A charging control method and related device

Abstract

The application discloses a charging control method and related equipment, and relates to the field of charging control. The method comprises the following steps: inputting a current waveform to be analyzed into a simulation model to obtain simulation data information of the current waveform to be analyzed, wherein the simulation model is established based on a phase field model of lithium dendrites, a battery network model, constraint condition information and a secondary current distribution module; determining a target charging current based on the simulation data information of the current waveform to be analyzed; and controlling the battery to perform a charging operation through the target charging current.

Description

Technical Field

[0001] This specification relates to the field of charging control, and more specifically, this application relates to a charging control method and related equipment. Background Technology

[0002] With the rapid popularization of electric vehicles, charging technology has become one of the key factors restricting their further development. Currently, most electric vehicles adopt a constant current constant voltage (CC-CV) charging strategy. Although this strategy is mature and widely used, it still has many shortcomings in terms of charging efficiency, charging time, and temperature control.

[0003] Lithium dendrites generated during charging can severely impact battery safety and lifespan, and may even cause safety accidents such as short circuits and fires. To at least address some of these issues, there is an urgent need for a building extraction method and related equipment with better cross-domain extraction capabilities. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] In a first aspect, this application proposes a charging control method, comprising:

[0006] The current waveform to be analyzed is input into the simulation model to obtain the simulation data information of the current waveform to be analyzed. The simulation model is established based on the lithium dendrite phase field model, battery network model, constraint information and secondary current distribution module.

[0007] The target charging current is determined based on the simulation data of the current waveform to be analyzed.

[0008] The battery is charged by controlling the target charging current.

[0009] In one feasible implementation, the phase-field model of the lithium dendrites described above is constructed based on the order parameter equation, the lithium ion diffusion equation, and the charge conservation equation.

[0010] In one feasible implementation, the above-mentioned order parameter equation is a nonlinear model that varies in time and space combined with the order parameter equation of Butler-Volmer dynamics.

[0011] The above lithium-ion diffusion equation is the Nernst-Planck equation;

[0012] The charge conservation equation described above is the current density conservation equation described by the Poisson equation.

[0013] In one feasible implementation, the battery grid model is a phase-field simulation model of the lithium coating morphology in a two-dimensional half-cell electrodeposition system. The battery network model is discretized using quadrilateral grids and employs adaptive grid technology.

[0014] In one feasible implementation, the aforementioned constraint information includes boundary condition information, lithium ion concentration information, and upper and lower boundary potential information.

[0015] In one feasible implementation, the aforementioned secondary current distribution module is used to describe the relationship between electrode dynamics and impedance.

[0016] In one feasible implementation, the above simulation data information includes space utilization and aspect ratio;

[0017] The current waveform to be analyzed above is generated based on a sine wave.

[0018] Secondly, this application proposes a charging control device, comprising:

[0019] The acquisition unit is used to input the current waveform to be analyzed into the simulation model to obtain the simulation data information of the current waveform to be analyzed. The simulation model is established based on the lithium dendrite phase field model, battery network model, constraint information and secondary current distribution module.

[0020] The determining unit is used to determine the target charging current based on the simulation data information of the current waveform to be analyzed;

[0021] A control unit is used to control the battery to perform a charging operation based on the target charging current.

[0022] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the charging control method as described in any of the first aspects above.

[0023] Fourthly, this application also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the charging control method of any one of the first aspects.

[0024] In summary, the charging control method proposed in this application includes: inputting the current waveform to be analyzed into a simulation model to obtain simulation data information of the current waveform to be analyzed, wherein the simulation model is established based on the lithium dendrite phase-field model, battery network model, constraint information, and secondary current distribution module; determining the target charging current based on the simulation data information of the current waveform to be analyzed; and controlling the battery to perform charging operation through the target charging current. The method proposed in this application, by inputting the designed current waveform into a simulation system based on the lithium dendrite phase-field model for simulation, can accurately analyze the influence of different current waveforms on lithium dendrite growth, and ultimately determine the target charging current that can significantly suppress lithium dendrite growth. This method effectively reduces the safety risks of the battery during charging and improves the battery's lifespan. Compared with the traditional constant current and constant voltage charging method, the sinusoidal charging current in this application, through precise design and simulation analysis, can improve charging efficiency while shortening charging time. This is of great significance for improving the popularity of electric vehicles and user experience. This application analyzes the impact of current waveform on battery internal temperature changes through simulation, enabling the effective design of a charging current waveform with lower temperature rise. This avoids battery damage or performance degradation caused by excessively high temperatures, thereby further improving battery safety and stability. Through the construction of simulation models and the optimization design of current waveforms, this application provides solid theoretical support and practical guidance for the application of sinusoidal charging current in electric vehicle charging, filling a gap in current research and demonstrating significant innovation and practicality. In summary, this application not only proposes a novel electric vehicle charging strategy but also significantly improves safety, efficiency, and temperature control during the charging process through in-depth simulation analysis and optimization design, providing a superior solution for fast charging of electric vehicles. Attached Figure Description

[0025] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0026] Figure 1 This is a schematic flowchart of a charging control method provided in an embodiment of this application;

[0027] Figure 2 A schematic diagram of phase field order variable variation provided in an embodiment of this application;

[0028] Figure 3 A schematic diagram of a typical CC mode fast charging current curve of a lithium-ion battery provided for embodiments of this application;

[0029] Figure 4A schematic diagram of an SRC1 curve provided in an embodiment of this application;

[0030] Figure 5 A schematic diagram of an SRC2 curve provided for an embodiment of this application;

[0031] Figure 6 A schematic diagram with an aspect ratio provided for an embodiment of this application;

[0032] Figure 7 This is a schematic diagram illustrating the effect of different sinusoidal current cycles on lithium dendrite growth, provided in an embodiment of this application.

[0033] Figure 8 This is a schematic diagram of the change curve of lithium dendrite space utilization under different sinusoidal current cycles provided in an embodiment of this application.

[0034] Figure 9 This is a schematic diagram illustrating the effect of different charging rates on lithium dendrite growth, provided in an embodiment of this application.

[0035] Figure 10 A schematic diagram of the change curve of lithium dendrite space utilization under different sinusoidal current charging rates is provided for an embodiment of this application.

[0036] Figure 11 A schematic diagram illustrating the effect of different duty cycles of SRC2 charging on lithium dendrites, provided in an embodiment of this application.

[0037] Figure 12 A schematic diagram of the change curve of lithium dendrite space utilization under different sinusoidal current duty cycles is provided for an embodiment of this application.

[0038] Figure 13 A schematic diagram illustrating the variation curves of lithium dendrite aspect ratio under different sinusoidal current duty cycles provided in this application embodiment.

[0039] Figure 14 This is a schematic diagram of a charging control device provided in an embodiment of this application;

[0040] Figure 15 This is a schematic diagram of an electronic device structure for a charging control method provided in an embodiment of this application. Detailed Implementation

[0041] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0042] Unlike traditional mesoscale methods that treat interfaces as zero-dimensional sharp interfaces, phase-field methods characterize the interfaces between different phases using phase-field variables. To avoid the problems encountered by previous sharp models in solving the problem, the phase-field method introduces a continuous state variable that can smoothly transition between phase transformations during computation; this is the order parameter or phase-field parameter. Figure 2 As shown, ξ is introduced as a phase field order variable. When lithium ions gain electrons to become lithium atoms and deposit on the electrode surface, forming a solid phase, ξ = 1. When lithium ions are far from the electrode and no redox reaction occurs, ξ = 0.

[0043] Please see Figure 1 This is a schematic flowchart of a charging control method provided in an embodiment of this application, which may specifically include:

[0044] S110. Input the current waveform to be analyzed into the simulation model to obtain the simulation data information of the current waveform to be analyzed. The simulation model is established based on the lithium dendrite phase field model, battery network model, constraint information and secondary current distribution module.

[0045] For example, the current waveform to be analyzed is input into a simulation model to obtain simulation data information of the current waveform. This simulation model is built based on the lithium dendrite phase-field model, battery network model, constraint information, and secondary current distribution module. The lithium dendrite phase-field model: This model is used to simulate the growth process of lithium dendrites. The phase-field method can accurately describe the migration and deposition behavior of lithium ions in the electrode, which is crucial for understanding the lithium dendrites that may appear during charging.

[0046] Battery network model: used to simulate the current distribution inside the battery, combined with electrochemical reaction kinetics to analyze how the current flows inside the battery, which is very important for evaluating the battery behavior under different current waveforms.

[0047] Constraint information: refers to the boundary conditions and initial conditions applied during the simulation process, such as temperature and pressure, which affect the overall reaction process of the battery.

[0048] Secondary current distribution module: This module is used to simulate the local changes and distribution of current to ensure that the current distribution in the simulation process conforms to the actual situation.

[0049] By inputting the current waveform to be analyzed into the simulation model, the system can obtain the electrochemical reaction characteristics that the current waveform may cause in actual use. These characteristics form the basis of the simulation data information.

[0050] S120. Determine the target charging current based on the simulation data of the current waveform to be analyzed.

[0051] For example, the target charging current is determined based on simulation data obtained from the simulation model. Specifically, by analyzing indicators related to lithium dendrite growth, temperature rise control, and battery safety performance in the simulation data, the system can identify a charging current waveform that can effectively suppress lithium dendrite growth, optimize charging efficiency, and reduce safety risks. This charging current waveform is the target charging current. This target charging current is the optimal charging current obtained by precisely adjusting the charging strategy while meeting battery safety and performance requirements.

[0052] S130, The battery is charged by controlling the target charging current as described above.

[0053] For example, the battery is charged using a target charging current. This charging operation aims to apply the previously determined target charging current to ensure that the battery can effectively suppress lithium dendrite growth during charging, optimize charging efficiency, and reduce temperature rise and other potential safety risks. Through this charging process, the overall performance of the battery can be improved, battery life can be extended, and the incidence of battery accidents can be reduced.

[0054] In summary, the method proposed in this application, by inputting the designed current waveform into a simulation system based on a lithium dendrite phase field model for simulation, can accurately analyze the influence of different current waveforms on lithium dendrite growth, and ultimately determine the target charging current that can significantly suppress lithium dendrite growth. This method effectively reduces the safety risks of the battery during charging and improves the battery's lifespan. Compared with the traditional constant current and constant voltage charging method, the sinusoidal charging current designed and simulated in this application can improve charging efficiency while shortening charging time. This is of great significance for increasing the popularity of electric vehicles and improving user experience. By simulating and analyzing the influence of the current waveform on the internal temperature change of the battery, this application can effectively design a charging current waveform with a lower temperature rise, avoiding battery damage or performance degradation caused by excessively high temperatures, thereby further improving the safety and stability of the battery. Through the construction of the simulation model and the optimized design of the current waveform, this application provides solid theoretical support and practical guidance for the application of sinusoidal charging current in electric vehicle charging, filling a gap in current research and possessing significant innovation and practicality. In summary, this application not only proposes a novel electric vehicle charging strategy, but also significantly improves the safety, efficiency, and temperature control during the charging process through in-depth simulation analysis and optimized design, providing a better solution for fast charging of electric vehicles.

[0055] In some examples, the phase-field model of the lithium dendrites described above is constructed based on the order parameter equation, the lithium-ion diffusion equation, and the charge conservation equation.

[0056] For example, the order parameter equation is used to describe the phase transition behavior during lithium dendrite growth. In phase-field models, the order parameter is used to distinguish substances in different phase states. In the simulation of lithium dendrite growth, the order parameter can describe the interface evolution process between lithium dendrites and the electrolyte. The introduction of the order parameter equation enables the model to capture the kinetic characteristics of lithium dendrite formation and growth, thus providing a basis for predicting the morphology of lithium dendrites.

[0057] The lithium-ion diffusion equation describes the diffusion behavior of lithium ions in electrode materials. Lithium-ion diffusion is a crucial factor influencing lithium dendrite growth. This equation simulates the migration process of lithium ions during charging by describing their spatial distribution and temporal evolution. The diffusion equation provides the model with key information about how lithium ions move within the electrode and how they affect dendrite growth.

[0058] The charge conservation equation ensures the conservation of charge throughout the electrochemical system. Charge conservation is one of the fundamental physical principles during battery charging, and the charge conservation equation can be used to simulate changes in current distribution and potential difference at different locations. The introduction of this equation allows the model to more accurately reflect the realities of electrochemical reactions, especially the impact of lithium dendrite growth on current and potential.

[0059] The aforementioned phase-field model of lithium dendrites combines the order parameter equation, the lithium-ion diffusion equation, and the charge conservation equation to construct a comprehensive simulation framework for analyzing and predicting the growth behavior of lithium dendrites. In practical applications, this model simulates the evolution of lithium dendrites during the charging process by inputting different charging current waveforms, and evaluates the inhibitory effect of different current waveforms on dendrite growth.

[0060] This simulation model identifies the charging current waveform that significantly inhibits lithium dendrite growth, and the target charging current is determined based on the simulation results. Ultimately, this target charging current is used in actual battery charging operations to reduce lithium dendrite growth and improve battery safety and charging efficiency.

[0061] In some examples, the above order parameter equations are order parameter equations that combine nonlinear models that vary in time and space with Butler-Volmer dynamics;

[0062] The above lithium-ion diffusion equation is the Nernst-Planck equation;

[0063] The charge conservation equation described above is the current density conservation equation described by the Poisson equation.

[0064] For example, considering the stress on dendrite growth in a solid electrolyte, an elastic energy density is introduced. The overall method is based on free energy; the total Gibbs free energy during lithium dendrite growth consists of the Mönchs free energy density, gradient energy density, electrostatic energy density, and elastic energy density, and can be expressed as:

[0065]

[0066] In the formula f ch f represents the Mössöz free energy density. grad f represents the gradient energy density. else f represents the electrostatic energy density. σ φ represents the elastic energy density, which is related to the stress state of each phase during lithium deposition. c represents the lithium concentration distribution in the electrolyte, and φ represents the potential distribution.

[0067] f ch (ξ)=Wξ 2 (1-ξ) 2 (2)

[0068] In the formula, Wξ 2 (1-ξ) 2 This is a basic double-well function, describing two equilibrium states at the electrode interface and in the electrolyte, respectively. W represents the barrier height.

[0069] Since the driving force of the electrode reaction associated with overpotential is much greater than that associated with thermodynamic interfacial energy, the rate of change of the phase interface is linearly related to the decrease in interfacial free energy and exponentially related to the driving force of the electrode reaction. Therefore, the nonlinear model describing the temporal and spatial changes of the order parameter, combined with Butler-Volmer kinetics, can be expressed as:

[0070]

[0071] In the formula c + Lithium ion concentration, L σ It is the interface mobility, L η Here, F is the reaction-related constant, h(ξ) is the interpolation function, h'(ξ) is the first derivative of the interpolation function, F is the Faraday constant, R is the gas constant, and T is the temperature. Therefore, it can be seen that the driving force for interface migration is mainly provided by the interfacial free energy and the electrokinetic reaction.

[0072] The Nernst-Planck equation is used to describe the diffusion and transport of lithium ions. During electrodeposition, it is assumed that solid lithium is stationary and does not diffuse, neglecting the effects of electron transport, and considering material consumption at the interface.

[0073] Neglecting anion migration, the partial differential equation describing the evolution of lithium-ion concentration over time is expressed as:

[0074]

[0075] In the formula D eff Let c be the diffusion coefficient. s Let c be the potential density of lithium metal, and c0 be the standard volume concentration of the electrolyte solution. The diffusion coefficient D... eff Determined by the interpolation function.

[0076] Assuming the system is electrically neutral, the conservation of current density can be described using the Poisson equation as follows:

[0077]

[0078] In the formula σ eff is the electrical conductivity.

[0079] In some examples, the battery mesh model described above is a phase-field simulation model of the lithium coating morphology in a two-dimensional half-cell electrodeposition system. The battery network model is discretized using quadrilateral meshes and employs adaptive meshing technology.

[0080] For example, a battery mesh model is used to simulate the phase-field simulation model of lithium coating morphology, and it is constructed based on a two-dimensional half-cell electrodeposition system. In this model, the coating growth process of a lithium-ion battery is simulated as an evolution process in a two-dimensional space. The core of the model is to discretize the simulation region using quadrilateral mesh discretization technology to ensure the accuracy of the simulation results and the controllability of the calculation. To further improve computational efficiency and accuracy, an adaptive mesh technique is adopted, and the mesh density can be dynamically adjusted as needed to better capture subtle changes in the lithium dendrite growth process.

[0081] In some examples, the above constraint information includes boundary condition information, lithium ion concentration information, and upper and lower boundary potential information.

[0082] For example, boundary condition information is used to define the boundary behavior of the simulation region, determining the flow of lithium ions and the potential distribution at the electrode boundaries. Lithium ion concentration information describes the distribution of lithium ions inside the battery; changes in lithium ion concentration are crucial factors determining the battery reaction rate and dendrite growth. Upper and lower boundary potential information refers to the potential difference between the upper and lower boundaries of the battery during the simulation; this potential information has a significant impact on simulating the electric field distribution and electrochemical reactions during actual charging.

[0083] In some examples, the aforementioned secondary current distribution module is used to describe the relationship between electrode dynamics and impedance.

[0084] For example, the secondary current distribution module is used to describe the relationship between electrode dynamics and impedance. By simulating the current distribution of the electrode material during charging and discharging, it evaluates the dynamic response behavior of the electrode and the influence of electrode impedance on the current distribution. This module helps to understand the current flow inside the electrode under different charging current conditions, thereby predicting possible lithium dendrite formation regions and morphologies.

[0085] Specifically, COMSOL Multiphysics software and the finite element method can be used. Phase-field simulation of lithium coating morphology was performed in a two-dimensional half-cell electrodeposition system with a size of 6×6μm². The simulation domain was discretized using a quadrilateral mesh with a minimum size of 0.005μm and a maximum size of 0.05μm. Adaptive mesh refinement was used to improve the convergence and accuracy of the simulation. Adiabatic boundary conditions were used for the four boundaries of the phase-field variables and the left and right boundaries of the concentration and potential. The upper and lower boundaries of the lithium ion concentration were set to 1M and 0M, respectively, and the upper and lower boundary potentials were set to 0.1V and 0V, respectively. A "secondary current distribution" module was added to simulate the electrochemical reactions at the electrolyte and electrode interface. The growth of lithium dendrites can be obtained by solving the governing equations (3), (4), and (5).

[0086] In some examples, the simulation data mentioned above includes space utilization and aspect ratio;

[0087] The current waveform to be analyzed above is generated based on a sine wave.

[0088] For example, a typical CC-mode fast charging current curve for a standard lithium-ion battery is shown below. Figure 3 As shown. During the CC charging phase, the battery cell maintains I MAX A stable current is maintained until the battery cell reaches its maximum terminal voltage level V. MAX This simulation design uses a constant current at a 1C charging rate for a single battery cell as I. MAX The charging time T under the subcurrent charging condition serves as a control group.

[0089] In the SRC1 charging method, I MAX As a baseline, the upper peak current is set to a multiple of C1, and the lower peak current to a multiple of C2, ensuring that the charging capacity within one cycle is the same as the control group. Multiple cycles are set within the entire charging time T. For example... Figure 4 As shown, Figure 4 This is the SRC1 curve.

[0090] In the SRC2 charging method, I is also used. MAX As a base value. Figure 5 As shown, Figure 5 This is the SRC2 curve.

[0091] A sinusoidal half-wave trend with a specific duty cycle set within a period. on t represents the proportion of the time of a sinusoidal half-wave within one period. off This indicates the remaining time within the cycle. Multiple cycles are set within the entire charging time T. The peak value of the sinusoidal half-wave is calculated using the following formula:

[0092] Given a period of time t, a peak value of a sinusoidal half-wave of magnitude A, and a duty cycle of z, the frequency of the sinusoidal half-wave is:

[0093]

[0094] Based on the fact that the charging amount is the same within a cycle:

[0095]

[0096] The calculation yielded:

[0097]

[0098] This study selected a control group and conducted simulations of SRC1 charging methods with different cycles and rates, as well as SRC2 charging methods with different duty cycles. Specific parameters are shown in Tables 1, 2, and 3.

[0099] Experimental group cycle C1 C2 control group T 0 1C SRC1-1 0.5 2C 1C SRC1-2 0.8 2C 1C SRC1-3 1 2C 1C SRC1-4 2 2C 1C SRC1-5 4 2C 1C

[0100] Table 1 Charging Current Waveform Design 1

[0101]

[0102]

[0103] Table 2 Charging Current Waveform Design 2

[0104] Experimental group cycle Duty cycle Peak size SRC2-1 2 100％ <![CDATA[1.57I MAX ]]> SRC2-2 2 80％ <![CDATA[1.96I MAX ]]> SRC2-3 2 66.7％ <![CDATA[2.36I MAX ]]> SRC2-4 2 50％ <![CDATA[3.14I MAX ]]> SRC2-5 2 25％ <![CDATA[6.28I MAX ]]>

[0105] Table 3 Charging current waveform design 3

[0106] The phase-field method was used to simulate lithium dendrite growth. One advantage of the phase-field method is its intuitiveness; the simulation results directly represent the growth status of lithium dendrites. Comparing the calculated results of the order parameter over time allows for a rough observation of the simulation results between the control and experimental groups. In addition to direct observation, two quantitative analysis data indicators are also provided: space utilization and aspect ratio.

[0107] Space utilization is the ratio of the lithium dendrite area to the computational domain area; a higher value indicates a larger area occupied by the lithium dendrites, and thus a larger morphology. Aspect ratio is the ratio of the lithium dendrite height (B) to its width (A), such as... Figure 6 As shown, the aspect ratio represents the sharpness of the lithium dendrite morphology; the higher the value, the sharper the lithium dendrite.

[0108] In some examples, the effect of different periods on lithium dendrite morphology was simulated based on the data in Table 1. The results of lithium dendrite morphology are as follows: Figure 7 As shown. Figure 8 The variation curves of lithium dendrite space utilization under the above simulation conditions are shown. According to... Figure 7 Compared with the control group, the experimental group showed a significant inhibition effect, and as the cycle time decreased, the number of sinusoidal current cycles increased throughout the charging time, resulting in smaller lithium dendrite morphology and better inhibition. Figure 8 The line graphs shown also demonstrate the same results. SRC1-1 exhibits the best suppression effect, with smaller lithium dendrite height and lateral length, and smoother side branches.

[0109] In some examples, with Figure 4 Based on the SCR1 curve shown, different charging rates C1 were used to simulate the morphological growth of lithium dendrites. The parameter settings are shown in Table 2. Figure 9 The simulation results are presented. The figure shows that all five charging rates significantly suppress lithium dendrite growth. Among them, the charging rates in SRC1-7 to SRC1-10 modes are greater than 1C, meaning there is a reverse current in each cycle, which is the discharge current. According to... Figure 10Data shows that under SRC1-7 conditions, the reverse current accounts for a very small proportion of the total current, and the space utilization rate does not change significantly compared to SCR1-6 conditions, resulting in an insignificant suppression effect. However, as the proportion of reverse current increases, the space utilization rate decreases significantly, and the lithium dendrite morphology is greatly reduced, with the most significant suppression effect observed under SCR1-10 conditions. Increasing the charging current rate further enhances the suppression effect.

[0110] In some examples, the effects of sinusoidal half-wave modes with duty cycles of 100%, 80%, 66.7%, 50%, and 25% on lithium dendrite growth were simulated using the SRC2 charging method. The simulation results are as follows: Figure 11 As shown in the figure. Simulation results show that the SCR2 charging current has a significant inhibitory effect on lithium dendrite growth. The differences in lithium dendrite growth morphology are not significant under different duty cycles.

[0111] Figure 12 and Figure 13 The data show the space utilization and aspect ratio of lithium dendrite morphology under different duty cycles. With increasing duty cycle, the space utilization increases, indicating larger lithium dendrite morphologies, with an overall variation range of around 0.2%. Compared to the previous two conditions, the change is not significant, and the overall space occupancy is also larger. With increasing duty cycle, the aspect ratio decreases, with no significant overall change trend.

[0112] In summary, the two sinusoidal trend charging currents designed in this application have a suppressive effect on lithium dendrite growth. Under the first curve SCR1 condition, the suppression effect on lithium dendrite morphology is more obvious as the function period decreases and the charging rate increases. Under the second curve SCR2 condition, the suppression effect on lithium dendrite morphology does not change significantly with the change in duty cycle. Based on the above description, selecting the SCR1 curve with a period of 0.5 and a sinusoidal charging current of 2C charging rate C1 results in the most significant suppression effect on lithium dendrites.

[0113] like Figure 14 As shown, this application proposes a charging control device, comprising:

[0114] The acquisition unit 21 is used to input the current waveform to be analyzed into the simulation model in order to obtain the simulation data information of the current waveform to be analyzed. The simulation model is established based on the lithium dendrite phase field model, battery network model, constraint information and secondary current distribution module.

[0115] The determining unit 22 is used to determine the target charging current based on the simulation data information of the current waveform to be analyzed;

[0116] Control unit 23 is used to control the battery to perform charging operation by means of the target charging current.

[0117] In some examples, the phase-field model of the lithium dendrites described above is constructed based on the order parameter equation, the lithium-ion diffusion equation, and the charge conservation equation.

[0118] In some examples, the above order parameter equations are order parameter equations that combine nonlinear models that vary in time and space with Butler-Volmer dynamics;

[0119] The above lithium-ion diffusion equation is the Nernst-Planck equation;

[0120] The charge conservation equation described above is the current density conservation equation described by the Poisson equation.

[0121] In some examples, the battery mesh model described above is a phase-field simulation model of the lithium coating morphology in a two-dimensional half-cell electrodeposition system. The battery network model is discretized using quadrilateral meshes and employs adaptive meshing technology.

[0122] In some examples, the above constraint information includes boundary condition information, lithium ion concentration information, and upper and lower boundary potential information.

[0123] In some examples, the aforementioned secondary current distribution module is used to describe the relationship between electrode dynamics and impedance.

[0124] In some examples, the simulation data mentioned above includes space utilization and aspect ratio;

[0125] The current waveform to be analyzed above is generated based on a sine wave.

[0126] like Figure 15 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-described charging control methods.

[0127] Since the electronic device described in this embodiment is a device used to implement a charging control device in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0128] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0129] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0130] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to execute the charging control method flow in the corresponding embodiment.

[0135] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0136] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0139] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0140] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0141] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A charging control method, characterized in that, include: The waveform of the current to be analyzed is input into the simulation model to obtain simulation data information of the waveform. The simulation model is established based on the phase-field model of lithium dendrites, the battery network model, constraint information, and the secondary current distribution module. The waveform of the current to be analyzed is generated based on a sine wave. The battery network model is a phase-field simulation model of the lithium coating morphology in a two-dimensional half-cell electrodeposition system. The battery network model is discretized with quadrilateral grids and adopts adaptive grid technology. The constraint information includes boundary condition information, lithium ion concentration information, and upper and lower boundary potential information. The target charging current is determined based on the simulation data of the current waveform to be analyzed; The battery is charged by controlling the target charging current.

2. The charging control method according to claim 1, characterized in that, The phase-field model of lithium dendrites is constructed based on the order parameter equation, the lithium-ion diffusion equation, and the charge conservation equation.

3. The charging control method according to claim 2, characterized in that, The order parameter equation is a nonlinear model that varies in time and space, combined with the order parameter equation of Butler-Volmer dynamics. The lithium-ion diffusion equation is the Nernst-Planck equation. The charge conservation equation is the current density conservation equation described by the Poisson equation.

4. The charging control method according to claim 1, characterized in that, The secondary current distribution module is used to describe the relationship between electrode dynamics and impedance.

5. The charging control method according to claim 1, characterized in that, The simulation data includes space utilization and aspect ratio.

6. A charging control device, characterized in that, include: The acquisition unit is used to input the waveform of the current to be analyzed into the simulation model to obtain the simulation data information of the waveform. The simulation model is established based on a lithium dendrite phase-field model, a battery network model, constraint information, and a secondary current distribution module. The waveform of the current to be analyzed is generated based on a sine wave. The battery network model is a phase-field simulation model of the lithium coating morphology in a two-dimensional half-cell electrodeposition system. The battery network model uses a quadrilateral grid for discretization and employs adaptive grid technology. The constraint information includes boundary condition information, lithium ion concentration information, and upper and lower boundary potential information. The determining unit is used to determine the target charging current based on the simulation data information of the current waveform to be analyzed; A control unit is used to control the battery to perform a charging operation based on the target charging current.

7. An electronic device, comprising: The memory and processor are characterized in that the processor, when executing a computer program stored in the memory, implements the steps of the charging control method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the charging control method as described in any one of claims 1-5.

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