material.

[0081] Step S3: Model coupling is performed on the rechargeable battery topology model and the battery energy potential field model to obtain the battery energy field model, and multi-scale molecular dynamics simulation is performed according to the battery energy field model to obtain battery ion migration trajectory simulation data;

[0082] In this embodiment, the battery topology model is coupled with the energy potential field model. This coupling forms a comprehensive energy field model of the battery, which can perform multi-scale molecular dynamics simulations. The movement and diffusion behavior of lithium ions in battery materials are simulated at the atomic level, as well as the battery performance at the macroscopic level. This method can deeply explore the ion transport path, diffusion rate and its changes in the charge and discharge cycle inside the battery.

[0083] Step S4: performing charge and discharge cycle lithium ion trajectory analysis on the rechargeable battery charge and discharge cycle data and the battery ion migration trajectory simulation data, thereby obtaining lithium ion migration trajectory data, and constructing a lithium precipitation detection model based on the lithium ion migration trajectory data;

[0084] In this embodiment, the migration trajectory of lithium ions in the charge and discharge cycle is analyzed by using the battery ion migration trajectory simulation data. By analyzing the position and velocity distribution of ions in the electrode material and electrolyte, the local area causing lithium precipitation is identified. A lithium precipitation detection model is constructed, and the model is trained in combination with a machine learning algorithm to predict the lithium precipitation that occurs in actual use. These models take into account the physicochemical properties of battery materials and their changes under different working conditions.

[0085] Step S5: acquiring real-time rechargeable battery sensor data, and predicting the dynamic behavior of lithium ions on the real-time rechargeable battery sensor data through a lithium plating detection model, thereby obtaining lithium ion dynamic behavior prediction data;

[0086] In this embodiment, the constructed lithium plating detection model is used, combined with the sensor data of parameters including battery temperature, voltage, current, etc. obtained from the rechargeable battery sensor in real time, to predict the dynamic behavior of lithium ions. These data are compared and analyzed with the model in real time to identify potential lithium plating risks. According to the output of the model, the risk level of lithium plating under the current battery operating state is evaluated, including predicting local high temperature areas or voltage abnormalities in the battery, which are precursors to lithium plating. The prediction results can be used to adopt timely charging control strategies to avoid unstable or damaged battery performance.

[0087] Step S6: Optimize charging parameters according to the lithium ion dynamic behavior prediction data and the real-time rechargeable battery sensor data to obtain optimized charging parameters, and transmit the optimized charging parameters to the rechargeable battery charging control platform to execute the charging control task.

[0088] In this embodiment, the charging parameters are optimized based on the lithium ion dynamic behavior prediction data and the real-time charging battery sensor data. This includes adjusting the charging current, voltage and charging time reflected by the real-time charging battery sensor data according to the lithium ion dynamic behavior prediction data to maximize the battery life and optimize its performance. The optimized charging parameters are transmitted to the battery management system (BMS) or a dedicated charging control platform to perform the actual charging control tasks. These control strategies are designed to ensure the safety and stability of the battery under various operating conditions while maximizing its energy efficiency and service life.

[0089] By analyzing the microstructure and component structure of the battery, the present invention can deeply understand the physical properties and geometric shapes inside the battery, and provide basic data for subsequent model building. Based on the structural data of the battery components, the topological structure model of the battery is established, which helps to understand the connection and action relationship between the various parts inside the battery, and provides an accurate model framework for subsequent simulation. Using the battery microstructure data, a specific atomic level model is established, which can describe the atomic arrangement and chemical environment inside the battery in detail. The battery microstructure model is parameterized by the energy potential field according to the charge and discharge cycle data, and these parameters can reflect the dynamic changes of the charge and energy distribution inside the battery. The topological structure model and the energy potential field model are coupled to establish a comprehensive battery energy field model, which can describe the energy transmission and distribution inside the battery. Through multi-scale molecular dynamics simulation, the specific trajectory and dynamic changes of ion migration inside the battery can be simulated and captured in real time, so as to deeply understand the behavior of lithium ions in the battery. Based on the actual charge and discharge cycle data and ion migration trajectory simulation data, the lithium precipitation phenomenon is analyzed and identified, and the lithium precipitation detection model is constructed and optimized. By using the lithium ion migration trajectory obtained from the simulation data, the dynamic behavior of lithium ions in the battery can be accurately predicted and detected, which is helpful for real-time monitoring and management of battery performance. Through the lithium plating detection model, the real-time collected battery sensor data is analyzed and predicted, and the dynamic behavior changes of lithium ions are monitored in real time, so as to respond and adjust the charging strategy in time. Using the predicted lithium ion dynamic behavior data, the charging parameters are optimized, such as adjusting the charging current, voltage and other parameters to improve the charging efficiency and safety of the battery. The optimized charging parameters are transmitted to the charging control platform to implement the charging control task to ensure that the battery is charged with high efficiency and safety, and to extend the battery life and stability. In summary, this systematic approach can not only realize the real-time monitoring and analysis of the lithium plating behavior inside the battery, but also improve the performance and safety of the battery by optimizing the charging parameters, so as to better meet the needs of electric vehicles and portable devices for high-performance lithium-ion batteries.

[0090] Optionally, step S1 specifically includes:

[0091] Step S11: acquiring the rechargeable battery structure data, and performing battery microstructure feature extraction and battery component structure feature extraction on the rechargeable battery structure data, thereby obtaining the battery microstructure data and battery component structure data;

[0092] In this embodiment, the structural data of the rechargeable battery is obtained, including its physical structure, component configuration, etc. These data can be obtained by three-dimensional scanning technology, electron microscope scanning, and CAD drawings. Then, the microstructure feature extraction and component structure feature extraction are performed on these structural data. For example, the battery cross section is analyzed using image processing technology to extract structural features of each level such as electrode gap, conductive path, etc. Through these analyses, detailed battery microstructure data and component structure data can be obtained for subsequent analysis and modeling.

[0093] Step S12: performing a topological structure analysis based on the battery assembly structure data to obtain a battery topological structure model;

[0094] In this embodiment, it is necessary to perform a topological structure analysis based on the obtained battery component structure data. This includes determining the connection mode, hierarchical relationship of the components inside the battery, and the overall structural layout of the battery. Using network analysis tools and CAD software, a topological structure model of the battery can be established to show the physical connection and data flow path between the components. This model helps to understand the overall operation mechanism and optimize the design of the battery.

[0095] Step S13: classifying component materials based on the battery microstructure data, thereby obtaining battery component material data;

[0096] In this embodiment, classification of component materials based on the obtained microstructure data is a key step. This can use machine learning algorithms to identify and classify different material components, such as positive electrode, negative electrode, electrolyte, etc. Through this classification, the quantity, distribution and location information of each material in the battery can be obtained, thereby forming detailed battery component material data.

[0097] Step S14: performing material property analysis on the battery component material data, thereby obtaining the battery component material property data;

[0098] In this embodiment, it is crucial to perform material property analysis on the obtained battery component material data. This includes measuring and analyzing the physical and chemical properties of each material, such as conductivity, stability, thermal performance, etc. This can be done through experimental testing and simulation calculations to obtain detailed property data of various materials, providing a basis for subsequent model building and performance prediction.

[0099] Step S15: mapping the material properties of the battery topology model according to the material characteristic data of the battery assembly, thereby obtaining the rechargeable battery topology model.

[0100] In this embodiment, based on the material property data of the battery components, these properties are mapped to the topological structure model of the battery. This means associating the material property data of each component with its position and role in the topological structure model. For example, high conductivity materials are used for the electrode part, high stability materials are used for the separator, etc. This mapping not only helps optimize the battery design, but also predicts its performance under different working conditions.

[0101] By acquiring the structural data of the rechargeable battery, the present invention can gain an in-depth understanding of the physical structure of the battery, including components such as electrodes and electrolytes. This is crucial for further analyzing the performance, life and safety of the battery. The topological structure analysis of the battery can help understand the layout and connection of its internal components. This is very important for optimizing battery design, improving energy efficiency and reducing energy loss. Using battery microstructure data for component material classification helps to determine the type and properties of each part of the material. This lays the foundation for subsequent material property analysis and helps to select the most suitable material for a specific application. By analyzing the characteristics of battery component materials, the electrochemical properties, stability and durability of the materials can be understood. This information is the key to optimizing battery performance and selecting appropriate materials. Mapping the battery component material property data to the topological structure model can help predict the behavior of the battery under different operating conditions. This model can be used to optimize battery design, predict performance and perform virtual testing, thereby saving development time and cost.

[0102] Optionally, step S12 is specifically:

[0103] Step S121: performing three-dimensional structural modeling on the battery assembly structural data, thereby obtaining a three-dimensional structural model of the battery assembly;

[0104] In this embodiment, the obtained battery component structure data is used for three-dimensional modeling. This process involves using CAD software or similar tools to convert the various components, layers, geometric shapes and positional relationships of the battery into a visual three-dimensional model. For example, for lithium-ion batteries, the structural information of the positive electrode, negative electrode, electrolyte and separator can be converted into a specific three-dimensional model. Through this step, the complexity and precision of the internal structure of the battery can be accurately represented, laying the foundation for the subsequent connection structure feature extraction.

[0105] Step S122: extracting connection structure features from the three-dimensional structure model of the battery assembly, thereby obtaining connection structure data of the battery assembly;

[0106] In this embodiment, for the established three-dimensional structure model of the battery assembly, the extraction of connection structure features is a key task. This includes identifying and analyzing the connection mode between the various components, interface design, and physical connection details. For example, through the tools of CAD software, the gap between the electrode and the electrolyte layer, the contact area and its conductive path, or the separation distance between the diaphragm and the electrode can be detected. The extraction of these features can provide the necessary accuracy and detail information for the subsequent connection relationship data.

[0107] Step S123: Associating the battery assembly connection relationship according to the battery assembly connection structure data, thereby obtaining the battery assembly connection relationship data;

[0108] In this embodiment, it is necessary to establish the connection relationship between battery components using the extracted connection structure data. This involves converting the connection features identified in the three-dimensional model, such as welding, pressing, bonding, etc., into a specific data representation. For example, the physical connection relationship between the electrode and the electrolyte is recorded as a specific data point, describing the actual physical contact or separation between them. These associated data are key information for analyzing the overall performance of the battery and optimizing the design.

[0109] Step S124: Perform a topological structure analysis based on the battery assembly connection relationship data to obtain a battery topological structure model.

[0110] In this embodiment, topological structure analysis based on the obtained battery component connection relationship data is a key step in realizing the battery topological structure model. This includes understanding the logical and physical connections between the components inside the battery through network analysis tools or graph theory methods. For example, a battery topological structure model is formed by analyzing the electrical paths, current distribution, and information transmission routes between battery components. This model can reveal the complex relationships of the internal structure of the battery and provide important guidance and foundation for battery performance optimization and system-level design.

[0111] The present invention can accurately express the position, shape and size of each component inside the battery by three-dimensional modeling of the structural data of the battery assembly. This provides accurate basic data for subsequent connection structure feature extraction and topological structure analysis. Extracting connection structure features from the three-dimensional structure model can describe in detail the connection mode and layout between the components inside the battery. These features are crucial to understanding the structural integrity, thermal management and current transmission of the battery. Establishing battery assembly connection relationship data can help understand the physical relationship between different components, such as how the modular structure of the battery pack affects the overall performance and reliability. This association helps to optimize the design, improve safety and reduce failure rates. Through topological structure analysis, the layout of the components inside the battery, the circuit path and the energy flow can be studied in depth. This analysis helps to discover potential optimization solutions and improve the performance, efficiency and life of the battery.

[0112] Optionally, step S2 specifically includes:

[0113] Step S21: acquiring charging and discharging cycle data of the rechargeable battery, and performing time alignment on the charging and discharging cycle data of the rechargeable battery, thereby obtaining the battery charging and discharging cycle data;

[0114] In this embodiment, the charge and discharge cycle data of the rechargeable battery are collected. These data generally include physical parameters such as voltage, current, and temperature during the charge and discharge process. The data can be acquired through laboratory testing, battery management system records, or sensor collection. Subsequently, these data are time-aligned to ensure the consistency of the time axis of different data sources for subsequent analysis. For example, the data in the charge and discharge cycle are aligned to a unified time point through a timestamp or synchronization signal. This process is the basis for establishing a rechargeable battery behavior model and predicting performance changes.

[0115] Step S22: performing atomic structure classification on the battery microstructure data to obtain an atomic structure data set;

[0116] In this embodiment, the atomic structure needs to be classified for the microstructure data of the battery. This involves using molecular dynamics simulation or other structural characterization methods to classify and organize data such as atomic distribution, bonding type, and lattice parameters in battery materials. For example, for the positive and negative electrode materials of lithium-ion batteries, the position of lithium atoms in the crystal structure, the lattice arrangement of oxides, etc. can be classified and analyzed. These classified data sets help to understand the structural characteristics of materials and their impact on battery performance.

[0117] Step S23: performing molecular dynamics simulation based on the atomic structure data set to obtain molecular dynamics simulation data;

[0118] In this embodiment, molecular dynamics simulation is performed using the classified atomic structure data set. This simulation can simulate the motion behavior of battery materials at the atomic scale, including the vibration, diffusion and interaction of atoms. By solving Newton's equations of motion by numerical methods, data such as the atomic position and velocity of time evolution can be obtained. For example, the diffusion path and reaction kinetics of lithium ions in electrode materials can be studied by molecular dynamics simulation. These simulation data provide an important theoretical basis for understanding the structural stability and electrochemical properties of battery materials.

[0119] Step S24: performing molecular dynamics coupling on the rechargeable battery topological structure model according to the molecular dynamics simulation data, thereby obtaining a battery microstructure model;

[0120] In this embodiment, the molecular dynamics simulation data obtained can be coupled with the topological structure model of the rechargeable battery. This means mapping the atomic position and motion information obtained in the molecular dynamics simulation to the macroscopic battery structure model. For example, the atomic model of the electrode material is matched to the size and geometry of the actual battery component to establish a microscopic structure model of the battery. This helps to understand the connection between the electrochemical reaction mechanism at the atomic level and the macroscopic battery performance.

[0121] Step S25: Parameterize the battery energy potential field of the battery microstructure model based on the battery charge and discharge cycle data, so as to obtain a battery energy potential field model.

[0122] In this embodiment, the energy potential field parameterization of the established battery microstructure model is performed using the acquired battery charge and discharge cycle data. This means that the energy changes in the battery charge and discharge process measured experimentally are mapped to the potential energy function used in the molecular dynamics simulation. For example, according to the voltage change and current data during the charge and discharge process, the potential energy parameters in the model are adjusted to more accurately reflect the electrochemical reaction energy barrier and energy changes of the battery material. The battery energy potential field model obtained in this way can provide theoretical support for predicting the stability, capacity decay and cycle life of the battery.

[0123] The present invention collects actual data of a rechargeable battery during the charge and discharge process. The accuracy and comparability of the data are ensured by time-aligning the charge and discharge cycle data. These data are essential for analyzing the charge and discharge behavior, capacity attenuation, and performance changes of the battery. By performing atomic structure classification on the microstructure data of the battery, the arrangement and organization of atoms in the battery material can be understood in detail. This classification helps to identify defects, interfaces, and chemical reaction sites in the material, thereby guiding the subsequent molecular dynamics simulation and the construction of the energy potential field model. Molecular dynamics simulation allows the movement and interaction of materials to be simulated at the atomic level, thereby obtaining information on the dynamic behavior and stability of the material. Through this simulation, the mechanical properties, thermodynamic properties, and charge transport mechanisms of the material can be studied, providing in-depth insights into the microscopic processes inside the battery. Coupling the results of the molecular dynamics simulation with the topological structure model of the rechargeable battery can achieve a correlation from the atomic level to the overall structure. Such a coupling analysis helps to verify the accuracy of the model, evaluate the stability and durability of the material, and optimize the design and use conditions of the material. Applying the charge and discharge cycle data to the energy potential field parameterization of the microstructure model can accurately describe the energy changes and potential energy surfaces of the internal battery materials during the charge transport process. This model can predict the battery's performance degradation mechanism, charge transfer pathways and their limiting conditions, providing a basis for optimizing the battery's electrochemical performance.

[0124] Optionally, step S25 is specifically:

[0125] Step S251: performing atomic structure characteristic analysis on the atomic structure data set to obtain atomic structure characteristic data;

[0126] In this embodiment, the collected atomic structure data set is analyzed in detail to obtain the atomic structure characteristic data of the battery material. This includes but is not limited to lattice parameters, atomic spacing, bond length, bond angle, crystal symmetry and other characteristics. For example, for the positive and negative electrode materials of lithium-ion batteries, the lattice type (such as cubic lattice, hexagonal lattice), lattice constant (a, b, c), interatomic distance (such as the distance between lithium atoms and oxygen atoms), lattice defects (such as vacancies, impurities), etc. can be analyzed. Through these characteristic data, the structural stability and electrochemical performance of battery materials can be deeply understood.

[0127] Step S252: obtaining a potential field model set, and performing a model adaptability evaluation on the potential field model set based on the battery component material characteristic data and the atomic structure characteristic data, thereby obtaining model adaptability data;

[0128] In this embodiment, it is necessary to obtain multiple potential field models, for example, based on molecular force fields (such as UFF, REAXFF) or quantum chemical methods (such as potential energy surfaces calculated by DFT), and based on the characteristic data of the battery component materials and the atomic structure characteristic data obtained in step S251, these potential field models are evaluated. The evaluation may include aspects such as the accuracy, computational efficiency, and scope of application of the model. For example, by comparing with experimental data or high-precision calculation results, the performance of the potential field model in describing the structure and kinetic properties of battery materials is evaluated. The goal of this step is to screen out the most suitable set of potential field models for the current research problem.

[0129] Step S253: selecting the most advantageous field model from the potential field model set according to the model adaptability data, thereby obtaining an initial potential field model;

[0130] In this embodiment, based on the obtained model adaptability data, the optimal potential field model is selected as the initial potential field model. This selection is usually based on the accuracy and computational efficiency of the model in describing the characteristics of battery materials. For example, the potential field model that performs best in terms of the atomic structure characteristics of battery materials and the accuracy of dynamic simulation can be selected. The key to this step is to ensure that the selected model can provide accurate structural and dynamic information, laying the foundation for subsequent model adjustment and electrochemical performance prediction.

[0131] Step S254: parameterize and adjust the initial potential field model according to the battery charge and discharge cycle data, so as to obtain a battery energy potential field model.

[0132] In this embodiment, the selected initial potential field model is parameterized using the obtained battery charge and discharge cycle data. This includes adjusting the potential energy function parameters in the model to better reflect the energy changes and electrochemical reaction kinetics during the actual battery charge and discharge process. For example, according to the voltage and current curves in the battery charge and discharge cycle, the bond length, bond angle parameters or charge distribution in the potential field model are optimized to improve the predictive ability and adaptability of the model. The battery energy potential field model finally obtained can be used to predict the structural stability, electrochemical performance and cycle life of battery materials.

[0133] The present invention can obtain key atomic structure characteristic data by performing a detailed analysis of the atomic structure data of the battery material. These data include information such as interatomic distance, bond length, bond angle, lattice parameters, etc., which help understand the basic structure of the material and the strength of the chemical bond. These characteristic data are crucial for the subsequent establishment of molecular dynamics models and energy potential field models because they determine the accuracy and applicability of the models. Different sets of potential field models are collected and prepared. Then, these potential field models are evaluated and compared in detail using the material characteristic data of the battery components and the obtained atomic structure characteristic data. The evaluation process includes the accuracy, computational efficiency, applicability and description ability of the model for specific battery materials. These evaluation data provide a basis for selecting the most advantageous field model, ensuring that subsequent simulations and analyses can accurately reflect the actual situation. Based on the model adaptability data obtained in the steps, the optimal potential field model is determined. Selecting the optimal model means that the structure, dynamics and chemical properties of the battery material can be accurately described while ensuring computational efficiency. The results of this step directly affect the subsequent model optimization and parameterization process, ensuring the reliability and practicality of the simulation results. The parameterization adjustment of the initial potential field model is to optimize the model according to the actual battery charge and discharge cycle data. By adjusting the parameters of the model, such as the force field constant of the potential field or other interaction terms, the model can better capture the behavior and performance changes of the battery under different charge and discharge conditions. This parameterized adjustment can improve the predictive ability of the model, make the simulation results more consistent with the experimental data, and provide effective tools and guidance for battery design and optimization.

[0134] Optionally, step S3 specifically includes:

[0135] Step S31: spatially aligning the rechargeable battery topological structure model and the battery energy potential field model to obtain a spatially aligned model set;

[0136] In this embodiment, the topological structure model of the rechargeable battery is spatially aligned with the pre-selected battery energy potential field model. The key is to ensure that each atomic position in the topological structure corresponds to the atomic position in the potential field model, so that subsequent model coupling and dynamic simulation can accurately describe the structure and energy field of the battery material. For example, through molecular modeling software such as VMD or PyMOL, the topological structure file (such as PDB format) and the potential field model file (such as ForceField parameter file) can be loaded, and manual or automatic atomic alignment operations can be performed to ensure that the position and naming of each atom are consistent. This ensures data consistency and accuracy in subsequent calculations.

[0137] Step S32: performing model coupling on the spatial alignment model set to obtain a battery energy field model;

[0138] In this embodiment, after the docking of the battery topology model and the energy potential field model that have been spatially aligned has been completed, a model coupling operation needs to be performed. This involves bringing together the spatially aligned models to form a complete battery energy field model. The coupling process includes ensuring that the interaction between the topological structure and the potential field model conforms to the laws of physicochemistry, such as the interaction forces between atoms and the way bonds are formed. Through molecular dynamics software such as LAMMPS or GROMACS, atomic positions and potential field parameters can be integrated into a unified computational framework for subsequent multi-scale simulations.

[0139] Step S33: performing multi-scale molecular dynamics simulation on the battery energy field model to obtain multi-scale molecular dynamics simulation data;

[0140] In this embodiment, a coupled battery energy field model is used for multi-scale molecular dynamics simulation. This simulation technology combines classical molecular dynamics methods and quantum chemical calculations to simulate the dynamic behavior and structural changes of battery materials at the atomic level. For example, molecular dynamics software based on potential field models is used to simulate the atomic motion and stability of crystal morphology of battery materials under different temperature and pressure conditions. Through these simulations, the structural dynamics and energy changes of battery materials in real environments can be obtained, providing key information for battery design and performance optimization.

[0141] Step S34: extracting battery ion migration trajectory features from the multi-scale molecular dynamics simulation data, thereby obtaining battery ion migration trajectory simulation data.

[0142] In this embodiment, the obtained multi-scale molecular dynamics simulation data is further processed to extract the migration trajectory characteristics of battery ions (such as lithium ions) in the material. This includes analyzing the diffusion path, migration rate, and adsorption and desorption process of ions in electrode materials. For example, by tracking the position, velocity, and energy of ions, the mechanism and kinetic characteristics of ion migration in battery materials can be revealed, providing an important reference for performance prediction and optimization during battery charging and discharging.

[0143] In the present invention, the topological structure model of the rechargeable battery and the battery energy potential field model are spatially aligned. This means that the physical structure of the battery and its energy potential field are precisely aligned in space to ensure that the model can accurately describe the structure and energy distribution inside the battery. Through spatial alignment, a set of accurate model sets can be established to provide a consistent basis for subsequent simulation and analysis. The spatially aligned model set is model coupled, that is, each model is integrated into a comprehensive battery energy field model. Such coupling can comprehensively utilize the advantages of each model to provide a more comprehensive and accurate description of the battery energy field. Through coupling, the interaction between the various parts inside the battery can be comprehensively considered to further improve the fidelity and predictive ability of the model. Multiscale molecular dynamics simulation is a simulation method based on different time and length scales for studying the dynamic behavior of battery materials. Through this simulation, the structure, energy changes and reaction kinetics of battery materials can be deeply understood from the perspective of atoms and molecules. These simulation data provide detailed information on the internal processes of the battery, such as ion movement, material deformation, etc. during charging and discharging. The migration trajectory characteristics of battery ions are extracted from multiscale molecular dynamics simulation data. These data show the specific movement path and kinetic characteristics of ions inside the battery, which is the key to understanding the ion transport mechanism during battery charging and discharging. By analyzing these trajectories, the ionic conductivity and charge and discharge efficiency of battery materials can be evaluated, providing an important reference for designing more efficient and stable batteries.

[0144] Optionally, step S33 is specifically:

[0145] Step S331: setting simulation conditions based on battery charge and discharge cycle data to obtain simulation condition data;

[0146] In this embodiment, the battery charge and discharge cycle data is used to set the initial conditions and boundary conditions of the simulation. For example, if the battery charging cycle in the experiment shows a specific current response pattern, similar charging current density needs to be set in the simulation to reproduce these patterns. In addition, temperature also has a significant impact on battery performance, so the simulated temperature conditions need to be set according to the actual operating temperature range. Setting these conditions is a key step to ensure that the simulation results are consistent with the actual battery operating state.

[0147] Step S332: classifying the simulation condition data into simulation types according to the battery component material characteristic data and the atomic structure characteristic data, thereby obtaining electronic scale simulation condition data, atomic scale simulation condition data and molecular scale simulation condition data;

[0148] In this embodiment, the simulation conditions determined in step S331 are classified according to the material property data and atomic structure property data of the battery components (such as the positive electrode, the negative electrode, and the electrolyte). The electron scale simulation focuses on the energy level structure and transport properties of the electrons, and therefore requires accurate electronic structure and energy band information. The atomic scale simulation focuses on the interatomic interaction force and the stability of the crystal structure, and requires parameterization and verification of various interatomic interaction potential functions. The molecular scale simulation focuses on molecular motion and solvent effects, and requires detailed molecular force field parameters and solvent models. Through these classifications, accurate input conditions and model parameters can be provided for subsequent molecular dynamics simulations of different scales.

[0149] Step S333: performing electron-scale molecular dynamics simulation on the battery energy field model according to the electron-scale simulation condition data, thereby obtaining electron dynamics simulation data;

[0150] In this embodiment, the battery energy field model is subjected to an electronic-scale molecular dynamics simulation using the determined electronic-scale simulation condition data. This simulation studies the behavior of electrons in materials by solving the equations of motion of electrons and the photoelectric effect. For example, quantum mechanics software such as VASP or Quantum ESPRESSO is used to simulate the energy level changes and transmission characteristics of electrons in electrode materials to reveal the response and efficiency of electrons during battery charging and discharging.

[0151] Step S334: performing atomic scale molecular dynamics simulation on the battery energy field model according to the atomic scale simulation condition data, thereby obtaining atomic dynamics simulation data;

[0152] In this embodiment, the battery energy field model is subjected to atomic-scale molecular dynamics simulation according to the determined atomic-scale simulation condition data. This simulation usually uses molecular dynamics software such as LAMMPS or GROMACS to simulate the interaction between atoms, the stability of the crystal structure, and the structural changes of the material during the charge and discharge process. Through these simulations, the mechanical properties, fracture mechanism, and structural stability of the material can be understood, providing key information for battery life and reliability.

[0153] Step S335: performing ion-scale molecular dynamics simulation on the battery energy field model according to the molecular-scale simulation condition data, thereby obtaining ion dynamics simulation data;

[0154] In this embodiment, molecular dynamics simulation at the ion scale is performed according to the determined molecular scale simulation condition data. This simulation focuses on the diffusion process of ions in the battery electrolyte and the solvent effect, which is crucial for understanding the influence of ion transport path and electrolyte concentration on battery performance. Molecular dynamics software such as NAMD or AMBER is used to simulate the dynamic behavior of ions in the electrolyte and the dynamic structure of solvent molecules, providing a detailed description of the ion migration characteristics during battery charging and discharging.

[0155] Step S336: performing scale simulation coupling on the electron dynamics simulation data, the atomic dynamics simulation data and the ion dynamics simulation data according to the battery energy field model, thereby obtaining multi-scale molecular dynamics simulation data.

[0156] In this embodiment, the electronic dynamics simulation data, atomic dynamics simulation data and ion dynamics simulation data are coupled for scale simulation. This coupling forms a multi-scale molecular dynamics simulation data set by merging simulation results of different scales, such as the band structure of electrons, the structural evolution of atoms and the migration trajectory of ions. These data sets integrate physical processes and material properties at different scales, provide a more comprehensive and accurate description of battery material behavior, and provide important reference and prediction capabilities for battery design and performance optimization.

[0157] The present invention uses actual data of battery charge and discharge cycles to set simulation conditions. These data reflect the behavior of the battery during actual use, such as current density, temperature change, voltage curve, etc. By setting conditions based on measured data, the authenticity and reliability of the simulation are ensured, so that subsequent simulations can better reflect the performance and characteristics of the battery in actual operation. The simulation condition data are classified according to the characteristics of battery component materials and atomic structure characteristics. The electronic scale, atomic scale and molecular scale simulation condition data obtained after classification are simulated at different scales to more accurately study the characteristics of battery materials at all levels. For example, electronic scale simulation can provide information about electronic structure and energy band characteristics; atomic scale simulation can reveal the interaction and structural dynamics between atoms; molecular scale simulation pays more attention to the interaction and dynamic behavior between molecules. Molecular dynamics simulations are performed at electronic scale, atomic scale and ion scale respectively. Through these simulations, detailed dynamic data at different scales can be obtained, including the energy state of electrons, the position and movement of atoms, the migration path of ions, etc. These data are the key to understanding the internal structure and behavior of battery materials, and help optimize the design and performance of batteries, and improve their energy density, cycle life and safety. The simulation data at the electronic scale, atomic scale and ion scale are coupled to form multi-scale molecular dynamics simulation data. Through this coupling, information at each scale can be integrated to achieve a more comprehensive and consistent understanding of battery materials and behaviors. This not only improves the accuracy of the simulation, but also helps reveal the mutual influence and synergy between different scales in the complex mechanism inside the battery.

[0158] Optionally, step S4 is specifically:

[0159] Step S41: performing lithium ion migration trajectory statistics on the battery ion migration trajectory simulation data, thereby obtaining high-frequency lithium ion migration trajectory data;

[0160] In this embodiment, by statistically analyzing the battery ion migration trajectory simulation data, the most common trajectory patterns in the high-frequency lithium ion migration process can be determined. For example, it can be identified how ions diffuse and insert in electrode materials under typical battery operating conditions, and how their rates change over time.

[0161] Step S42: performing simulation condition association on the high-frequency lithium ion migration trajectory data and the rechargeable battery charge and discharge cycle data, thereby obtaining lithium ion migration condition data;

[0162] In this embodiment, the obtained high-frequency lithium ion migration trajectory data is associated with the actual battery charge and discharge cycle data. Because the battery ion migration trajectory simulation data is obtained by simulating the charge and discharge cycle data of the rechargeable battery, by analyzing the relationship between these data and the ion migration trajectory, the conditional data that affects the ion migration behavior can be established. For example, it can be found how the diffusion rate of ions changes under a specific current density; or how the position distribution of ions inserted into the electrode is affected at different temperatures.

[0163] Step S43: performing a timing analysis of lithium ion migration trajectories under the same conditions based on the lithium ion migration condition data and the high-frequency lithium ion migration trajectory data, thereby obtaining lithium ion migration timing data;

[0164] In this embodiment, the established lithium ion migration condition data is combined with the high-frequency lithium ion migration trajectory data for timing analysis. This analysis can help understand the dynamic changes of ion migration in the battery under specific operating conditions. The position and speed of ions at different time points can be analyzed to understand the specific behavior of ions during charging and discharging. For example, the rapid insertion of ions at the beginning of charging can be observed, and then how the insertion rate slows down after the battery is charged to a certain extent.

[0165] Step S44: performing migration trajectory statistics on the lithium ion migration time series data, thereby obtaining lithium ion migration trajectory data;

[0166] In this embodiment, the obtained lithium ion migration time series data is further statistically analyzed. Trajectory pattern recognition and frequency distribution analysis can be performed to determine the typical patterns and changes of ion migration trajectories during the charge and discharge cycle. This analysis helps to identify abnormal ion behavior of the battery under specific operating conditions, such as abnormal ion insertion rate or atypical ion position distribution.

[0167] Step S45: constructing a lithium plating detection model based on the lithium ion migration condition data and the lithium ion migration trajectory data.

[0168] In this embodiment, a lithium plating detection model is constructed using lithium ion migration condition data and lithium ion migration trajectory data. This model can be based on a machine learning algorithm, such as a support vector machine or a deep learning model, or can be constructed based on a physical and chemical equation. The goal of the model is to predict the lithium plating phenomenon that occurs in the battery under specific operating conditions and to evaluate its potential impact on battery life and performance. For example, the model can predict how great the risk of lithium plating in the battery is under high temperature and high current conditions, as well as operating strategies to reduce the risk of lithium plating.

[0169] The present invention can obtain high-frequency lithium ion migration trajectory data by statistically analyzing the battery ion migration trajectory simulation data. These data reflect the actual migration path and frequency of lithium ions in the battery, helping to reveal the dynamic behavior inside the battery. After the high-frequency lithium ion migration trajectory data is correlated with the charge and discharge cycle data, the conditional data of lithium ion migration can be obtained. These data guide the subsequent analysis, so that the migration behavior of lithium ions can be closely related to the actual charge and discharge cycle conditions, improving the authenticity and applicability of the simulation. Using the lithium ion migration conditional data and the high-frequency migration trajectory data, the timing analysis of the lithium ion migration trajectory under the same conditions is performed. This step can generate the migration of lithium ions at different time points, including detailed information such as speed change and position change, to help understand the dynamic process inside the battery. Further statistical analysis of the lithium ion migration timing data can obtain the trajectory data of lithium ion migration. These data can not only be used to verify the accuracy of the model, but also provide important references for subsequent battery design and optimization, especially in understanding battery life, performance attenuation mechanism, etc. A lithium precipitation detection model is constructed based on lithium ion migration conditional data and migration trajectory data. This model can predict the location and amount of lithium plating in the battery, helping to detect problems or potential risks in the battery in advance, and then improve battery design or adjust usage strategies, thereby extending battery life and improving safety.

[0170] Optionally, the present specification also provides a detection system for lithium deposition in a rechargeable battery, which is used to perform the detection method for lithium deposition in a rechargeable battery as described above, and the detection system for lithium deposition in a rechargeable battery comprises:

[0171] A topological structure analysis module is used to obtain the rechargeable battery structure data, and to extract the battery microstructure characteristics and battery component structure characteristics of the rechargeable battery structure data, so as to obtain the battery microstructure data and battery component structure data; and to perform topological structure analysis based on the battery component structure data, so as to obtain the rechargeable battery topological structure model;

[0172] An energy potential field parameterization module is used to obtain the charge and discharge cycle data of the rechargeable battery, perform microscopic atomic structure modeling according to the battery microstructure data, thereby obtaining a battery microstructure model, and perform battery energy potential field parameterization on the battery microstructure model based on the charge and discharge cycle data of the rechargeable battery, thereby obtaining a battery energy potential field model;

[0173] The molecular dynamics simulation module is used to couple the rechargeable battery topology model and the battery energy potential field model to obtain the battery energy field model, and to perform multi-scale molecular dynamics simulation based on the battery energy field model to obtain battery ion migration trajectory simulation data;

[0174] The lithium ion trajectory analysis module is used to perform charge and discharge cycle lithium ion trajectory analysis on the charge and discharge cycle data of the rechargeable battery and the battery ion migration trajectory simulation data, so as to obtain the lithium ion migration trajectory data, and build a lithium precipitation detection model based on the lithium ion migration trajectory data;

[0175] The lithium ion dynamic behavior prediction module is used to obtain real-time rechargeable battery sensor data, and predict the lithium ion dynamic behavior of the real-time rechargeable battery sensor data through the lithium precipitation detection model, so as to obtain the lithium ion dynamic behavior prediction data;

[0176] The charging parameter optimization module is used to optimize the charging parameters according to the lithium-ion dynamic behavior prediction data and the real-time charging battery sensor data, so as to obtain the optimized charging parameters, and transmit the optimized charging parameters to the charging control platform of the rechargeable battery to perform the charging control task.

[0177] The detection system for lithium deposition in a rechargeable battery of the present invention can implement any detection method for lithium deposition in a rechargeable battery of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the detection method for lithium deposition in a rechargeable battery. The internal modules of the system cooperate with each other, thereby improving the battery performance of the rechargeable battery.

[0178] Optionally, the present specification also provides a battery system, including a rechargeable battery and a rechargeable battery lithium deposition detection system applied to the rechargeable battery, the rechargeable battery lithium deposition detection system being used to execute a rechargeable battery lithium deposition detection method as described above.

[0179] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0180] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for detecting lithium deposition in a rechargeable battery, characterized in that: The following steps are involved: Step S1: acquiring rechargeable battery structure data, and extracting battery microstructure features and battery component structure features from the rechargeable battery structure data, thereby obtaining battery microstructure data and battery component structure data; performing topological structure analysis based on the battery component structure data, thereby obtaining a rechargeable battery topological structure model; Step S2: acquiring the charge and discharge cycle data of the rechargeable battery, performing microscopic atomic structure modeling according to the battery microstructure data, thereby obtaining a battery microstructure model, and performing battery energy potential field parameterization on the battery microstructure model based on the charge and discharge cycle data of the rechargeable battery, thereby obtaining a battery energy potential field model; Step S3: Model coupling is performed on the rechargeable battery topology model and the battery energy potential field model to obtain the battery energy field model, and multi-scale molecular dynamics simulation is performed according to the battery energy field model to obtain battery ion migration trajectory simulation data. Step S3 is specifically as follows: Step S31: spatially aligning the rechargeable battery topological structure model and the battery energy potential field model to obtain a spatially aligned model set; Step S32: performing model coupling on the spatial alignment model set to obtain a battery energy field model; Step S33: Perform multi-scale molecular dynamics simulation on the battery energy field model to obtain multi-scale molecular dynamics simulation data. Step S33 is specifically as follows: Step S331: setting simulation conditions based on battery charge and discharge cycle data to obtain simulation condition data; Step S332: classifying the simulation condition data into simulation types according to the battery component material characteristic data and the atomic structure characteristic data, thereby obtaining electronic scale simulation condition data, atomic scale simulation condition data and molecular scale simulation condition data; Step S333: performing electron-scale molecular dynamics simulation on the battery energy field model according to the electron-scale simulation condition data, thereby obtaining electron dynamics simulation data; Step S334: performing atomic scale molecular dynamics simulation on the battery energy field model according to the atomic scale simulation condition data, thereby obtaining atomic dynamics simulation data; Step S335: performing ion-scale molecular dynamics simulation on the battery energy field model according to the molecular-scale simulation condition data, thereby obtaining ion dynamics simulation data; Step S336: performing scale simulation coupling on the electronic dynamics simulation data, the atomic dynamics simulation data and the ion dynamics simulation data according to the battery energy field model, thereby obtaining multi-scale molecular dynamics simulation data; Step S34: extracting battery ion migration trajectory features from the multi-scale molecular dynamics simulation data, thereby obtaining battery ion migration trajectory simulation data; Step S4: Perform charge and discharge cycle lithium ion trajectory analysis on the rechargeable battery charge and discharge cycle data and the battery ion migration trajectory simulation data, thereby obtaining lithium ion migration trajectory data, and constructing a lithium precipitation detection model based on the lithium ion migration trajectory data. Step S4 is specifically as follows: Step S41: performing lithium ion migration trajectory statistics on the battery ion migration trajectory simulation data, thereby obtaining high-frequency lithium ion migration trajectory data; Step S42: performing simulation condition association on the high-frequency lithium ion migration trajectory data and the rechargeable battery charge and discharge cycle data, thereby obtaining lithium ion migration condition data; Step S43: performing a timing analysis of lithium ion migration trajectories under the same conditions based on the lithium ion migration condition data and the high-frequency lithium ion migration trajectory data, thereby obtaining lithium ion migration timing data; Step S44: performing migration trajectory statistics on the lithium ion migration time series data, thereby obtaining lithium ion migration trajectory data; Step S45: constructing a lithium deposition detection model based on the lithium ion migration condition data and the lithium ion migration trajectory data; Step S5: acquiring real-time rechargeable battery sensor data, and predicting the dynamic behavior of lithium ions on the real-time rechargeable battery sensor data through a lithium plating detection model, thereby obtaining lithium ion dynamic behavior prediction data; Step S6: Optimize charging parameters according to the lithium ion dynamic behavior prediction data and the real-time rechargeable battery sensor data to obtain optimized charging parameters, and transmit the optimized charging parameters to the rechargeable battery charging control platform to execute the charging control task.

2. The method for detecting lithium deposition in a rechargeable battery according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring the rechargeable battery structure data, and performing battery microstructure feature extraction and battery component structure feature extraction on the rechargeable battery structure data, thereby obtaining the battery microstructure data and battery component structure data; Step S12: performing a topological structure analysis based on the battery assembly structure data to obtain a battery topological structure model; Step S13: classifying component materials based on the battery microstructure data, thereby obtaining battery component material data; Step S14: performing material property analysis on the battery component material data, thereby obtaining the battery component material property data; Step S15: mapping the material properties of the battery topology model according to the material characteristic data of the battery assembly, thereby obtaining the rechargeable battery topology model.

3. The method for detecting lithium deposition in a rechargeable battery according to claim 2, characterized in that: Step S12 is specifically as follows: Step S121: performing three-dimensional structural modeling on the battery assembly structural data, thereby obtaining a three-dimensional structural model of the battery assembly; Step S122: extracting connection structure features from the three-dimensional structure model of the battery assembly, thereby obtaining connection structure data of the battery assembly; Step S123: Associating the battery assembly connection relationship according to the battery assembly connection structure data, thereby obtaining the battery assembly connection relationship data; Step S124: Perform a topological structure analysis based on the battery assembly connection relationship data to obtain a battery topological structure model.

4. The method for detecting lithium deposition in a rechargeable battery according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: acquiring charging and discharging cycle data of the rechargeable battery, and performing time alignment on the charging and discharging cycle data of the rechargeable battery, thereby obtaining the battery charging and discharging cycle data; Step S22: performing atomic structure classification on the battery microstructure data to obtain an atomic structure data set; Step S23: performing molecular dynamics simulation based on the atomic structure data set to obtain molecular dynamics simulation data; Step S24: performing molecular dynamics coupling on the rechargeable battery topological structure model according to the molecular dynamics simulation data, thereby obtaining a battery microstructure model; Step S25: Parameterize the battery energy potential field of the battery microstructure model based on the battery charge and discharge cycle data, so as to obtain a battery energy potential field model.

5. The method for detecting lithium deposition in a rechargeable battery according to claim 4, characterized in that: Step S25 is specifically as follows: Step S251: performing atomic structure characteristic analysis on the atomic structure data set to obtain atomic structure characteristic data; Step S252: obtaining a potential field model set, and performing a model adaptability evaluation on the potential field model set based on the battery component material characteristic data and the atomic structure characteristic data, thereby obtaining model adaptability data; Step S253: selecting the most advantageous field model from the potential field model set according to the model adaptability data, thereby obtaining an initial potential field model; Step S254: parameterize and adjust the initial potential field model according to the battery charge and discharge cycle data, so as to obtain a battery energy potential field model.

6. A detection system for lithium deposition in a rechargeable battery, characterized in that: Used to perform the detection method for lithium deposition in a rechargeable battery as claimed in claim 1, the detection system for lithium deposition in a rechargeable battery comprises: A topological structure analysis module is used to obtain the rechargeable battery structure data, and to extract the battery microstructure characteristics and battery component structure characteristics of the rechargeable battery structure data, so as to obtain the battery microstructure data and battery component structure data; and to perform topological structure analysis based on the battery component structure data, so as to obtain the rechargeable battery topological structure model; An energy potential field parameterization module is used to obtain the charge and discharge cycle data of the rechargeable battery, perform microscopic atomic structure modeling according to the battery microstructure data, thereby obtaining a battery microstructure model, and perform battery energy potential field parameterization on the battery microstructure model based on the charge and discharge cycle data of the rechargeable battery, thereby obtaining a battery energy potential field model; The molecular dynamics simulation module is used to couple the rechargeable battery topology model and the battery energy potential field model to obtain the battery energy field model, and to perform multi-scale molecular dynamics simulation based on the battery energy field model to obtain battery ion migration trajectory simulation data; The lithium ion trajectory analysis module is used to perform charge and discharge cycle lithium ion trajectory analysis on the charge and discharge cycle data of the rechargeable battery and the battery ion migration trajectory simulation data, so as to obtain the lithium ion migration trajectory data, and build a lithium precipitation detection model based on the lithium ion migration trajectory data; The lithium ion dynamic behavior prediction module is used to obtain real-time rechargeable battery sensor data, and predict the lithium ion dynamic behavior of the real-time rechargeable battery sensor data through the lithium precipitation detection model, so as to obtain the lithium ion dynamic behavior prediction data; The charging parameter optimization module is used to optimize the charging parameters according to the lithium-ion dynamic behavior prediction data and the real-time charging battery sensor data, so as to obtain the optimized charging parameters, and transmit the optimized charging parameters to the charging control platform of the rechargeable battery to perform the charging control task.

7. A battery system, characterized in that: The invention comprises a rechargeable battery and a detection system for lithium deposition in a rechargeable battery applied to the rechargeable battery. The detection system for lithium deposition in a rechargeable battery is used to execute the detection method for lithium deposition in a rechargeable battery as claimed in claim 1.

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