A method for designing non-flammable phosphate electrolytes compatible with graphite anodes using machine learning

Abstract

The present application belongs to the field of lithium battery safety technology, and specifically discloses a method for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning, wherein the method comprises: inputting fixed components contained in a target phosphate electrolyte into a machine learning model, obtaining a first molar ratio critical value of a main solvent and a phosphate solvent, so as to characterize a critical number of phosphate molecules compatible with the graphite negative electrode; setting constraints and optimization objectives, including: setting a first constraint based on the first molar ratio critical value, setting a second constraint based on the flame retardancy of the electrolyte, and setting a third constraint based on the ionic conductivity of the electrolyte; maximizing the compatibility with the graphite negative electrode as the first optimization objective, maximizing the flame retardancy as the second optimization objective, and maximizing the ion transmission capacity as the third optimization objective; constructing a phosphate electrolyte ratio optimization model based on the set constraints and optimization objectives and solving the model to determine the optimal ratio of the target phosphate electrolyte.

Description

Technical Field

[0001] The present application belongs to the field of lithium battery safety technology, and more specifically, relates to a method for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning. Background Art

[0002] Lithium-ion batteries are widely used in consumer electronics, electric vehicles, and energy storage. However, the electrolyte within the battery is highly flammable and volatile, leading to frequent lithium battery safety incidents caused by this electrolyte in recent years. The development of non-flammable electrolytes not only improves the intrinsic safety of batteries but also prevents fires from igniting adjacent batteries, reducing the risk of secondary disasters and ultimately enhancing the safety of batteries and energy storage systems.

[0003] Phosphate flame-retardant solvents are considered an ideal flame-retardant solvent for the development of non-flammable electrolytes due to their low cost, excellent flame retardancy, and compatibility with existing commercial carbonate electrolytes. However, phosphate flame retardants strongly interact with lithium ions, tending to form in the main solvation shell and participate in the formation of the solid electrolyte interface (SEI) film. However, due to the poor electron shielding ability of their products, the SEI film formed is unable to effectively prevent the continued decomposition of the solvent, which in turn causes failure of the graphite anode. This has limited the development of phosphate-based non-flammable electrolytes. Summary of the Invention

[0004] In response to the defects of the related art, the embodiments of the present application provide a method for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning, aiming to solve the problem in the related art that the phosphate electrolyte easily causes the graphite negative electrode to fail.

[0005] In a first aspect, embodiments of the present application provide a method for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning, comprising:

[0006] Inputting fixed components contained in the target phosphate ester electrolyte into a machine learning model to obtain a first molar ratio critical value of the main solvent and the phosphate ester solvent in the target phosphate ester electrolyte, wherein the first molar ratio critical value is used to characterize the critical number of phosphate molecules allowed to exist in the target phosphate ester electrolyte when it is compatible with the graphite negative electrode;

[0007] Setting constraints and optimization objectives, including: setting a first constraint condition for compatibility of the target phosphate ester electrolyte with the graphite negative electrode based on a first molar ratio critical value, setting a second constraint condition based on the flame retardancy of the target phosphate ester electrolyte, and setting a third constraint condition based on the ionic conductivity of the target phosphate ester electrolyte; maximizing the compatibility of the target phosphate ester electrolyte with the graphite negative electrode as the first optimization objective, maximizing the flame retardancy of the target phosphate ester electrolyte as the second optimization objective, and maximizing the ion transport capacity of the target phosphate ester electrolyte as the third optimization objective;

[0008] Construct a phosphate electrolyte ratio optimization model based on the set constraints and optimization objectives;

[0009] The phosphate ester electrolyte formulation optimization model is solved to determine the optimal ratio of the target phosphate ester electrolyte.

[0010] In some embodiments, whether the phosphate electrolyte is compatible with the graphite negative electrode is characterized by the performance parameters of the graphite negative electrode, which include the first-cycle charge and discharge efficiency of the graphite, the reversible capacity and capacity retention rate of the graphite negative electrode during the cycle.

[0011] In some embodiments, the machine learning model is trained based on the following steps:

[0012] Obtaining the molar ratio of the main solvent to the phosphate ester solvent in non-flammable phosphate ester electrolyte samples containing different components and different ratios, as well as the corresponding performance parameters of the graphite anode;

[0013] Based on the performance parameters of the graphite anode, the critical value of the first molar ratio of the main solvent to the phosphate ester solvent in the non-flammable phosphate ester electrolyte samples containing different components is determined to construct training samples;

[0014] A machine learning model is trained based on the training samples to learn the mapping relationship between the components contained in the non-flammable phosphate electrolyte sample and the first molar ratio critical value.

[0015] In some embodiments, the target phosphate electrolyte comprises a lithium salt, an organic non-flammable phosphate solvent, an organic carbonate solvent, a low-viscosity organic carbonate solvent, and an additive, wherein the organic carbonate solvent is a main solvent.

[0016] In some embodiments, the flame retardancy of the target phosphate ester electrolyte is characterized by a second critical molar ratio of the low-viscosity organic carbonate solvent to the organic non-flammable phosphate ester solvent, and the ionic conductivity of the target phosphate ester electrolyte is characterized by a third critical molar ratio of the lithium salt to the organic non-flammable phosphate ester solvent.

[0017] In some embodiments, the first constraint is:

[0018] The molar ratio of the organic carbonate solvent to the organic non-flammable phosphate solvent in the target phosphate electrolyte is greater than or equal to a first molar ratio critical value;

[0019] The second constraint is:

[0020] The molar ratio of the low-viscosity organic carbonate solvent to the organic non-flammable phosphate solvent in the target phosphate electrolyte is less than or equal to a second molar ratio critical value;

[0021] The third constraint is:

[0022] The molar ratio of the lithium salt to the organic non-flammable phosphate ester solvent in the target phosphate ester electrolyte is less than or equal to a third molar ratio critical value.

[0023] In some embodiments, the organic non-flammable phosphate solvent has the general formula:

[0024]

[0025] wherein R1 and R2 are each independently one of an alkyl group having 1 to 10 carbon atoms and a haloalkyl group having 1 to 10 carbon atoms, and R3 is an alkyl group having 1 to 10 carbon atoms or an alkoxy group having 1 to 10 carbon atoms.

[0026] In some embodiments, the phosphate electrolyte, when compatible with the graphite negative electrode, meets the following requirements: the first cycle charge and discharge efficiency of the graphite negative electrode is ≥85%, the reversible capacity of the graphite negative electrode during the cycle is ≥360 mAh / g, and the capacity retention rate of the graphite negative electrode after 100 cycles is ≥90%.

[0027] In some embodiments, solving a phosphate ester electrolyte formulation optimization model includes:

[0028] The phosphate electrolyte formulation optimization model was solved using the NSGA-II algorithm.

[0029] In a second aspect, an embodiment of the present application further provides a device for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning, comprising:

[0030] An acquisition module is used to input a fixed component contained in a target phosphate ester electrolyte into a machine learning model to obtain a first molar ratio critical value of the main solvent and the phosphate ester solvent in the target phosphate ester electrolyte, where the first molar ratio critical value is used to characterize a critical number of phosphate molecules allowed to exist in the target phosphate ester electrolyte when it is compatible with a graphite negative electrode;

[0031] A setting module for setting constraints and optimization objectives, including: setting a first constraint condition for compatibility of the target phosphate ester electrolyte with the graphite negative electrode based on a first molar ratio critical value, setting a second constraint condition based on the flame retardancy of the target phosphate ester electrolyte, and setting a third constraint condition based on the ionic conductivity of the target phosphate ester electrolyte; maximizing the compatibility of the target phosphate ester electrolyte with the graphite negative electrode as the first optimization objective, maximizing the flame retardancy of the target phosphate ester electrolyte as the second optimization objective, and maximizing the ion transport capacity of the target phosphate ester electrolyte as the third optimization objective;

[0032] A construction module is used to construct a phosphate electrolyte ratio optimization model based on set constraints and optimization objectives;

[0033] The determination module is used to solve the phosphate ester electrolyte formula optimization model and determine the optimal ratio of the target phosphate ester electrolyte.

[0034] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0035] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0036] In a fifth aspect, an embodiment of the present application further provides a computer program product, which, when running on a processor, enables the processor to execute the method described in the first aspect or any possible implementation of the first aspect.

[0037] An embodiment of the present application provides a method for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning. A machine learning model is used to obtain a first critical value of the molar ratio of a main solvent and a phosphate solvent in a target phosphate electrolyte containing fixed components to characterize the critical number of phosphate molecules allowed to exist in the target phosphate electrolyte under the condition of compatibility with a graphite negative electrode. At the same time, considering the graphite negative electrode compatibility, flame retardancy, and ion transport capacity of the target phosphate electrolyte, constraints and optimization objectives are set to construct a phosphate electrolyte ratio optimization model with multiple constraints and multiple optimization objectives. The model is solved to obtain the optimal ratio of the target phosphate electrolyte that takes into account graphite negative electrode compatibility, flame retardancy, and ion transport capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in this application or related technologies, the following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] Figure 1 1 is a flow chart of a method for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning, as provided in an embodiment of the present application;

[0040] Figure 2 1 is a schematic structural diagram of a device for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning, provided in an embodiment of the present application;

[0041] Figure 3 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0043] Figure 1 is a flow chart of a method for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning provided in an embodiment of the present application, such as Figure 1 As shown, the method includes at least the following steps:

[0044] S101. Inputting fixed components contained in the target phosphate ester electrolyte into a machine learning model to obtain a first molar ratio critical value of the main solvent and the phosphate ester solvent in the target phosphate ester electrolyte.

[0045] The first molar ratio critical value is used to characterize the critical number of phosphate molecules allowed to exist in the target phosphate electrolyte when it is compatible with the graphite negative electrode.

[0046] Specifically, phosphate ester electrolytes use phosphate ester compounds as the primary solvent or additive. Phosphate ester compounds generally have good flame retardancy and exhibit good electrochemical stability at specific salt-to-solvent molar ratios. Phosphate ester electrolytes also form a SEI film on the surface of battery electrodes, which helps inhibit electrolyte decomposition and electrode corrosion.

[0047] However, phosphate flame retardants have strong interactions with lithium ions and are prone to appear in the main solvation structure shell and participate in the formation of SEI film. However, due to the poor ability of their products to shield electrons, the SEI film formed is difficult to effectively prevent the continuous decomposition of the solvent, thereby causing failure of the graphite negative electrode.

[0048] Optionally, whether the phosphate electrolyte is compatible with the graphite negative electrode is characterized by the performance parameters of the graphite negative electrode, which include the first-cycle charge and discharge efficiency of the graphite (i.e., coulombic efficiency), the reversible capacity of the graphite negative electrode during the cycle, and the capacity retention rate after a preset number of cycles.

[0049] The compatibility of phosphate ester electrolytes with graphite anodes is related to the number of phosphate ester molecules in the electrolyte. Currently, the following method is generally used to obtain the critical number of phosphate ester molecules for a phosphate ester electrolyte with a fixed composition that is compatible with a graphite anode: evaluate the compatibility of multiple groups of phosphate ester electrolytes with the same composition but different ratios with the graphite anode, and observe the initial coulombic efficiency, reversible capacity and capacity retention rate of the graphite anode during the cycle; then use Raman spectroscopy to analyze the number of phosphate ester molecules in phosphate ester electrolytes with different ratios, construct binary data (performance parameters of the graphite anode, number of phosphate ester molecules), and select the critical number of phosphate ester molecules for the phosphate ester electrolyte containing the current composition.

[0050] However, the cycle of manual screening is long, the process is cumbersome, and the result error is large. Therefore, in the embodiment of the present application, it is considered to use machine learning to clarify the physicochemical properties of the solvent and the interaction between the solvent and lithium ions. At the same time, since the statistics of the critical number of phosphate molecules require strict variable control of phosphate electrolytes with different ratios, the embodiment of the present application considers characterizing the critical number of phosphate molecules by the critical value of the molar ratio between the main solvent and the phosphate solvent in the phosphate electrolyte (denoted as the first molar ratio critical value), thereby reducing statistical requirements and complexity.

[0051] When the molar ratio of the main solvent to the phosphate ester solvent in the target phosphate ester electrolyte is greater than or equal to the first molar ratio critical value, it indicates that the target phosphate ester electrolyte is compatible with the graphite anode. Therefore, constraints can be set based on the first molar ratio critical value to participate in solving the optimal ratio of the phosphate ester electrolyte compatible with the graphite anode.

[0052] The machine learning model is pre-trained to learn the mapping relationship between the components of phosphate ester electrolytes with different compositions and the critical first molar ratio of the main solvent to the phosphate ester solvent when compatible with a graphite anode. The fixed components of the target phosphate ester electrolyte are input into the trained machine learning model, and the machine learning model outputs the critical first molar ratio of the main solvent to the phosphate ester solvent in the target phosphate ester electrolyte, which represents the critical number of phosphate molecules allowed to be present in the target phosphate ester electrolyte when compatible with a graphite anode.

[0053] Optionally, when the target phosphate electrolyte is compatible with the graphite negative electrode, it meets the following requirements: the first cycle charge and discharge efficiency of the graphite is ≥85%, the reversible capacity of the graphite negative electrode during the cycle is ≥360mAh / g, and the capacity retention rate of the graphite negative electrode after 100 cycles is ≥90%.

[0054] S102. Set constraints and optimization goals.

[0055] Setting constraints includes: setting a first constraint that the target phosphate electrolyte is compatible with the graphite negative electrode based on a first molar ratio critical value, setting a second constraint based on the flame retardancy of the target phosphate electrolyte, and setting a third constraint based on the ionic conductivity of the target phosphate electrolyte.

[0056] Optimization goals are set, including: maximizing the graphite negative electrode compatibility of the target phosphate ester electrolyte as a first optimization goal, maximizing the flame retardancy of the target phosphate ester electrolyte as a second optimization goal, and maximizing the ion transport capacity of the target phosphate ester electrolyte as a third optimization goal.

[0057] S103. Construct a phosphate electrolyte ratio optimization model based on the set constraints and optimization objectives.

[0058] Specifically, in the design process of non-flammable phosphate electrolyte compatible with graphite negative electrode, the phosphate electrolyte is pursued to have graphite negative electrode compatibility, flame retardancy (non-flammability) and fast ion transport capability. Based on this, constraints and optimization goals are set, and a phosphate electrolyte ratio optimization model with multiple constraints and multiple optimization goals is constructed.

[0059] The phosphate electrolyte's graphite anode compatibility and flame retardancy conflict, making it impossible to maximize both simultaneously. A trade-off is necessary. Ionic conductivity refers to the ability of ions in an electrolyte salt to conduct current, reflecting the rate and efficiency of ion migration. Generally speaking, higher ionic conductivity indicates faster ion migration in the electrolyte, better ion transport capacity, and better conductivity.

[0060] S104. Solve the phosphate ester electrolyte formula optimization model to determine the optimal ratio of the target phosphate ester electrolyte.

[0061] Specifically, by solving a phosphate ester electrolyte formulation optimization model with multiple constraints and multiple optimization objectives, the optimal ratio of the phosphate ester electrolyte containing fixed components is obtained.

[0062] A multi-constraint, multi-optimization objective phosphate ester electrolyte formulation optimization model includes multiple constraints and multiple optimization objectives, where the first optimization objective conflicts with the second. Various solutions are available, including weighted methods, Pareto optimality, genetic algorithms, and particle swarm optimization.

[0063] Preferably, the second-generation Nondominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the multi-constraint, multi-optimization objective phosphate electrolyte formulation optimization model. The core concept of the NSGA-II algorithm is to use a genetic algorithm to find the Pareto frontier of a multi-objective optimization problem. First, the population is non-dominated sorted, that is, the population is stratified according to the dominance relationship between the solutions. Then, the crowding ratio is used to maintain the diversity of the population, ensuring that the algorithm can search for a uniformly distributed Pareto optimal solution set.

[0064] The embodiment of the present application provides a method for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning. The first molar ratio critical value of the main solvent and the phosphate solvent in the target phosphate electrolyte containing fixed components is obtained through a machine learning model to characterize the critical number of phosphate molecules allowed to exist in the target phosphate electrolyte under the condition of compatibility with the graphite negative electrode; at the same time, considering the graphite negative electrode compatibility, flame retardancy and ion transport capacity of the target phosphate electrolyte, constraints and optimization objectives are set, and a phosphate electrolyte ratio optimization model with multiple constraints and multiple optimization objectives is constructed. The model is solved to obtain the optimal ratio of the target phosphate electrolyte when taking into account the graphite negative electrode compatibility, flame retardancy and ion transport capacity.

[0065] In some embodiments, the machine learning model in S101 is trained based on the following steps:

[0066] Obtaining the molar ratio of the main solvent to the phosphate ester solvent in non-flammable phosphate ester electrolyte samples containing different components and different ratios, as well as the corresponding performance parameters of the graphite anode;

[0067] Based on the performance parameters of the graphite anode, the critical value of the first molar ratio of the main solvent to the phosphate ester solvent in the non-flammable phosphate ester electrolyte samples containing different components is determined to construct training samples;

[0068] A machine learning model is trained based on the training samples to learn the mapping relationship between the components contained in the non-flammable phosphate electrolyte sample and the first molar ratio critical value.

[0069] Specifically, the machine learning model is obtained through pre-training, and the general process of pre-training is: to obtain as many numbers of phosphate molecules as possible of non-flammable phosphate electrolyte samples containing different components and different proportions, as well as the performance parameters of the corresponding graphite negative electrode. Since only non-flammable phosphate electrolytes are subsequently screened and optimized, only non-flammable phosphate electrolyte samples are counted during the pre-training stage of machine learning. Optionally, whether the phosphate electrolyte sample is flammable can be evaluated by an ignition test. Optionally, the number of phosphate molecules can be obtained by Raman spectroscopy analysis. Specifically, statistics can be performed through a ternary array (non-flammability of the phosphate electrolyte, performance parameters of the graphite negative electrode, and number of phosphate molecules).

[0070] To reduce statistical requirements, the critical number of phosphate molecules is characterized by the critical value of the molar ratio between the main solvent and the phosphate ester solvent (i.e., the first molar ratio critical value). The performance parameters of the graphite anode are observed, and the first molar ratio critical value of the main solvent and the phosphate ester solvent in non-flammable phosphate ester electrolyte samples with different components is determined to construct a training sample. The performance parameters of each non-flammable phosphate ester electrolyte sample are observed, including the first-cycle charge and discharge efficiency of graphite, the reversible capacity of the graphite anode during cycling, and the capacity retention rate.

[0071] A machine learning model is trained based on the training samples to learn the mapping relationship between the components of the non-flammable phosphate ester electrolyte and the first molar ratio critical value. It is conceivable that, under the premise of strictly controlling the solvent solute content of the non-flammable phosphate ester electrolyte sample, the machine learning model can also be directly used to learn the mapping relationship between the components contained in the non-flammable phosphate ester electrolyte sample and the critical number of phosphate ester molecules.

[0072] In some embodiments, the phosphate electrolyte comprises a lithium salt, an organic non-flammable phosphate solvent, an organic carbonate solvent A, a low-viscosity organic carbonate solvent B, and additives, wherein the organic carbonate solvent A is used as the main solvent.

[0073] Optionally, the graphite anode compatibility of the target phosphate ester electrolyte is characterized by a first molar ratio critical value between the organic carbonate solvent A and the organic non-flammable phosphate ester solvent. When the molar ratio of the organic carbonate solvent A to the organic non-flammable phosphate ester solvent in the target phosphate ester electrolyte is greater than or equal to the first molar ratio critical value, the target phosphate ester electrolyte is considered compatible with the graphite anode. Thus, the first constraint condition is set as: the molar ratio of the organic carbonate solvent A to the organic non-flammable phosphate ester solvent in the target phosphate ester electrolyte is greater than or equal to the first molar ratio critical value.

[0074] Optionally, the flame retardancy of the target phosphate ester electrolyte is characterized by a second molar ratio critical value of the low-viscosity organic carbonate solvent B and the organic non-flammable phosphate ester solvent. When the flame retardancy of the target phosphate ester electrolyte is characterized by a molar ratio of the low-viscosity organic carbonate solvent B to the organic non-flammable phosphate ester solvent being less than or equal to the second molar ratio critical value, the target phosphate ester electrolyte is considered non-flammable. Thus, the second constraint condition is set as: the molar ratio of the low-viscosity organic carbonate solvent B to the organic non-flammable phosphate ester solvent in the target phosphate ester electrolyte is less than or equal to the second molar ratio critical value.

[0075] Optionally, the ionic conductivity of the target phosphate ester electrolyte is characterized by a third molar ratio critical value of the lithium salt and the organic non-flammable phosphate ester solvent. When the molar ratio of the lithium salt to the organic non-flammable phosphate ester solvent in the target phosphate ester electrolyte is less than or equal to the third molar ratio critical value, the target phosphate ester electrolyte is considered to have fast ion transport capability. Therefore, the third constraint condition is set as: the molar ratio of the lithium salt to the organic non-flammable phosphate ester solvent in the target phosphate ester electrolyte is less than or equal to the third molar ratio critical value.

[0076] The second molar ratio critical value and the third molar ratio critical value are preset.

[0077] Optionally, the lithium salt is at least one of lithium hexafluorophosphate, lithium difluorophosphate, lithium bis(trifluoromethylsulfonyl)imide, lithium bis(fluorosulfonyl)imide, lithium tetrafluoroborate, and lithium bis(oxalatoborate). Optionally, the concentration of the lithium salt is 0.8 to 2.0 mol / L.

[0078] Optionally, the organic non-flammable phosphate solvent has the general structural formula:

[0079]

[0080] wherein R1 and R2 are each independently one of an alkyl group having 1 to 10 carbon atoms and a haloalkyl group having 1 to 10 carbon atoms, and R3 is an alkyl group having 1 to 10 carbon atoms or an alkoxy group having 1 to 10 carbon atoms.

[0081] Optionally, the organic carbonate solvent A is at least one of ethylene carbonate, propylene carbonate, and fluoroethylene carbonate.

[0082] Optionally, the low-viscosity organic carbonate solvent B is at least one of dimethyl carbonate, diethyl carbonate, ethyl methyl carbonate, dipropyl carbonate, methyl trifluoroethyl carbonate, and bis(2,2,2-trifluoroethyl) carbonate.

[0083] Optionally, the additive is at least one of ethylene carbonate, 1,3-propane sultone, bromobenzene, hexafluorobenzene, bis(2,2,2-trifluoroethyl) ether, 1,1,1,3,3,3-hexafluoroisopropyl methyl ether, 1,1,2,2-tetrafluoroethyl ether-2,2,3,3-tetrafluoropropyl ether, 1,1,2,2-tetrafluoroethyl-2,2,2-trifluoroethyl ether, tris(2,2,2-trifluoroethyl)orthoformate, 1H,1H,5H-octafluoropentyl-1,1,2,2-tetrafluoroethyl ether, 1-(2,2,2-trifluoroethoxy)-1,1,2,2-tetrafluoroethane, hexafluorocyclotriphosphazene, ethoxy(pentafluoro)cyclotriphosphazene, lithium difluorooxalatoborate, lithium dioxalatoborate, vinyl sulfate, 1,3-propane sultone, and lithium difluorodioxyphosphazene.

[0084] Figure 2 Schematic diagram of the structure of a device for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning provided in an embodiment of the present application, such as Figure 2 As shown, the device at least includes:

[0085] An acquisition module 201 is configured to input a fixed component contained in a target phosphate ester electrolyte into a machine learning model to obtain a first critical molar ratio of a main solvent to a phosphate ester solvent in the target phosphate ester electrolyte, wherein the first critical molar ratio is used to characterize a critical number of phosphate molecules allowed to exist in the target phosphate ester electrolyte while being compatible with a graphite negative electrode;

[0086] Setting module 202, for setting constraints and optimization objectives, including: setting a first constraint condition for compatibility of the target phosphate ester electrolyte with the graphite negative electrode based on the first molar ratio critical value, setting a second constraint condition based on the flame retardancy of the target phosphate ester electrolyte, and setting a third constraint condition based on the ionic conductivity of the target phosphate ester electrolyte; maximizing the compatibility of the target phosphate ester electrolyte with the graphite negative electrode as the first optimization objective, maximizing the flame retardancy of the target phosphate ester electrolyte as the second optimization objective, and maximizing the ion transport capacity of the target phosphate ester electrolyte as the third optimization objective;

[0087] A construction module 203 is used to construct a phosphate electrolyte ratio optimization model based on set constraints and optimization objectives;

[0088] The determination module 204 is used to solve the phosphate ester electrolyte formulation optimization model to determine the optimal ratio of the target phosphate ester electrolyte.

[0089] In some embodiments, whether the phosphate electrolyte is compatible with the graphite negative electrode is characterized by the performance parameters of the graphite negative electrode, which include the first-cycle charge and discharge efficiency of the graphite, the reversible capacity and capacity retention rate of the graphite negative electrode during the cycle.

[0090] In some embodiments, the machine learning model is trained based on the following steps:

[0091] Obtaining the molar ratio of the main solvent to the phosphate ester solvent in non-flammable phosphate ester electrolyte samples containing different components and different ratios, as well as the corresponding performance parameters of the graphite anode;

[0092] Based on the performance parameters of the graphite anode, the critical value of the first molar ratio of the main solvent to the phosphate ester solvent in the non-flammable phosphate ester electrolyte samples containing different components is determined to construct training samples;

[0093] A machine learning model is trained based on the training samples to learn the mapping relationship between the components contained in the non-flammable phosphate electrolyte sample and the first molar ratio critical value.

[0094] In some embodiments, the target phosphate electrolyte comprises a lithium salt, an organic non-flammable phosphate solvent, an organic carbonate solvent, a low-viscosity organic carbonate solvent, and an additive, wherein the organic carbonate solvent is a main solvent.

[0095] In some embodiments, the flame retardancy of the target phosphate ester electrolyte is characterized by a second critical molar ratio of the low-viscosity organic carbonate solvent to the organic non-flammable phosphate ester solvent, and the ionic conductivity of the target phosphate ester electrolyte is characterized by a third critical molar ratio of the lithium salt to the organic non-flammable phosphate ester solvent.

[0096] In some embodiments, the first constraint is:

[0097] The molar ratio of the organic carbonate solvent to the organic non-flammable phosphate solvent in the target phosphate electrolyte is greater than or equal to a first molar ratio critical value;

[0098] The second constraint is:

[0099] The molar ratio of the low-viscosity organic carbonate solvent to the organic non-flammable phosphate solvent in the target phosphate electrolyte is less than or equal to a second molar ratio critical value;

[0100] The third constraint is:

[0101] The molar ratio of the lithium salt to the organic non-flammable phosphate ester solvent in the target phosphate ester electrolyte is less than or equal to a third molar ratio critical value.

[0102] In some embodiments, the organic non-flammable phosphate solvent has the general formula:

[0103]

[0104] wherein R1 and R2 are each independently one of an alkyl group having 1 to 10 carbon atoms and a haloalkyl group having 1 to 10 carbon atoms, and R3 is an alkyl group having 1 to 10 carbon atoms or an alkoxy group having 1 to 10 carbon atoms.

[0105] In some embodiments, the phosphate electrolyte, when compatible with the graphite negative electrode, meets the following requirements: the first cycle charge and discharge efficiency of the graphite negative electrode is ≥85%, the reversible capacity of the graphite negative electrode during the cycle is ≥360 mAh / g, and the capacity retention rate of the graphite negative electrode after 100 cycles is ≥90%.

[0106] In some embodiments, the determination module 204 is specifically configured to:

[0107] The phosphate electrolyte formulation optimization model was solved using the NSGA-II algorithm.

[0108] It is understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the description of the aforementioned method embodiment and will not be described in detail here. It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment, and the corresponding program modules in the device have similar implementation principles and technical effects as those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method and will not be described in detail here.

[0109] Based on the methods described in the above embodiments, embodiments of the present application provide an electronic device. The device may include: at least one memory for storing programs and at least one processor for executing the programs stored in the memory. When the programs stored in the memory are executed, the processor is configured to execute the methods described in the above embodiments.

[0110] Figure 3 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 3 As shown, the electronic device may include: a processor 301, a communication interface 302, a memory 303, and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other via the communication bus 304. The processor 301 may call software instructions in the memory 303 to execute the method described in the above embodiment.

[0111] In addition, the logic instructions in the above-mentioned memory 303 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology, or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present application.

[0112] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0113] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0114] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0115] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0116] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0117] It will be understood that the various numerical numbers involved in the embodiments of the present application are merely distinctions for the convenience of description and are not intended to limit the scope of the embodiments of the present application.

[0118] It is easy for those skilled in the art to understand that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning, characterized in that: include: Inputting fixed components contained in a target phosphate ester electrolyte into a machine learning model to obtain a first molar ratio critical value of the main solvent and the phosphate ester solvent in the target phosphate ester electrolyte, wherein the first molar ratio critical value is used to characterize the critical number of phosphate molecules allowed to exist in the target phosphate ester electrolyte when it is compatible with a graphite negative electrode; Setting constraints and optimization targets, including: setting a first constraint that the target phosphate ester electrolyte is compatible with the graphite negative electrode based on the first molar ratio critical value, setting a second constraint based on the flame retardancy of the target phosphate ester electrolyte, and setting a third constraint based on the ionic conductivity of the target phosphate ester electrolyte; maximizing the compatibility of the target phosphate ester electrolyte with the graphite negative electrode as the first optimization target, maximizing the flame retardancy of the target phosphate ester electrolyte as the second optimization target, and maximizing the ion transport capacity of the target phosphate ester electrolyte as the third optimization target; Wherein, whether the phosphate electrolyte is compatible with the graphite negative electrode is characterized by the performance parameters of the graphite negative electrode, and the performance parameters include the first cycle charge and discharge efficiency of the graphite negative electrode, the reversible capacity and capacity retention rate during the cycle of the graphite negative electrode; The flame retardancy of the target phosphate ester electrolyte is characterized by a second critical molar ratio of the low-viscosity organic carbonate solvent to the organic non-flammable phosphate ester solvent, and the ionic conductivity of the target phosphate ester electrolyte is characterized by a third critical molar ratio of the lithium salt to the organic non-flammable phosphate ester solvent; The first constraint condition is that the molar ratio of the organic carbonate solvent to the organic non-flammable phosphate solvent in the target phosphate electrolyte is greater than or equal to the first molar ratio critical value; The second constraint condition is that the molar ratio of the low-viscosity organic carbonate solvent to the organic non-flammable phosphate solvent in the target phosphate electrolyte is less than or equal to the second molar ratio critical value; The third constraint condition is that the molar ratio of the lithium salt to the organic non-flammable phosphate ester solvent in the target phosphate ester electrolyte is less than or equal to the third molar ratio critical value; Construct a phosphate electrolyte ratio optimization model based on the set constraints and optimization objectives; The phosphate ester electrolyte formulation optimization model is solved to determine the optimal ratio of the target phosphate ester electrolyte.

2. The method according to claim 1, characterized in that The machine learning model is trained based on the following steps: Obtaining the molar ratio of the main solvent to the phosphate ester solvent in non-flammable phosphate ester electrolyte samples containing different components and different ratios, as well as the corresponding performance parameters of the graphite anode; Based on the performance parameters of the graphite anode, the critical value of the first molar ratio of the main solvent to the phosphate ester solvent in the non-flammable phosphate ester electrolyte samples containing different components is determined to construct training samples; A machine learning model is trained based on the training samples to learn a mapping relationship between components contained in the non-flammable phosphate electrolyte sample and the first molar ratio critical value.

3. The method according to claim 1, characterized in that The target phosphate electrolyte comprises a lithium salt, an organic non-flammable phosphate solvent, an organic carbonate solvent, a low-viscosity organic carbonate solvent, and an additive, wherein the organic carbonate solvent is the main solvent.

4. The method according to claim 3, characterized in that The general structural formula of the organic non-flammable phosphate solvent is: wherein R1 and R2 are each independently one of an alkyl group having 1 to 10 carbon atoms and a haloalkyl group having 1 to 10 carbon atoms, and R3 is an alkyl group having 1 to 10 carbon atoms or an alkoxy group having 1 to 10 carbon atoms.

5. The method according to claim 1, wherein When the phosphate electrolyte is compatible with the graphite negative electrode, the following requirements are met: the first-cycle charge and discharge efficiency of the graphite is ≥85%, the reversible capacity of the graphite negative electrode during the cycle is ≥360 mAh / g, and the capacity retention rate of the graphite negative electrode after 100 cycles is ≥90%.

6. The method according to claim 1, wherein Solving the phosphate electrolyte formulation optimization model includes: The phosphate electrolyte formulation optimization model is solved using the NSGA-II algorithm.

7. A device for designing a non-flammable phosphate electrolyte compatible with a graphite negative electrode using machine learning, characterized in that: include: An acquisition module is used to input a fixed component contained in a target phosphate ester electrolyte into a machine learning model to obtain a first molar ratio critical value of the main solvent and the phosphate ester solvent in the target phosphate ester electrolyte, wherein the first molar ratio critical value is used to characterize a critical number of phosphate molecules allowed to exist in the target phosphate ester electrolyte when it is compatible with a graphite negative electrode; A setting module, for setting constraints and optimization targets, including: setting a first constraint condition for compatibility of the target phosphate ester electrolyte with the graphite negative electrode based on the first molar ratio critical value, setting a second constraint condition based on the flame retardancy of the target phosphate ester electrolyte, and setting a third constraint condition based on the ionic conductivity of the target phosphate ester electrolyte; maximizing the compatibility of the target phosphate ester electrolyte with the graphite negative electrode as the first optimization target, maximizing the flame retardancy of the target phosphate ester electrolyte as the second optimization target, and maximizing the ion transport capacity of the target phosphate ester electrolyte as the third optimization target; Wherein, whether the phosphate electrolyte is compatible with the graphite negative electrode is characterized by the performance parameters of the graphite negative electrode, and the performance parameters include the first cycle charge and discharge efficiency of the graphite negative electrode, the reversible capacity and capacity retention rate during the cycle of the graphite negative electrode; The flame retardancy of the target phosphate ester electrolyte is characterized by a second critical molar ratio of the low-viscosity organic carbonate solvent to the organic non-flammable phosphate ester solvent, and the ionic conductivity of the target phosphate ester electrolyte is characterized by a third critical molar ratio of the lithium salt to the organic non-flammable phosphate ester solvent; The first constraint condition is that the molar ratio of the organic carbonate solvent to the organic non-flammable phosphate solvent in the target phosphate electrolyte is greater than or equal to the first molar ratio critical value; The second constraint condition is that the molar ratio of the low-viscosity organic carbonate solvent to the organic non-flammable phosphate solvent in the target phosphate electrolyte is less than or equal to the second molar ratio critical value; The third constraint condition is that the molar ratio of the lithium salt to the organic non-flammable phosphate ester solvent in the target phosphate ester electrolyte is less than or equal to the third molar ratio critical value; A construction module is used to construct a phosphate electrolyte ratio optimization model based on set constraints and optimization objectives; The determination module is used to solve the phosphate electrolyte formula optimization model to determine the optimal ratio of the target phosphate electrolyte.

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