A battery energy density benefit prediction method and device, and a storage medium

Abstract

The application relates to the technical field of batteries, and discloses a battery energy density benefit prediction method and device and a storage medium. The battery energy density benefit prediction method comprises the following steps: providing an energy density benefit prediction model under a target silicon-based battery system, wherein the energy density benefit prediction model is used for predicting the energy density benefit corresponding to the silicon content and the discharge cut-off voltage; and performing a target operation according to the energy density prediction model. The application provides the energy density benefit prediction model based on the coupling relationship between the silicon content and the discharge cut-off voltage, for the first time, integrates two key design parameters into the same prediction framework, directly establishes an explicit correlation, provides a quantitative tool for parameter collaborative design of the energy density benefit evaluation of a silicon-carbon negative electrode battery, and has the advantages of being extremely efficient and low in cost, and is suitable for rapid, large-scale parameter scanning and trend analysis at the initial stage of research and development.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method and apparatus for predicting battery energy density gains, and a storage medium. Background Technology

[0002] Graphite and silicon-carbon materials are currently the main anode materials in the battery industry.

[0003] On the one hand, the lithium delithiation potential of graphite anode batteries (vs. Li) + The voltage of Li is extremely low, around 0.1V, while the typical lower limit of full-cell voltage is around 3.0V. Graphite materials have already released most of their capacity above 3.0V; over-discharging to below 2.5V could severely damage the graphite and the cathode.

[0004] On the other hand, the lithium delithiation potential of silicon-carbon composite anode batteries (vs. Li) + The voltage of silicon-carbon composite anode batteries is relatively high (around 0.4V), and the lower limit voltage of the full cell can be as low as 2.0V-2.5V. Above 3.0V, the capacity of silicon-carbon materials is largely unutilized, with its main capacity distributed below 3.0V. If the discharge cutoff voltage is set too high (such as the 3.0V commonly used in pure graphite batteries), the capacity of silicon cannot be released, rendering the addition of silicon meaningless. However, the "low-voltage" design of silicon-carbon composite anode batteries also carries some risks: over-discharge of the positive electrode and structural damage; if the formulation / process is inappropriate, lithium plating may still occur in the graphite portion; and increased silicon volume changes may affect lifespan.

[0005] In other words, the lower limit of the graphite system is the safety red line, while the lower limit of the silicon-carbon system is the trade-off between performance and lifespan / safety.

[0006] Therefore, for silicon-based batteries, how to rationally design the discharge cutoff voltage to balance performance and lifespan / safety is a technical problem that urgently needs to be solved.

[0007] The above information is provided as background information only to aid in understanding this application and does not constitute an assertion or admission that any of the above content can be used as prior art relative to this application. Summary of the Invention

[0008] This application provides a method and apparatus for predicting battery energy density gains, as well as a storage medium, to address the difficulty in balancing battery performance and lifespan / safety.

[0009] To achieve the above objectives, this application provides the following technical solution: In a first aspect, embodiments of this application provide a method for predicting battery energy density gains, including: A predictive model for energy density gain under a target silicon-based battery system is provided. The predictive model is used to predict the corresponding energy density gain based on silicon content and discharge cutoff voltage. Perform the target operation based on the energy density prediction model.

[0010] Optionally, the method for constructing the energy density gain prediction model includes: Obtain an experimental sample dataset, which includes multiple sets of experimental sample data corresponding to different silicon contents. Each set of experimental sample data includes the measured energy density gain corresponding to various discharge cutoff voltages under the current silicon content. Based on the experimental sample dataset, the functional form of the energy density benefit prediction model is first determined, and then the parameters of the energy density benefit prediction model are determined by curve fitting, thus obtaining the energy density benefit prediction model.

[0011] Optionally, the step of first determining the functional form of the energy density gain prediction model based on the experimental sample dataset, and then determining the parameters of the energy density gain prediction model through curve fitting to obtain the energy density gain prediction model includes: Based on the experimental sample dataset, a power function relationship between energy density gain and discharge cutoff voltage was obtained. Where C is the energy density gain, B0 is the baseline value of the discharge cutoff voltage, B is the actual design value of the discharge cutoff voltage, A% is the silicon content, K is the first parameter related to A, and N is the second parameter related to A. For each silicon content, using the power function relationship as the objective, curve fitting is performed on the measured values ​​of energy density gain under multiple corresponding discharge cutoff voltages to obtain the values ​​of the first and second parameters corresponding to the current silicon content; Based on various silicon contents and their corresponding first and second parameter values, a polynomial fitting method is used to obtain the first polynomial relationship between the first parameter and the silicon content. And the second polynomial relationship between the second parameter and the silicon content. ; According to the first polynomial relation The second polynomial relation and the power function relationship The energy density gain prediction model is constructed. .

[0012] Optionally, the value of B0 is 3.0V, and the value of B is in the range of 2.0V to 3.0V.

[0013] Optionally, the first polynomial relation and the second polynomial relation They respectively satisfy the following relations: ; .

[0014] Optionally, the target operation includes: By inputting silicon content and discharge cutoff voltage into the energy density prediction model, the corresponding predicted energy density gain value is obtained. Based on the predicted energy density gains, battery materials are screened and systems are evaluated.

[0015] Optionally, the target operation includes: By inputting the target energy density gain into the energy density prediction model, feasible combinations of silicon content and discharge cutoff voltage that satisfy the target energy density gain are obtained. The cell design window is guided by the feasible combination of silicon content and discharge cutoff voltage.

[0016] Secondly, embodiments of this application provide a battery energy density gain prediction device for implementing the battery energy density gain prediction method described in any of the above claims, including: The model building unit is used to build an energy density gain prediction model for the target silicon-based battery system. The energy density gain prediction model is used to predict the corresponding energy density gain based on the silicon content and the discharge cutoff voltage. An execution unit is used to perform a target operation based on the energy density prediction model.

[0017] Thirdly, embodiments of this application provide a battery energy density benefit prediction device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the battery energy density benefit prediction method described above.

[0018] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions thereon, which are executed by a computer processor to implement the battery energy density gain prediction method described in any of the above claims.

[0019] Compared with the prior art, this application has the following beneficial effects: This application provides an energy density benefit prediction model based on the coupling relationship between silicon content and discharge cutoff voltage. For the first time, it integrates two key design parameters into the same prediction framework and directly establishes an explicit correlation. This provides a quantitative tool for parameter co-design in the energy density benefit assessment of silicon-carbon anode batteries. It has advantages such as high efficiency and low cost, and is suitable for rapid and large-scale parameter scanning and trend analysis in the early stages of R&D.

[0020] This application has other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of this application. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the battery energy density benefit prediction method provided in the embodiments of this application; Figure 2 This is a comparison chart of energy density gains under different silicon contents provided in the embodiments of this application; Figure 3 This is a graph showing the change in energy density gain under different cutoff voltages provided in the embodiments of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] The delithiation potential of silicon-based anode materials is 0.4V, meaning that silicon only begins to delithigate in large quantities when the anode potential reaches 0.4V. In contrast, the delithiation potential of graphite anode materials is only 0.1V. This difference results in the following: during full-cell discharge, when the full-cell voltage drops to 3.0V, the capacity of silicon in the silicon-carbon anode is basically unutilized, as its main capacity is distributed below 3.0V; the capacity of silicon is only released when the full-cell voltage drops below 3.0V.

[0025] Therefore, if the discharge cutoff voltage is set too high (such as the 3.0V commonly used in pure graphite batteries), the capacity of silicon will be essentially not released, the energy density improvement will be negligible, and adding silicon will be pointless. Conversely, if the discharge cutoff voltage is set too low, although the capacity of silicon can be fully released, the following risks will be introduced: Over-discharge of the positive electrode: Low voltage causes excessive delithiation of the positive electrode material, leading to the collapse of the layered structure and causing irreversible damage; Lithium plating risk: If the formulation or process is improper, lithium plating may still occur in the graphite part, which may cause internal short circuits or even thermal runaway. Lifetime degradation: Silicon materials undergo accelerated volume changes during deep delithiation, resulting in a significant decrease in cycle life.

[0026] Meanwhile, higher silicon content leads to a higher upper limit for battery energy density, but also results in greater cycle expansion, more severe high-temperature side reactions, and a higher risk of battery degradation and breakage. Conversely, lower silicon content leads to better battery cycle stability and higher safety, but the increase in energy density is limited, and the technical and economic viability of adding silicon decreases.

[0027] As a result, the inventors discovered that the design of silicon-carbon anode batteries is essentially a trade-off between energy density gains and cycle life and safety risks. How to accurately quantify the coupling relationship between silicon content, discharge cutoff voltage and energy density gains has become a key issue that urgently needs to be solved in the early stages of silicon-carbon battery research and development.

[0028] For this purpose, please refer to Figure 1 This application provides a method for predicting battery energy density gains, including: S1. Provide an energy density gain prediction model for the target silicon-based battery system. This energy density gain prediction model is used to predict the corresponding energy density gain based on the silicon content and discharge cutoff voltage.

[0029] S2. Execute the target operation based on the energy density prediction model.

[0030] It should be noted that the target silicon-based battery system refers to a battery system in which the anode material includes silicon-carbon materials. It is understood that the parameters of the energy density gain prediction model will differ for different types of silicon-based battery systems (e.g., different cathode materials, different types of silicon-carbon materials, different testing conditions, etc.). This is because the model parameters are obtained based on experimental data under a specific system and reflect the statistical regularities under that system. If the battery system is changed, the model parameters need to be re-determined based on the experimental data of that system. However, the model construction method provided later in this embodiment is universal.

[0031] Silicon content refers to the percentage by mass of silicon material in the negative electrode active material; energy density gain refers to the percentage increase in energy density relative to the reference discharge cutoff voltage (e.g., 3.0V).

[0032] Target operations refer to various application operations based on energy density prediction models, such as parameter prediction, parameter inverse calculation, or parameter optimization, without any specific restrictions.

[0033] This application provides an energy density benefit prediction model based on the coupling relationship between silicon content and discharge cutoff voltage. For the first time, it integrates two key design parameters into the same prediction framework and directly establishes an explicit correlation. This provides a quantitative tool for parameter co-design in the evaluation of energy density benefits of silicon-carbon anode batteries. It has the advantages of being extremely efficient and low-cost, and is suitable for rapid and large-scale parameter scanning and trend analysis in the early stages of R&D.

[0034] In one alternative implementation, the method for constructing the energy density gain prediction model includes: Obtain the experimental sample dataset, which includes multiple sets of experimental sample data corresponding to different silicon contents. Each set of experimental sample data includes the measured energy density gain corresponding to various discharge cutoff voltages under the current silicon content. Based on the experimental sample dataset, the functional form of the energy density benefit prediction model is first determined, and then the parameters of the energy density benefit prediction model are determined through curve fitting, thus obtaining the energy density benefit prediction model.

[0035] The predictive model construction method in this embodiment is mainly based on data induction, fitting empirical formulas from experimental sample data to reflect statistical regularities rather than strict physical causality. Unlike methods that rely on physical material experiments, the construction method in this embodiment does not require individual sample preparation and testing for each parameter combination; unlike predictive methods that rely on solving complex electrochemical equations or training with massive amounts of data, the construction method in this embodiment does not require inputting difficult-to-obtain material property parameters, nor does it rely on large-scale training datasets.

[0036] Specifically, the method of verifying parameters by using physical material feeding experiments requires the actual production and testing of batteries, inputting a series of physical design parameters and actual operating conditions, which is time-consuming and costly. The prediction method based on physical equations or data-driven methods requires inputting various material property parameters, geometric and boundary conditions, or relying on a large amount of training data. Although the inference stage can be faster, the overall response is affected by the model size and data quality.

[0037] In contrast, the model constructed in this embodiment only requires input of silicon content and discharge cutoff voltage to directly obtain the predicted energy density gain within the allowable error range. This model is built based on the statistical laws of actual data, and has high accuracy within its applicable range, making it suitable for rapid estimation and parameter screening in the early stages of R&D. While the physical material feeding test method has the highest measured accuracy, its non-continuous prediction capability is weak, making it suitable for key project verification and standard test establishment. Prediction methods based on physical equations or data-driven methods can achieve better accuracy when the parameters are accurate or the data is sufficient and well-distributed, but there are difficulties in obtaining parameters or risks of fitting and generalization, making them suitable for mechanism research or virtual experimental scenarios.

[0038] Furthermore, based on the experimental sample dataset, the functional form of the energy density gain prediction model is first determined, and then the parameters of the energy density gain prediction model are determined through curve fitting, resulting in the energy density gain prediction model, including: S11. Based on the experimental sample dataset, obtain the power function relationship between energy density gain and discharge cutoff voltage. Where C is the energy density gain, B0 is the baseline value of the discharge cutoff voltage, B is the actual design value of the discharge cutoff voltage, A is the silicon content, K is the first parameter related to A, and N is the second parameter related to A.

[0039] In this step, the inventors repeatedly studied experimental sample data and discovered the physical characteristic that "the greater the voltage drop, the higher the benefit, but the rate of increase of benefit gradually slows down." They concluded that the energy density benefit C and the voltage drop (B0-B) follow a power function distribution law, thereby determining the functional form of the model and providing an accurate functional basis for subsequent parameter fitting.

[0040] S12. For each silicon content, using a power function relationship as the objective, curve fitting is performed on the measured energy density gain values ​​at multiple corresponding discharge cutoff voltages to obtain the values ​​of the first and second parameters corresponding to the current silicon content.

[0041] S13. Based on various silicon contents and their corresponding first and second parameter values, a polynomial fitting is used to obtain the first polynomial relationship between the first parameter and the silicon content. And the second polynomial relationship between the second parameter and the silicon content. .

[0042] S14. According to the first polynomial relation Second polynomial relation Power function relationship An energy density benefit prediction model was constructed. .

[0043] This embodiment adopts a hierarchical fitting construction method, which first determines the function form and then fits the parameters. This significantly reduces the experimental sample size required for modeling, while ensuring the predictive stability and accuracy of the model within its application range.

[0044] For example, B0 is 3.0V, and the value of B ranges from 2.0V to 3.0V.

[0045] It should be explained that the voltage value of 3.0V is the typical discharge cutoff voltage of a pure graphite anode battery. Calculating the energy density gain based on 3.0V facilitates horizontal comparison with mature technologies and intuitively demonstrates the gain effect of low-voltage design for silicon-carbon anodes. The value range of B, "2.0V~3.0V", covers the complete design range from the safety boundary point (3.0V) to the extreme low voltage (2.0V), encompassing both conventional application scenarios and more aggressive design scenarios with higher risks, providing researchers with a complete and sufficient trade-off space.

[0046] In summary, using 3.0V as the benchmark provides a clear reference for the model output results; the voltage range of 2.0V to 3.0V ensures the reliability of predictions within the model boundaries while avoiding the accuracy risks associated with extrapolating to the over-discharge region.

[0047] In one alternative implementation, based on an energy density prediction model, at least one of the following target operations can be performed: (1) Input silicon content and discharge cutoff voltage into the energy density prediction model to obtain the corresponding energy density benefit prediction value; based on the energy density benefit prediction value, perform battery material screening and system evaluation.

[0048] This operation falls under the category of positive prediction applications. It is particularly suitable for the early stages of R&D when faced with multiple silicon-carbon material candidates and various voltage design options. Instead of building batteries for each one for actual testing, the model can quickly screen out combinations with higher energy density gains, narrowing the scope of experimental verification, significantly reducing R&D costs, and improving R&D efficiency.

[0049] (2) Input the target energy density gain into the energy density prediction model to obtain a feasible combination of silicon content and discharge cutoff voltage that satisfies the target energy density gain; guide the cell design window based on the feasible combination of silicon content and discharge cutoff voltage.

[0050] This operation is an application of reverse engineering. Researchers input their desired energy density gain into the model, and through reverse engineering, they can obtain a set of all (A, B) combinations that satisfy that goal. This set visually represents the various design possibilities for achieving the target gain. Researchers can use this as an initial window for cell design, and then, combined with practical engineering constraints such as material costs and process feasibility, select the design scheme most suitable for the current conditions.

[0051] To facilitate understanding, an application example is provided below. In this example, the specific parameters of the target silicon-based battery system are as follows: (1) Positive and negative electrode material system: Cathode material: Lithium cobalt oxide; Anode material: It is composed of a mixture of silicon-carbon material and graphite material. The silicon-carbon material adopts silicon particle nano-sizing and porous carbon skeleton support technology. The silicon content in the silicon-carbon material is 45%~48%. The mass percentage of silicon in the anode active material (i.e. silicon content) is denoted as A, and the value ranges from 10% to 50%.

[0052] (2) Battery voltage range Maximum voltage of full battery: 4.55V; The full battery discharge cutoff voltage (actual design value) is denoted as B, and its range is 2.0V~3.0V; The reference discharge cutoff voltage B0 is set to 3.0V and used as a reference for calculating energy density gains.

[0053] (3) Test conditions and process parameters Discharge rate: 0.2C; Test temperature: 25℃; Negative electrode coating methods include single-layer coating and double-layer coating. In double-layer coating, the coating thickness ratio of the upper layer to the lower layer is greater than 2:8. Optional coating schemes include both upper and lower layers being silicon-carbon materials, the upper layer being pure graphite material and the lower layer being silicon-carbon material, or the upper layer being silicon-carbon material and the lower layer being pure graphite material. Battery structure: stacked structure or wound structure.

[0054] Studies using the aforementioned battery system have revealed that the relationship between silicon-carbon content (A), discharge cutoff voltage (B), and energy density gain (C) conforms to the following formula: ; Wherein, K and N are parameters related to silicon content A, and further research revealed that they respectively conform to the following polynomial relationships: 10%≤A%≤50%; 10%≤A%≤50%; Substituting the above relationships, we can obtain the complete energy density gain prediction model: .

[0055] Example 1: Silicon particle nanofiber and porous carbon framework support technology result in a silicon content of 47% in silicon-carbon materials. The graphite DV100 has a density of 28.7~28.8 μm, a tap density of 1.0 g / cm³, and a BET of 1.232 m² / g. Silicon-carbon DV99 has a thickness of 15.1µm to 15.7µm and a tap density of 0.96 g / cm³. 3 ~0.97g / cm 3 BET=1.6m2 / g; The lower limit voltage is 2.8V and 2.6V; The silicon-carbon content is 10%, 15%, 18%, 20%, 30%, and 40%. Table 1 Figure 2 Table 1 shows the actual values ​​for this example, and the errors between the model predictions and actual values. This demonstrates that the model provided in this embodiment has high reliability: the model error is controlled within 0.5% in most regions (especially low to medium silicon content or higher voltage), and the overall average error is acceptable, making it suitable for preliminary prediction. For a design with A%=40% and B=2.6V, a systematic underestimation of approximately 1.7% is known during application.

[0056] Example 2: Silicon particle nanofiber and porous carbon framework support technology result in a silicon content of 47% in silicon-carbon materials. The graphite DV100 has a thickness of 28.7~28.8 μm and a tap density of 1.0 g / cm³. 3 BET=1.232m 2 / g; The silicon-carbon DV99 has a thickness of 15.1~15.7 μm and a tap density of 0.96 g / cm³. 3 ~0.97g / cm 3 BET=1.6m 2 / g; The lower limit voltages are 2.9V, 2.8V, 2.7V, 2.6V, 2.5V, 2.4V, 2.3V, 2.2V, 2.1V, and 2.0V. The silicon-carbon content is 12% and 20%.

[0057] Table 2 Figure 3 Table 2 shows the actual value data for this example, and the error between the model prediction and the actual value for this example. From this, we can conclude that... (1) For a silicon-carbon content of A%=12%, the model makes more accurate predictions when B≥2.4V. Although it slightly overestimates at lower voltages, the error is controllable and can be used for reliable trend analysis and preliminary design.

[0058] Overall accuracy: The model has a prediction error of less than 0.3% at most voltage points (especially 2.4V to 2.9V), demonstrating extremely high accuracy.

[0059] Error trend: As the voltage decreases (B decreases), the prediction error gradually increases at B=2.3V and below, reaching its maximum (+0.97%) at B=2.0V. This indicates that the model has a slight systematic underestimation or overestimation of the gains of this silicon content at extremely low voltages.

[0060] Practical application guidance: The absolute value of all prediction errors does not exceed 1%. In the early evaluation of engineering and R&D, this level of error is usually perfectly acceptable.

[0061] (2) For a silicon-carbon content of A%=20%, the ED gain shows an overall trend of increasing with decreasing voltage. It has excellent prediction accuracy in the low voltage range, while the deviation in the medium and high voltage range reflects the actual physical challenges faced by high silicon anodes during deep discharge.

[0062] Accuracy distribution: The formula systematically underestimates the voltage range of 2.2V to 2.8V, with a maximum error of -0.92% (2.6V).

[0063] The root cause: The core reason for this phenomenon is related to the characteristics of the silicon-carbon anode. For a relatively high silicon content of 20%, during deep discharge (low voltage), the actual capacity (ED gain) of the battery may be lower than theoretically expected due to the huge volume expansion of silicon particles, hindered lithium-ion diffusion, or intensified polarization. The formula fitted in this application is essentially a smooth analytical function that captures the overall upward trend, but it is difficult to fully reproduce the "growth saturation" or "capacity decay" effect caused by physical limitations in the low-voltage region.

[0064] Application Recommendation: For assessments involving deep discharge (B≤2.6V) and high silicon content, in practical applications, the predicted values ​​for this region can be appropriately corrected based on experience (e.g., multiplied by a correction factor), or it can be clearly indicated that there is a systematic overestimation.

[0065] Secondly, embodiments of this application provide a battery energy density gain prediction device for implementing the battery energy density gain prediction method as described in the above embodiments, including: The model building unit is used to build an energy density gain prediction model for the target silicon-based battery system. The energy density gain prediction model is used to predict the corresponding energy density gain based on the silicon content and discharge cutoff voltage. The execution unit is used to perform the target operation based on the energy density prediction model.

[0066] The above-described apparatus can execute the methods provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the methods, which will not be described in detail here.

[0067] Thirdly, embodiments of this application provide a battery energy density benefit prediction device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described battery energy density benefit prediction method.

[0068] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the battery energy density gain prediction method as provided in all embodiments of this application.

[0069] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0070] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0071] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0072] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0073] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not limit the scope of patent protection of this application. Any technical solutions that are based on the essential concept of this application and utilize the content described in the text and drawings of this application, resulting in equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this application.

Claims

1. A method for predicting battery energy density gains, characterized in that, include: A predictive model for energy density gain under a target silicon-based battery system is provided. The predictive model is used to predict the corresponding energy density gain based on silicon content and discharge cutoff voltage. Perform the target operation based on the energy density prediction model.

2. The battery energy density gain prediction method according to claim 1, characterized in that, The method for constructing the energy density gain prediction model includes: Obtain an experimental sample dataset, which includes multiple sets of experimental sample data corresponding to different silicon contents. Each set of experimental sample data includes the measured energy density gain corresponding to various discharge cutoff voltages under the current silicon content. Based on the experimental sample dataset, the functional form of the energy density benefit prediction model is first determined, and then the parameters of the energy density benefit prediction model are determined by curve fitting, thus obtaining the energy density benefit prediction model.

3. The battery energy density gain prediction method according to claim 2, characterized in that, The process of first determining the functional form of the energy density gain prediction model based on the experimental sample dataset, and then determining the parameters of the energy density gain prediction model through curve fitting, to obtain the energy density gain prediction model, includes: Based on the experimental sample dataset, a power function relationship between energy density gain and discharge cutoff voltage was obtained. Where C is the energy density gain, B0 is the baseline value of the discharge cutoff voltage, B is the actual design value of the discharge cutoff voltage, A% is the silicon content, K is the first parameter related to A, and N is the second parameter related to A. For each silicon content, using the power function relationship as the objective, curve fitting is performed on the measured values ​​of energy density gain under multiple corresponding discharge cutoff voltages to obtain the values ​​of the first and second parameters corresponding to the current silicon content; Based on various silicon contents and their corresponding first and second parameter values, a polynomial fitting method is used to obtain the first polynomial relationship between the first parameter and the silicon content. And the second polynomial relationship between the second parameter and the silicon content. ; According to the first polynomial relation The second polynomial relation and the power function relationship The energy density gain prediction model is constructed. .

4. The battery energy density gain prediction method according to claim 3, characterized in that, The value of B0 is 3.0V, and the value of B ranges from 2.0V to 3.0V.

5. The battery energy density gain prediction method according to claim 3, characterized in that, The first polynomial relation and the second polynomial relation They respectively satisfy the following relations: ； 。 6. The battery energy density gain prediction method according to claim 1, characterized in that, The target operation includes: By inputting silicon content and discharge cutoff voltage into the energy density prediction model, the corresponding predicted energy density gain value is obtained. Based on the predicted energy density gains, battery materials are screened and systems are evaluated.

7. The battery energy density gain prediction method according to claim 1, characterized in that, The target operation includes: By inputting the target energy density gain into the energy density prediction model, feasible combinations of silicon content and discharge cutoff voltage that satisfy the target energy density gain are obtained. The cell design window is guided by the feasible combination of silicon content and discharge cutoff voltage.

8. A battery energy density gain prediction device, characterized in that, A method for predicting battery energy density gains as described in any one of claims 1-7 includes: The model building unit is used to build an energy density gain prediction model for the target silicon-based battery system. The energy density gain prediction model is used to predict the corresponding energy density gain based on the silicon content and the discharge cutoff voltage. An execution unit is used to perform a target operation based on the energy density prediction model.

9. A battery energy density gain prediction device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the battery energy density gain prediction method as described in any one of claims 1-7.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that, The computer-executable instructions are executed by a computer processor to implement the battery energy density gain prediction method as described in any one of claims 1-7.

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