KR102631134B1 - Method for predicting lithium-ion battery performance, system using the same, and device comprising thereof - Google Patents

Abstract

The present invention relates to a method for predicting the electrochemical performance of a lithium-ion battery, specifically, a method for predicting battery performance according to an electrode active material with a two-dimensional heterogeneous stacked structure, and a system and device using the same.

Description

Lithium-ion battery performance prediction method and system and device using the same {METHOD FOR PREDICTING LITHIUM-ION BATTERY PERFORMANCE, SYSTEM USING THE SAME, AND DEVICE COMPRISING THEREOF}

The present invention relates to a method for predicting the electrochemical performance of a lithium-ion battery, specifically, a method for predicting battery performance according to an electrode active material with a two-dimensional heterogeneous stacked structure, and a system and device using the same.

This study was supported by the Korea Institute of Energy Research Research and Development Project (C0-2417) and the Korea Forest Service (Korea Forestry Promotion Agency) Forest Science and Technology Research and Development Project (2020229B10-2022-AC01).

Recently, various two-dimensional materials such as MoS ₂ , WS ₂ , and WSe ₂ are attracting attention as future semiconductor materials. The above two-dimensional structural materials are flexible and strong with a nanometer-thin thickness, and can be used in various fields such as electronic devices, sensors, and energy due to their metallic, semiconductor, and non-conducting properties. In particular, in the case of two-dimensional heterogeneous multilayer materials, there is an advantage in that it is possible to modulate physical properties according to the stacking combination of single layers and realize synergistic coupling between the physical properties of two-dimensional materials. For example, in the case of graphene/MoS ₂ heterogeneous stacked materials, it has been reported that charge accumulation increases by about 10 times due to intercalation reaction compared to MoS ₂ /MoS ₂ homogeneous stacked materials (Reference, P. Kim et al., Nature 558, 425 (2018)). In addition, it has been observed that a 1.3 nm thick ultra-thin graphene/MoS ₂ heterogeneous stacked (graphene/MoS ₂ /graphene) material exhibits a photocurrent that is 7 times higher than that of a MoS ₂ single stacked material (Reference, Nat. Commun 7, 13278 (2016)).

Two-dimensional transition metal dichalcogenide (TMD) materials and heterogeneous multilayer two-dimensional materials are expected to have great potential to be adopted in various high value-added application fields such as electronics, optoelectronics, energy, sensors, and biomedicine in the future. From $5.5 million in 2020, the total global market size for two-dimensional materials is expected to increase more than 23 times to about $130 million in 2030. In addition, the total domestic market size in 2030 is expected to be $17.8 million, and the compound annual growth rate (CAGR) between 2020 and 2030 is expected to be 36.72% (Reference, “Analysis of technology market trends in the field of non-graphene two-dimensional materials” , Electronics and Telecommunications Research Institute internal report, June 2016).

In addition, two-dimensional chalcogenide materials and heterogeneous multilayer two-dimensional materials have endless possibilities not only in the market for the materials themselves but also in their application fields, especially in high-efficiency, low-power devices and energy fields such as secondary batteries and thermoelectric devices. The potential for application in material development is very high.

Among various application fields, the lithium-ion battery market among secondary batteries is growing in size every year. Many things related to daily life are accomplished through electronic devices, and electronic devices have become an essential part of daily life. In addition, the demand for batteries, an essential element of electronic devices, is increasing, and the importance of developing better batteries is increasing. In the future, batteries will be used in more industries than now, and batteries with higher capacity and higher efficiency will be required than now.

Finding new materials through empirical and experimental efforts takes a lot of time and money. Significant efforts are currently underway, primarily in academia and laboratories, to use quantum simulation (QS) on high-performance computers to accelerate the search for new and better electrode materials for the battery industry. Quantum simulations based on first-principles full-density functional theory (DFT) or equivalent provide reliable computer simulations that can predict the properties of battery materials at the atomic scale for currently known cathodes, anodes, and electrolytes. The accuracy of QS-based predictions of electrode material properties has been demonstrated in a wide range of applications (e.g. semiconductor and pharmaceuticals).

Accordingly, the present inventors arrived at the present invention in response to market demands for a method of predicting battery performance according to electrode active materials with a two-dimensional heterogeneous layered structure, such as transition metal dichalcogenide, using quantum simulation.

The present invention was developed to solve the above-described problem, and one aspect of the present invention provides a method for predicting battery performance.

Another embodiment of the present invention provides a battery performance prediction system according to the above prediction method.

Another embodiment of the present invention provides a battery performance prediction device including the prediction system.

However, the technical problem to be achieved by the present invention is not limited to the technical problem mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below. It could be.

As a technical means for achieving the above-described technical problem, one aspect of the present invention is,

A method of predicting battery performance according to electrode active materials of heterogeneous stacked structure, comprising: providing a database on two-dimensional structures; generating a two-dimensional heterogeneous layered structure based on two or more of the two-dimensional structures selected by user input; Structurally optimizing an optimal two-dimensional heterogeneous stacked structure selected by a user's input among the plurality of two-dimensional heterogeneous stacked structures; Searching for a cation insertion site in the structurally optimized two-dimensional heterogeneous stacked structure; Calculating a structure into which the cations are sequentially inserted; and deriving discharge voltage and capacity from the calculation results.

The cation may be a lithium ion (Li ⁺ ).

The battery may be a lithium-ion battery, and the electrode may be an anode.

The two-dimensional structure of the database may be provided in the CIF or POSCAR support format.

The step of generating the two-dimensional heterogeneous stacked structure is to automatically search for a super-cell combination with the smallest lattice parameter mismatch within given conditions from the two-dimensional structure to create a heterogeneous stacked structure. It may be creating a .

The step of generating the two-dimensional heterogeneous layered structure may be a search based on the maximum lattice constant, minimum lattice constant, maximum mismatch range, or interlayer distance set in advance by the user.

In the step of generating the two-dimensional heterogeneous layered structure, the vacuum area is detected regardless of the direction of the input structure, the out-of-plane direction is automatically set to the z-axis, and the super-cell The search involves converting a hexagonal structure into a rectangular structure, and maximizes the minimum distance between atoms between layers through an in-plane direction shift of each layer during stacking, as shown in Equation 1 below. Creating a structure could be:

[Equation 1]

The step of structurally optimizing the two-dimensional heterogeneous stacked structure may involve performing structural optimization through quantum chemical calculations based on density functional theory (DFT).

The step of searching for the cation insertion position of the two-dimensional heterogeneous stacked structure is to select candidate cation insertion sites through analysis of the Voronoi diagram of the structure, and to determine the primary cation at each position through DFT calculation in Equation 2 below. It may include calculating the insertion energy and determining the cation insertion location from the lowest cation insertion energy structure:

[Equation 2]

The step of calculating the structure into which the cation is sequentially inserted may include deriving the total insertion position based on the super-cell size of the layer that determines the most stable cation insertion position.

The step of calculating the structure in which the cation is sequentially inserted may be to calculate the structure in which the cation is inserted at the insertion position furthest from the previously inserted cation atoms at each step.

Calculating the insertion structure at the insertion position furthest from the previously inserted cation atoms in each step above may be DFT calculation of the cation insertion structure in which Equation 3 below is maximum when determining the i-th cation insertion position. there is:

[Equation 3]

In Equation 3, Li _i is the i-th cation insertion site, Li _j is the j-th cation insertion site, May be the distance between the ith cation insertion site and the jth cation insertion site.

The step of deriving the discharge voltage and capacity from the calculation results may be deriving the half-cell voltage and maximum cation insertion capacity.

In addition, another aspect of the present invention is,

A battery performance prediction system according to the above prediction method is provided.

In addition, another aspect of the present invention is,

A battery performance prediction device including the prediction system is provided.

According to one aspect of the present invention, it is possible to predict an optimized two-dimensional heterogeneous stacked structure according to a combination of materials of various two-dimensional structures and calculate the voltage and capacity characteristics of the battery according to the structure, by combining the materials in the electrode active material. and battery performance according to structure can be predicted without excessive experimentation.

The effects of the present invention are not limited to the effects described above, and should be understood to include all effects that can be inferred from the configuration of the invention described in the detailed description or claims of the present invention.

Figure 1 represents a flowchart of a method for predicting battery performance according to an embodiment of the present invention.
Figure 2 illustrates the steps of creating and selecting the optimal heterogeneous stack structure in the battery performance prediction method according to an embodiment of the present invention.
Figure 3 is a table showing the interlayer distance error compared to the experimental value in the battery performance prediction method according to an embodiment of the present invention.
Figure 4 is an example of the step of searching for the optimal cation insertion position in the battery performance prediction method according to an embodiment of the present invention.
Figure 5 is an example of calculating voltage and capacity characteristics in the battery performance prediction method according to an embodiment of the present invention.

Hereinafter, the present invention will be described with reference to the attached drawings. However, the present invention may be implemented in various different forms and, therefore, is not limited to the embodiments described herein. In order to clearly explain the present invention in the drawings, parts that are not related to the description are omitted, and similar parts are given similar reference numerals throughout the specification.

Throughout the specification, when a part is said to be "connected (connected, contacted, combined)" with another part, this means not only "directly connected" but also "indirectly connected" with another member in between. "Includes cases where it is. Additionally, when a part is said to “include” a certain component, this does not mean that other components are excluded, but that other components can be added, unless specifically stated to the contrary.

The terms used herein are only used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “comprise” or “have” are intended to indicate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

In this specification, “module” includes a unit comprised of hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A module may be an integrated part, a minimum unit that performs one or more functions, or a part thereof. For example, the module may be composed of an application-specific integrated circuit (ASIC).

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The first aspect of the present application is,

A method of predicting battery performance according to electrode active materials of heterogeneous stacked structure, comprising: providing a database on two-dimensional structures; generating a two-dimensional heterogeneous layered structure based on two or more of the two-dimensional structures selected by user input; Structurally optimizing an optimal two-dimensional heterogeneous stacked structure selected by a user's input among the plurality of two-dimensional heterogeneous stacked structures; Searching for a cation insertion site in the structurally optimized two-dimensional heterogeneous stacked structure; Calculating a structure into which the cations are sequentially inserted; and deriving discharge voltage and capacity from the calculation results.

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Hereinafter, a method for predicting battery performance according to the first aspect of the present application will be described in detail with reference to the attached drawings.

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First, in one implementation of the present application, a step of providing a database for two-dimensional structures is included (100).

In one embodiment of the present application, the battery performance prediction method can generate all possible structures of two-dimensional heterogeneous stacked electrode materials such as graphene and transition metal dichalcogenide, as well as mixed variants such as composite materials thereof. Additionally, in one embodiment of the present application, the platform may provide screening of all electrode material structures produced according to their relative performance to provide higher nominal voltage, higher capacity for lithium-ion batteries. This platform technology can provide rapid screening of the voltage and capacity of any structure generated through quantum simulation of the database.

In one embodiment of the present application, a development engine, herein referred to as a development engine, allows a user to select two-dimensional structures among two-dimensional structures stored in a database through input, request calculations, generate capacity information, and specify screening criteria. A design and development engine at the battery electrode material to cell system level can be provided. These development engines may also include an easy-to-use graphical user interface (GUI) to perform these tasks.

Referring to FIG. 1, in one implementation of the present application, the two-dimensional structure of the database may be provided in a CIF or POSCAR support format. The application form is only a non-limiting example and is not limited thereto.

In one embodiment of the present application, the two-dimensional structure may include, but is not limited to, graphene, transition metal chalcogenide, or other two-dimensional structured material. When the two-dimensional structure is a transition metal dichalcogenide, it can be expressed as MX ₂ , where M is a transition metal, X is a chalcogen element, and M is a transition metal such as Mo, W, Nb, and Ti. _One selected _{from the} group consisting _of , _{and the 2} , ZrTe ₂ , and NbSe ₂ , and more preferably MoS ₂ , MoSe ₂ , or WS ₂ .

In one embodiment of the present application, the battery may be a lithium-ion battery, and the electrode may be an anode. Additionally, the cation may be a lithium ion (Li ⁺ ). However, this is a non-limiting example, and the prediction method of the present application can be used in the case of an energy storage device using an electrode active material with a two-dimensional heterogeneous laminated structure.

Next, as shown in FIG. 1, in one implementation of the present application, a step of generating a two-dimensional heterogeneous layered structure based on two or more of the two-dimensional structures selected by user input is included (200). .

In one embodiment of the present application, the step of generating the two-dimensional heterogeneous stacked structure automatically searches for a super-cell combination with the smallest mismatch in lattice parameters within given conditions from the two-dimensional structure. This may create a heterogeneous layered structure.

In one implementation of the present application, the user's input value may be a plurality of exfoliated two-dimensional structures including a vacuum, and preferably, two exfoliated two-dimensional structures may be the input value.

In one implementation of the present application, the step of generating the two-dimensional heterogeneous layered structure includes setting the maximum lattice constant, minimum lattice constant, maximum mismatch range, or interlayer distance set in advance by the user. It may be a search based on criteria. Preferably, as a default value, the maximum lattice constant is 20. , minimum lattice constant 5 , maximum mismatch range ±5%, interlayer distance 3.5 However, this is a non-limiting example and can be set by changing within an appropriate range.

In one implementation of the present application, the vacuum area is detected regardless of the direction of the input structure and the out-of-plane direction is automatically set to the z-axis, and when searching for a super-cell, a hexagon ( It involves converting a hexagonal structure into a rectangular structure, and creates a structure that maximizes the minimum distance between atoms between layers through an in-plane direction shift of each layer when stacked, as shown in Equation 1 below. You can:

[Equation 1]

In other words, it may mean finding the optimal layer shift that can minimize Equation 1 above.

Next, in one embodiment of the present application, a step 300 of structurally optimizing an optimal two-dimensional heterogeneous stacked structure selected by a user's input among the plurality of two-dimensional heterogeneous stacked structures is included.

In one embodiment of the present application, in the step of structurally optimizing the two-dimensional heterogeneous stacked structure, automated code for generating the two-dimensional heterogeneous stacked structure may be included and used. The code automatically creates a heterogeneous stacked structure from two separate hierarchical structures. Users can set limits on the longest lattice length, automatically search for all possible directions, and automatically optimize atomic arrangements (shifts) to minimize repulsion between adjacent atoms. An example of the automation code described above is shown in Figure 2.

In one embodiment of the present application, a user can select an appropriate structure from various generated two-dimensional heterogeneous stacked structures by considering mismatch and quantum chemical simulation time.

In one embodiment of the present application, the step of structurally optimizing the two-dimensional heterogeneous stacked structure may be performing structural optimization through quantum chemical calculations based on density functional theory (DFT). The optimal van der Waals correction algorithm obtained from optimization results can be applied to various two-dimensional structures.

Figure 3 is a table showing the interlayer distance error compared to the experimental value in the battery performance prediction method according to an embodiment of the present invention. Referring to Figure 3, it can be seen that the DFT calculation method performs a process of selecting an algorithm that can generally well correct the interlayer distance of representative two-dimensional structures in order to correct the van der Waals interaction, which cannot be directly simulated. .

Next, in one embodiment of the present application, a step of searching for a cation insertion position in the structurally optimized two-dimensional heterogeneous stacked structure is included (400).

In one embodiment of the present application, the step of searching for the cation insertion site of the two-dimensional heterogeneous stacked structure includes selecting cation insertion site candidates through Voronoi diagram analysis of the structure, and DFT calculation in Equation 2 below. It may include calculating the primary cation insertion energy for each position and determining the cation insertion position from the lowest cation insertion energy structure:

[Equation 2]

In one embodiment of the present application, the cation insertion site may be replaced with an anion hollow or metal site in the case of a transition metal dichalcogenide material.

Figure 4 is an example of the step of searching for the optimal cation insertion position in the battery performance prediction method according to an embodiment of the present invention. It can be seen that in the MoS ₂ /Graphene material, candidates for positions (numbers 1 to 5) where lithium ions can be inserted were selected from the hollow positions and anion binding positions of each layer, and the insertion energy of each candidate position was calculated. , it can be confirmed that the overall insertion position of lithium ions is determined by confirming the main dependence on the hollow position of a specific material among the two materials forming the interface based on the positions with the lowest calculated insertion energy.

Next, in one embodiment of the present application, a step of calculating a structure into which the cations are sequentially inserted is included (500). The step of calculating the structure into which the cation is sequentially inserted may include deriving the total insertion position based on the super-cell size of the layer that determines the most stable cation insertion position.

In one embodiment of the present application, the step of calculating the structure into which the cation is sequentially inserted may be calculating the structure of insertion at the insertion position furthest from the previously inserted cation atoms at each step. Calculating the insertion structure at the insertion position furthest from the previously inserted cation atoms in each step above may be DFT calculation of the cation insertion structure in which Equation 3 below is maximum when determining the i-th cation insertion position. there is:

[Equation 3]

In Equation 3, Li _i is the i-th cation insertion site, Li _j is the j-th cation insertion site, May be the distance between the ith cation insertion site and the jth cation insertion site.

Lastly, in one implementation of the present application, a step of deriving the discharge voltage and capacity from the calculation results is included (600).

In one embodiment of the present application, the step of deriving the discharge voltage and capacity from the calculation results may be deriving a half-cell voltage and a maximum cation insertion capacity. This step can be calculated by Equation 4 below.

[Equation 4]

In equation 4 above, class During the discharge process, two adjacent compositions of lithium ions in the electrode material M ( ), The composition of lithium ions is The Gibbs free energy when is the Gibbs free energy of lithium metal, The composition of lithium ions is This is the open circuit voltage (OCV). The maximum cation insertion capacity can be derived from the range where the OCV value is positive.

Figure 5 is an example of calculating voltage and capacity characteristics in the battery performance prediction method according to an embodiment of the present invention. Referring to Figure 5, it can be seen that the half-cell voltage and maximum cation insertion capacity were derived when the Graphene-NbSe2 heterogeneous stacked structure material was used as the electrode active material, and the insertion order of lithium ions was calculated using Equation 3 above. It can be seen that the half-cell voltage curve obtained by determining the insertion position furthest from the positive ions shows a similar shape to the voltage curve obtained in the experiment.

The second aspect of the present application is,

A battery performance prediction system according to the above prediction method is provided.

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Detailed description of parts overlapping with the first aspect of the present application has been omitted, but the content described in the first aspect of the present application can be applied equally even if the description is omitted in the second aspect.

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The third aspect of the present application is,

A battery performance prediction device including the prediction system is provided.

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Detailed description of parts overlapping with the first and second aspects of the present application has been omitted, but the content described in the first and second aspects of the present application can be applied equally even if the description is omitted in the third aspect. .

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The method according to the above-described embodiment of the present invention may be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. A computer-readable recording medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on a computer-readable recording medium may be specially designed and configured for embodiments of the present invention, or may be known and usable by those skilled in the computer software field. Computer-readable recording media include magnetic recording media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, ROM, RAM, and flash memory. , includes hardware configured to store and execute program instructions. Program instructions include machine language code created by a compiler and high-level language code that can be executed on a computer using an interpreter. The hardware may be configured to operate as one or more software modules for processing the method according to the invention and vice versa.

The method according to an embodiment of the present invention may be executed in an electronic device in the form of program instructions. Electronic devices include portable communication devices such as smartphones and smart pads, computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, and home appliances.

A method according to an embodiment of the present invention may be included and provided in a computer program product. Computer program products are commodities and can be traded between sellers and buyers. Computer program products may be distributed in the form of machine-readable recording media or online through an application store. In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or created temporarily in a storage medium such as the memory of a manufacturer's server, an application store's server, or a relay server.

Each component, such as a module or program, according to an embodiment of the present invention may be composed of a single or a plurality of sub-components, and some of these sub-components may be omitted or other sub-components may be added. may be included. Some components (modules or programs) may be integrated into one entity and perform the same or similar functions performed by each corresponding component before integration. Operations performed by modules, programs, or other components according to embodiments of the present invention are executed sequentially, in parallel, repeatedly, or heuristically, or at least some operations are executed in a different order, omitted, or other operations are added. It can be.

The description of the present invention described above is for illustrative purposes, and those skilled in the art will understand that the present invention can be easily modified into other specific forms without changing the technical idea or essential features of the present invention. will be. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive. For example, each component described as single may be implemented in a distributed manner, and similarly, components described as distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims described below, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be construed as being included in the scope of the present invention.

Claims (15)

As a method for predicting battery performance according to electrode active materials of heterogeneous stacked structure,
A two-dimensional structure that is a transition metal dichalcogenide expressed as graphene or MX2 (where M is selected from the group consisting of transition metals such as Mo, W, Nb, and Ti, and X is S, Se, and Te) providing a database for (one selected from the group consisting of);
Based on the two or more two-dimensional structures selected by the user's input, the vacuum area is detected and the out-of-plane direction is set to the z-axis, and when searching for a super-cell, a hexagon ( It involves converting a hexagonal structure into a rectangular structure, and creates a structure that maximizes the minimum distance between atoms between layers by shifting the in-plane direction of each layer when stacked, as shown in Equation 1 below. Creating a two-dimensional heterogeneous layered structure;
Structurally optimizing the optimal two-dimensional heterogeneous stacked structure selected by user input among the plurality of two-dimensional heterogeneous stacked structures by optimizing the atomic arrangement to minimize repulsion between atoms between the plurality of adjacent heterogeneous stacked structures;
Selecting cation insertion site candidates by analyzing the Voronoi diagram of the structure, calculating the primary cation insertion energy for each position through DFT calculation in Equation 2 below, and cation insertion from the lowest cation insertion energy structure. Searching for a cation insertion site in the structurally optimized two-dimensional heterogeneous stacked structure, including determining the location;
calculating a structure in which the cations are sequentially inserted by calculating a structure in which Equation 3 below is maximum; and
A step of deriving the half-cell voltage and maximum cation insertion capacity from the calculation results,
Structurally optimizing the optimal two-dimensional heterogeneous stacked structure selected by user input among the plurality of two-dimensional heterogeneous stacked structures by optimizing the atomic arrangement to minimize repulsion between atoms between the plurality of adjacent heterogeneous stacked structures. ;Is
Based on constraints on the longest lattice length set by the user and all possible directions searched, atomic arrangement is optimized to minimize repulsion between adjacent atoms, creating a two-dimensional heterogeneous stacked structure from two separate hierarchies. A method for predicting battery performance.
[Equation 1]

[Equation 2]

[Equation 3]

(In Equation 3 above, Lii is the i-th cation insertion site, Lij is the j-th cation insertion site, is the distance between the ith cation insertion site and the jth cation insertion site) The method of claim 1, wherein the cation is lithium ion (Li ⁺ ).
The method of claim 1, wherein the battery is a lithium-ion battery,
A method, characterized in that the electrode is an anode.
The method of claim 1, wherein the two-dimensional structure of the database is provided in CIF or POSCAR support format.
delete delete delete delete delete delete delete delete delete A battery performance prediction system according to the prediction method of claim 1.
A battery performance prediction device comprising the prediction system of claim 14.