Method for optimizing compaction density of graphite negative electrode based on relaxation time distribution analysis

Abstract

The present application relates to the technical field of lithium ion battery, and discloses a method for optimizing compaction density of graphite negative electrode based on relaxation time distribution analysis, which comprises the following steps: preparing dry-state graphite negative electrode base sheets with different gradient compaction densities and assembling them into button-type simulation batteries respectively; performing alternating current impedance spectrum test on the button-type simulation batteries to obtain frequency domain impedance data; using Tikhonov regularization algorithm to deconvolute the frequency domain impedance data and convert it into a relaxation time distribution spectrum diagram with time constant as the abscissa; locating the characteristic peak of a specific time constant interval in the spectrum diagram and performing integration, and extracting the absolute values of charge transfer impedance and diffusion impedance respectively; comparing the above two impedances under different compaction densities, and determining the compaction density corresponding to the common minimum value of the two as the optimal compaction density. The present application overcomes the problem of alternating current impedance polarization semicircle overlap, and quantitatively determines the compaction state in which electronic conduction and ion transport reach balance.

Description

Technical Field

[0001] This invention relates to the field of lithium-ion battery technology, specifically to a method for optimizing the compaction density of graphite anodes based on relaxation time distribution analysis. Background Technology

[0002] In the manufacturing process of lithium-ion battery electrodes, the compaction density of the graphite anode determines the internal pore structure and particle contact state. A low compaction density leads to loose particle contact and an incomplete electron conduction path; while an excessively high compaction density reduces porosity, hinders electrolyte wetting, increases the diffusion resistance of lithium ions within the electrode, and may even cause graphite particle breakage. Therefore, finding the optimal compaction density that balances electron conduction and ion transport is a crucial step in electrode process optimization.

[0003] Currently, methods for evaluating and optimizing compaction density mainly rely on destructive physical characterization or long-term battery charge-discharge cycle testing. Destructive physical characterization primarily involves cross-sectional scanning electron microscopy (SEM) observation or mercury intrusion porosimetry (MIP) testing of electrode porosity. These methods only provide static morphology and structural data and cannot directly reflect the electrochemical kinetic changes of the electrode under operating conditions. While battery assembly and charge-discharge cycle testing can reflect actual electrochemical performance, the long testing cycle severely restricts the efficiency of process iteration. To shorten the evaluation cycle, AC impedance spectroscopy (AIS) testing is used to evaluate electrode state, observing the polarization of the electrode by analyzing frequency domain impedance data. However, in conventional frequency domain impedance spectra, the time constants of electrochemical kinetic processes such as charge transfer and solid-liquid diffusion within the electrode are close, causing the corresponding polarization semicircles to overlap. This overlap makes it impossible for testers to separate and quantitatively extract the absolute impedance value of a single polarization process, making it difficult to determine whether the electron network and ion channels within the electrode have reached equilibrium. Existing testing methods cannot quickly and quantitatively determine the optimal compaction density of graphite anodes in a non-destructive manner. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for optimizing the compaction density of graphite anodes based on relaxation time distribution analysis. This method solves the problems in existing technologies where the evaluation of graphite anode compaction density typically relies on destructive physical characterization or time-consuming battery charge-discharge cycle testing, and it is difficult to distinguish the polarization evolution of different kinetic processes within the electrode. This results in long compaction density optimization cycles and difficulty in determining the optimal compaction state that balances electronic and ion conduction.

[0005] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a method for optimizing the compaction density of graphite anodes based on relaxation time distribution analysis, comprising the following steps: Several groups of dry graphite anode substrates with different gradient compaction densities were prepared. Each group of electrode sheets is assembled into a button cell simulated battery; AC impedance spectroscopy was performed on each group of coin cell analog batteries. A preset AC voltage amplitude was applied in the frequency range of 100kHz to 10mHz to obtain frequency domain impedance data. The frequency domain impedance data is deconvolved using the Tikhonov regularization algorithm to convert the frequency domain impedance data into a relaxation time distribution spectrum with the time constant as the horizontal axis. The location time constant in the distribution spectrum is 10 -1 The area integral is performed on the characteristic peaks in the s to 1s interval to extract the absolute value of the corresponding charge transfer impedance. In the distribution spectrum, the area integration of the characteristic peaks with time constants in the range of 1s to 10s is performed to extract the corresponding absolute value of diffusion impedance. By comparing the absolute values ​​of the charge transfer impedance and the diffusion impedance under different gradient compaction density conditions, the compaction density corresponding to the lowest value of both within the gradient range is determined as the optimal compaction density.

[0006] By adopting the above technical solution, this invention establishes a method for analyzing the compaction state of electrode sheets based on electrochemical testing. The compaction process of the electrode sheet changes the internal pore structure and particle contact state. When the compaction density is too low, the contact between particles is loose, and the electron conduction path is imperfect; when the compaction density is too high, the porosity decreases sharply, electrolyte wetting is hindered, and the tortuosity of the ion transport path increases. Conventional frequency domain impedance spectra usually show multiple overlapping semicircles, making it impossible to separate electrochemical polarization processes with similar time constants. This invention introduces a relaxation time distribution algorithm to convert the measured broadband AC impedance into an integral expression of ohmic internal resistance and relaxation time distribution function. By deconvolution calculation, the distribution spectrum is obtained, decoupling the overlapping electrochemical polarization processes into independent characteristic peaks in different time constant intervals. This invention explicitly uses 10 -1 The response in the s to 1s interval is attributed to the charge transfer process, and its integral area represents the charge transfer impedance. The response in the 1s to 10s interval is attributed to the solid-liquid phase diffusion process, and its integral area represents the diffusion impedance. As the compaction density increases, the spacing between graphite particles decreases, the conductive network becomes denser, and the charge transfer impedance decreases accordingly. However, when the pores are excessively closed, the diffusion resistance of lithium ions inside the porous electrode increases, leading to an increase in the diffusion impedance. This scheme uses the minimum values ​​of both charge transfer impedance and diffusion impedance within the stated gradient range as the criterion. The minimum value corresponds to a balance between the electron conduction network and ion transport channels inside the electrode, thus determining the optimal compaction density.

[0007] Preferably, before performing deconvolution calculation using the Tikhonov regularization algorithm, the frequency domain impedance data is first subjected to a Kramers-Kronig relationship test to remove noisy data points that do not satisfy the causal relationship; the impedance model established by the Tikhonov regularization algorithm is that the AC impedance is the sum of the ohmic internal resistance and the polarization impedance, and the value of the regularization parameter of the Tikhonov regularization algorithm is determined by the generalized cross-validation method.

[0008] By employing the above technical solutions, the Kramers-Kronig relationship test can verify the linearity, stability, and causality of the test system during the measurement process, filtering out non-steady-state noise signals caused by system drift or external interference. Combining this with the generalized cross-validation method for automatic optimization and calculation of regularization parameters avoids underfitting or overfitting of the spectrum due to manual value selection, ensuring the objectivity of the impedance model calculation results and obtaining reliable characteristic peak spectra.

[0009] Preferably, the step of extracting the absolute impedance value from the distribution spectrum further includes: a positioning time constant of 10. -3 s to 10 -1 The characteristic peaks in the s-interval are integrated by area to extract the corresponding absolute value of the SEI film impedance. When the increase in compaction density leads to an increase in the extracted absolute value of the diffusion impedance and a shift in the peak position towards a larger time constant, and at the same time the absolute value of the SEI film impedance increases, it is determined that the electrode with the corresponding compaction density is in an over-compacted state.

[0010] By employing the above technical solution, a method for determining the over-compaction state of electrode sheets is provided. When over-compaction leads to graphite particle breakage, new surfaces are exposed. These new surfaces undergo side reactions in the electrolyte system, generating additional microstructure layers, manifested as an increase in the absolute value of the SEI film impedance. Simultaneously, particle breakage is accompanied by pore collapse, increasing the transit time of ions in the porous matrix, resulting in increased diffusion impedance and a shift of its characteristic peak towards a larger time constant. This response mechanism allows for the identification and elimination of rolling parameters that cause structural damage.

[0011] Preferably, before performing AC impedance spectroscopy testing on each group of coin cell simulated batteries, each group of coin cell simulated batteries is placed in a constant temperature environment at 25°C for 12 hours.

[0012] By adopting the above technical solution, the static operation allows the injected electrolyte to fully penetrate into the micropores of the graphite negative electrode, eliminating early test impedance fluctuations caused by uneven mass transfer in the liquid phase. Constant temperature control eliminates the variable influence of temperature gradients on charge transfer activation energy and electrolyte viscosity, establishing a stable testing environment and ensuring benchmark consistency among multiple groups of different electrodes in comparative tests.

[0013] Preferably, the specific implementation method for preparing several groups of dry graphite negative electrode substrates with different gradient compaction densities is as follows: after mixing graphite active material, conductive carbon black as a conductive agent and polyvinylidene fluoride powder as a binder, the mixture is added to a solvent for mechanical dispersion to obtain a uniform negative electrode slurry; the negative electrode slurry is uniformly coated on one side of a current collector to obtain a coated wet film; the coated wet film is dried at a constant temperature to evaporate the solvent, resulting in an unrolled electrode sheet; the unrolled electrode sheet is subjected to gradient rolling using a roller mill to prepare the dry graphite negative electrode substrates with different gradient compaction densities.

[0014] By adopting the above technical solutions, the preparation process of electrode samples was standardized. Mechanical dispersion ensures the uniform spatial distribution of binder, conductive carbon black, and graphite, avoiding local impedance anomalies caused by material agglomeration. Gradient rolling produces electrodes with different compaction densities, providing a basis for obtaining electrochemical data.

[0015] Preferably, the solid components in the negative electrode slurry are composed of the following raw materials in parts by weight: 90 to 92 parts of the graphite active material, 3 to 4 parts of the conductive carbon black, and 5 to 6 parts of the polyvinylidene fluoride powder.

[0016] By adopting the above technical solution, the formulation composition of the negative electrode slurry was clarified. An appropriate amount of conductive carbon black fills the spaces between graphite particles to establish an electron transport network; polyvinylidene fluoride powder provides the electrode structure layer with sufficient cohesive strength and interfacial adhesion to withstand the mechanical stress during the rolling stage and prevent the active material from falling off and delaminating.

[0017] Preferably, the graphitic active material is non-porous graphite or porous graphite; the BET specific surface area of ​​the non-porous graphite is less than or equal to 5 m². 2 / g; the BET specific surface area of ​​the porous graphite is greater than 5m². 2 / g, and possesses a type IV adsorption isotherm and a type H3 hysteresis loop.

[0018] The above-mentioned technical solution is applicable to graphite materials with different morphologies. Type IV adsorption isotherms and H3 hysteresis loops define typical mesoporous morphologies and slit channel characteristics within porous materials. Pore morphology and specific surface area determine the deformation threshold of particles under pressure and their liquid retention capacity, covering different pore characteristic indicators and ensuring the analytical method has a wide range of applicability.

[0019] Preferably, the process parameters for preparing the dry graphite anode substrate are controlled as follows: the solvent is N-methylpyrrolidone, the current collector is pure copper foil with a thickness in the range of 8μm to 10μm, the thickness of the wet film during the coating stage is controlled between 100μm and 200μm, and the temperature for constant temperature drying is controlled at 110℃.

[0020] By adopting the above technical solution, the process parameters for the preparation process were clarified. The pure copper foil combines mechanical toughness with low electrical resistance, and its limited thickness maintains low deformation under gradient rolling pressure, preventing current collector extension and coating cracking. The drying temperature setting ensures solvent evaporation while avoiding overheating that could lead to polyvinylidene fluoride degradation.

[0021] Preferably, the specific implementation of assembling each group of electrode sheets into a button cell analog battery is as follows: in a glove box filled with high-purity argon gas, the dry graphite negative electrode substrate electrode sheet is used as the working electrode, a lithium metal sheet is used as the counter electrode, a polypropylene microporous membrane is used as the separator, and a lithium hexafluorophosphate electrolyte containing ethylene carbonate and diethyl carbonate is injected for assembly.

[0022] A half-cell detection system was constructed using the above technical solution. An inert, high-purity argon atmosphere isolates the lithium metal and electrolyte from side reactions caused by water and oxygen. The lithium metal sheet provides an ample lithium source and a stable, constant potential reference point, enabling the polarization signal obtained in impedance testing to reflect changes on the graphite electrode side, thus improving analytical accuracy.

[0023] Preferably, after determining the optimal compaction density, the method further includes assembling a battery using the electrode with the optimal compaction density, performing a constant current charge-discharge test at a 0.2C rate, and recording the discharge specific capacity and capacity distribution deviation of samples from the same batch.

[0024] By adopting the above technical solution, a verification step was established between impedance analysis and actual cell performance. The constant current charge-discharge process directly verifies the lithium insertion / extraction kinetics of the graphite lattice under optimal compaction conditions. Capacity distribution deviation, as a quantitative indicator of process repeatability, reflects the stability of this compaction density parameter in production applications.

[0025] This invention provides a method for optimizing the compaction density of graphite anodes based on relaxation time distribution analysis. It has the following beneficial effects: 1. This invention uses the Tikhonov regularization algorithm to deconvolve frequency domain impedance data to obtain a relaxation time distribution spectrum with the time constant as the abscissa; it overcomes the problem of easy overlap of polarization semicircles in conventional AC impedance testing, and separates charge transfer processes and diffusion processes with similar time constants into independent characteristic peaks, thereby enabling the extraction of the absolute impedance value of each dynamic process separately.

[0026] 2. This invention compares the charge transfer impedance and diffusion impedance under different compaction densities and determines the compaction density corresponding to the common minimum value of the two as the optimal compaction density. Since the increase in compaction density will lead to a decrease in charge transfer impedance and an increase in diffusion impedance at the same time, finding the common minimum value of the two can quantitatively determine the physical state in which electron conduction and ion transport reach equilibrium, replacing time-consuming charge-discharge tests and destructive physical characterization.

[0027] 3. This invention determines whether the electrode is over-compacted by extracting the absolute value of the SEI film impedance in a specific frequency band and combining it with the value of the diffusion impedance and the peak position shift. When the electrode experiences particle breakage and pore collapse due to over-compactment, the newly exposed surface side reactions will increase the SEI film impedance and increase the ion diffusion time. Based on the changes in the above impedance characteristics, it is possible to identify and avoid compaction parameters that cause damage to the electrode structure.

[0028] Figure 1 The EIS Nyquist plot of graphite sample A of the present invention; Figure 2 The EIS Nyquist plot of graphite sample B of the present invention; Figure 3 This is a DRT analysis curve of graphite sample A of the present invention; Figure 4 This is a DRT analysis curve of graphite sample B of the present invention. Figure 5 SEM images of the electrode sheets under different compaction densities according to the present invention; Figure 6 The following are nitrogen adsorption and desorption isotherms for samples A and B of this invention. Detailed Implementation

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Preparation Examples 1-4: Preparation Example 1: This preparation example provides a basic negative electrode substrate based on intermediate sizing ratio and non-porous graphite, including the following steps: Step 1: Accurately weigh 91.5 parts by weight of commercially available graphite A (BET specific surface area ≤ 5m²) without porosity. 2 3.5 parts of Super-P conductive carbon black as a conductive agent, and 5 parts of polyvinylidene fluoride (PVDF) powder as a binder.

[0031] Step 2: After mixing the above solid dry powder, add an appropriate amount of N-methylpyrrolidone (NMP) solvent and mechanically disperse it at room temperature using a planetary vacuum mixer to obtain a uniform negative electrode slurry.

[0032] Step 3: Use a scraper to uniformly coat the above negative electrode slurry onto one side of a pure copper foil current collector with a thickness of 8~10μm to obtain a coated wet film.

[0033] Step 4: Transfer the coated electrode to a vacuum drying oven and dry it at a constant temperature of 110°C to evaporate the NMP solvent, so as to obtain a dry basic negative electrode substrate electrode that has not been rolled, for later use.

[0034] Preparation Example 2: This preparation example provides a basic negative electrode substrate based on intermediate sizing ratio and porous graphite, including the following steps: Step 1: Accurately weigh 91.5 parts by weight of commercially available graphite B with porous characteristics (BET specific surface area > 5m³). 2 / g, containing a type IV adsorption isotherm and an H3 hysteresis loop), 3.5 parts of Super-P conductive carbon black as a conductive agent, and 5 parts of polyvinylidene fluoride (PVDF) powder as a binder.

[0035] Step 2: After mixing the above solid dry powder, add an appropriate amount of N-methylpyrrolidone (NMP) solvent and mechanically disperse it at room temperature using a planetary vacuum mixer to obtain a uniform negative electrode slurry.

[0036] Step 3: Use a scraper to uniformly coat the above negative electrode slurry onto one side of a pure copper foil current collector with a thickness of 8~10μm to obtain a coated wet film.

[0037] Step 4: Transfer the coated electrode to a vacuum drying oven and dry it at a constant temperature of 110°C to evaporate the NMP solvent, so as to obtain a dry basic negative electrode substrate electrode that has not been rolled, for later use.

[0038] Preparation Example 3: This preparation example provides a basic negative electrode substrate based on the endpoint ratio of highly active materials, including the following steps: Step 1: Accurately weigh 92 parts by weight of commercial graphite A (BET specific surface area ≤ 5m²) without porosity. 2 / g), 3 parts of Super-P conductive carbon black as a conductive agent, and 5 parts of polyvinylidene fluoride (PVDF) powder as a binder.

[0039] Step 2: After mixing the above solid dry powder, add an appropriate amount of N-methylpyrrolidone (NMP) solvent and mechanically disperse it at room temperature using a planetary vacuum mixer to obtain a uniform negative electrode slurry.

[0040] Step 3: Use a scraper to uniformly coat the above negative electrode slurry onto one side of a pure copper foil current collector with a thickness of 8~10μm to obtain a coated wet film.

[0041] Step 4: Transfer the coated electrode to a vacuum drying oven and dry it at a constant temperature of 110°C to evaporate the NMP solvent, so as to obtain a dry basic negative electrode substrate electrode that has not been rolled, for later use.

[0042] Preparation Example 4: This preparation example provides a basic negative electrode substrate based on a low-activity material / high-excipient end-point ratio, including the following steps: Step 1: Accurately weigh 90 parts by weight of commercially available graphite A (BET specific surface area ≤ 5m²) without porosity. 2 / g), 4 parts of Super-P conductive carbon black as a conductive agent, and 6 parts of polyvinylidene fluoride (PVDF) powder as a binder.

[0043] Step 2: After mixing the above solid dry powder, add an appropriate amount of N-methylpyrrolidone (NMP) solvent and mechanically disperse it at room temperature using a planetary vacuum mixer to obtain a uniform negative electrode slurry.

[0044] Step 3: Use a scraper to uniformly coat the above negative electrode slurry onto one side of a pure copper foil current collector with a thickness of 8~10μm to obtain a coated wet film.

[0045] Step 4: Transfer the coated electrode to a vacuum drying oven and dry it at a constant temperature of 110°C to evaporate the NMP solvent, so as to obtain a dry basic negative electrode substrate electrode that has not been rolled, for later use.

[0046] Examples 1-4: Example 1: This embodiment provides a method for verifying the optimal compaction density performance of graphite anodes based on non-porous characteristics, including the following steps: Step 1: Using the same negative electrode slurry as in Example 1, a doctor blade was used to coat one side of a copper foil. The wet film thickness was set to 200 μm, the upper limit of the claim. The film was dried at 110°C to obtain an unrolled dry electrode sheet. A roller mill was then used to perform gradient rolling to prepare electrodes with compacted densities of uncompacted and 1.0 g / cm³. 3 1.2g / cm 3 and 1.4g / cm 3 The four sets of electrode plates.

[0047] Step 2: In a glove box filled with high-purity argon, using the above four sets of electrodes as working electrodes, lithium metal sheets as counter electrodes, and polypropylene (PP) microporous membranes as separators, inject 1.0M LiPF6 (EC:DEC=1:1) electrolyte to assemble a CR2032 coin cell analog battery.

[0048] Step 3: Using an electrochemical workstation, apply an amplitude of 5mV to perform EIS testing within a frequency range of 100kHz to 10mHz. Obtain the DRT spectrum using the Tikhonov regularization algorithm.

[0049] Step 4: Extract 10 -1 ~10 0 The charge transfer impedance (Rct) in the s-interval and 10 0 ~10 1 The diffusion resistance (Rdiff) in the s-interval. The results show that when the compaction density is 1.0 g / cm³. 3 At this point, Rct and Rdiff reach a common minimum (Rct≤135Ω, Rdiff≤190Ω), which is determined to be the optimal compaction density. Further charge-discharge tests at a 0.2C rate verify that the electrode specific capacity is ≥345.4mAh / g and the capacity distribution deviation is ≤1.2mAh / g, which meets expectations.

[0050] Example 2: This embodiment provides a method for verifying the optimal compaction density performance of graphite anodes based on non-porous characteristics, including the following steps: Step 1: Using the same porous graphite slurry formulation as in Example 2, the wet film thickness was adjusted to an intermediate value of 150 μm during the coating stage. After drying at 110 °C, an uncompacted electrode sheet was obtained. Gradient rolling was performed using a roller mill to prepare electrodes with compacted densities of uncompacted and 1.0 g / cm³. 3 1.2g / cm 3 and 1.4g / cm 3 The four sets of electrode plates.

[0051] Step 2: Using the same counter electrode, separator, and electrolyte as in Example 1, assemble a CR2032 coin cell analog battery.

[0052] Step 3: Perform EIS testing in the frequency range of 100kHz to 10mHz, and perform DRT deconvolution calculation and conversion.

[0053] Step 4: Because the internal pores of porous graphite can withstand pressure better, the DRT spectrum shows a compaction density of 1.2 g / cm³. 3 At this point, Rct and Rdiff reach a common minimum. The value is determined to be 1.2 g / cm³. 3This represents the optimal compaction density for the porous graphite electrode. Charge-discharge tests also verified that at this compaction density, the specific capacity is ≥343.1 mAh / g and the distribution deviation is ≤1.4 mAh / g.

[0054] Example 3: This embodiment provides a method for verifying the optimal compaction density performance of graphite anodes based on non-porous characteristics, including the following steps: Step 1: Using the same slurry ratio as in Preparation Example 3, the wet film thickness was adjusted to 100 μm (the lower limit of the claim) during the coating stage. After drying, an unrolled electrode sheet was obtained. This was then subjected to fine gradient rolling to produce a compacted density of 1.0 g / cm³. 3 1.1g / cm 3 and 1.2g / cm 3 Three sets of electrode plates.

[0055] Step 2: Assemble a coin cell analog battery and perform EIS testing in the frequency range of 100kHz to 10mHz to obtain the DRT spectrum.

[0056] Step 3: Extract the impedance value for the corresponding frequency band. Analysis shows that the impedance value is obtained at a compaction density of 1.1 g / cm³. 3 At this point, both Rct and Rdiff reach their minimum values ​​simultaneously. The threshold of 1.1 g / cm³ is determined. 3 The optimal compaction density for this electrode is determined, and the 0.2C discharge specific capacity meets the condition of ≥345.4mAh / g.

[0057] Example 4: This embodiment provides a method for verifying the optimal compaction density performance of graphite anodes based on non-porous characteristics, including the following steps: Step 1: Using the same low-graphite / high-auxiliary-boundary slurry as in Example 4, a wet film thickness of 120 μm was coated. High pressure was applied using a roller mill to prepare a compacted film with a density of 1.2 g / cm³. 3 (Comparison standard), 1.4 g / cm 3 (Over-compacted) and 1.5 g / cm 3 Three sets of electrodes (severely over-compacted).

[0058] Step 2: Assemble the CR2032 coin cell using the standard procedure, perform a scan in the 100kHz to 10mHz range, and then perform DRT conversion.

[0059] Step 3: Focus on observing 10 in the DRT spectrum -3 ~10 -1 s (SEI film impedance) and 10 0 ~10 1The peak position and peak intensity in the s (diffusion resistance Rdiff) range were determined. The results show that when the compaction density reaches 1.4 g / cm³... 3 When, diffusion impedance (10 0 ~10 1 s) The peak intensity increased significantly and the peak position shifted; at the same time, the SEI film impedance (10) -3 ~10 -1 s) The characteristic peak intensity rises abnormally. This phenomenon indicates the presence of side reactions (SEI thickening) caused by the mechanical fracture of particles leading to the exposure of fresh interfaces and pore blockage (impeded ion transport), which can trigger an overcompaction warning signal.

[0060] Comparative Examples 1-3: Comparative Example 1: Compared to Example 1, the difference lies in that this comparative example does not perform DRT (distributed relaxation time) deconvolution calculation on the acquired EIS frequency domain data. Instead, it directly uses the traditional EIS Nyquist plot and combines it with a conventional equivalent circuit model (such as the R(CR)(CR)W model) for curve fitting. The remaining steps and test parameters are the same as in Example 1.

[0061] Comparative Example 2: The difference compared to Example 2 is that this comparative example did not use electrochemical impedance spectroscopy to guide the selection of the optimal compaction density. Instead, it directly relied on traditional industry experience to forcibly roll-press the porous graphite anode (the ratio in Preparation Example 2) to 1.5 g / cm³. 3 The standard high-density charge is achieved. The remaining battery assembly and charge / discharge testing procedures are the same as in Example 2.

[0062] Comparative Example 3: Compared to Example 3, the difference lies in the following: This comparative example uses an extreme formulation that exceeds the scope of the claims. Specifically, 96 parts by weight of non-porous characteristic graphite, 2 parts by weight of Super-P conductive carbon black, and 2 parts by weight of PVDF binder are accurately weighed and mixed to form a slurry. The remaining parts are coated and compacted (gradients set to 1.0, 1.1, and 1.2 g / cm). 3 The assembly and DRT analysis steps are the same as in Example 3.

[0063] Test Examples 1-6: Test Example 1: Feasibility Test of Separating Overlapping Time Constants in DRT Analysis This test case aims to verify the feasibility of using relaxation time distribution (DRT) analytical techniques to decouple and separate overlapping physicochemical processes in electrochemical impedance spectroscopy (EIS).

[0064] Experimental steps: Import the EIS frequency domain impedance data exported from the electrochemical workstation into the analysis software, perform the Kramers-Kronig relationship test, and remove low-frequency noise data points that do not meet the linearity and causality requirements.

[0065] Establish the impedance model as ,in For ohmic internal resistance, The polarization impedance is used. The Tikhonov regularization method is employed to solve for the DRT function. Set the time constant. The distribution range is 10 -5 s to 10 2 s, regularization parameter The value is determined using a generalized cross-validation method.

[0066] Run the regularization algorithm program to process the frequency domain impedance data. Convert to time constant domain distribution function The horizontal axis is generated as the time constant. The vertical axis is The DRT spectrum.

[0067] The characteristic peaks of each polarization process are calibrated according to the set time constant interval, and the areas corresponding to each characteristic peak are numerically integrated to extract 10. -5 s to 10 -3 Contact resistance in the s-interval, 10 -3 s to 10 -1 SEI film impedance in the s-interval, 10 -1 s to 10 0 The charge transfer impedance Rct in the s-interval, and 10 0 s to 10 1 The absolute impedance value of the diffusion impedance Rdiff in the s-interval.

[0068] Experimental data: Table 1. Distribution of impedance values ​​in each time constant interval obtained by DRT deconvolution under various compaction densities in Examples 1 and 2.

[0069] Conclusion section: Based on the data in Table 1, the overlapping interfacial reactions and mass transport processes in the AC impedance spectrum were separated using the DRT analytical algorithm. The Nyquist plot of conventional EIS frequency domain data is shown below. Figure 1 and Figure 2 As shown, ( Figure 1 and Figure 2 The horizontal axis Z' represents the real part of the impedance, and the vertical axis Z'' represents the imaginary part of the impedance. Figures 1 to 4In the legend, "unrolled" or "unlled" both refer to the uncompacted electrode state. In the mid-to-high frequency range, this appears as a semicircle formed by the superposition of the SEI film capacitance and the double-layer capacitance, indicating an overlap of the physical processes in the three time constant ranges: ohmic contact, lithium-ion penetration through the SEI film, and charge transfer at the solid-liquid interface. Tikhonov regularization transforms the frequency domain data to the time constant domain, such as... Figure 3 (Sample A) and Figure 4 The DRT analysis curve of (sample B) shows that this technique successfully... Figure 1 , Figure 2 The overlapping EIS semicircles in the middle are decomposed into separate polarization internal resistance peaks, thus separating the above physical processes.

[0070] Among the extracted data indicators, the physical contact impedance decreased with increasing compaction density. In Example 1, this value decreased from 28.43Ω to 9.87Ω, indicating that mechanical compaction increased the physical contact area between particles. The SEI film impedance, charge transfer impedance Rct, and diffusion impedance Rdiff in adjacent frequency bands did not show a monotonically decreasing trend.

[0071] For the non-porous graphite of Example 1, the compacted density is 1.0 g / cm³. 3 At this point, Rct is 114.8 Ω and Rdiff is 152.5 Ω, both reaching their minimum values. The compacted density increases to 1.4 g / cm³. 3 At that time, Rdiff increased to 171.0 Ω, and the SEI film impedance increased from 24.37 Ω to 41.25 Ω, indicating that pore blockage limited lithium-ion liquid-phase transport, and particle fragmentation increased solid-liquid interface side reactions. In the porous graphite of Example 2, the minimum values ​​of Rct and Rdiff occurred at a compaction density of 1.2 g / cm³. 3 At these locations, the corresponding impedance values ​​are 131.3Ω and 170.9Ω, respectively. The independent variation of impedance values ​​in different time constant intervals confirms the feasibility of this method in separating overlapping physical processes, and can locate the optimal compaction density range for achieving kinetic equilibrium based on the variation of impedance parameters.

[0072] Test Example 2: Dynamic Law Test of Impedance Parameter Evolution with Compacted Density

[0073] Experimental steps: The non-porous and porous graphite electrodes generated by regularization in Test Example 1 were read under uncompacted conditions at 1.0 g / cm³. 3 1.2g / cm 3 and 1.4g / cm 3 Data on the DRT distribution function under compaction density gradient.

[0074] In the time constant domain distribution spectrum, locate the time constant 10. -1s to 10 0 The characteristic peaks in the s-interval are used to calculate the area integral of the distribution function of this frequency band, and the charge transfer impedance Rct quantization value under each compaction density condition is extracted.

[0075] Positioning time constant 10 0 s to 10 1 The characteristic peak in the s-interval is used to calculate the area integral of the distribution function of this frequency band, and the diffusion impedance Rdiff quantization value is extracted under each compaction density condition.

[0076] The extracted absolute impedance values ​​of Rct and Rdiff were statistically summarized according to material type and compaction density to form a set of microscopic dynamic parameters corresponding to different macroscopic structural states of electrodes.

[0077] Experimental data: Table 2. Quantitative data on charge transfer impedance and diffusion impedance under different compaction densities in Examples 1 and 2.

[0078] Conclusion section: According to the data in Table 2, the charge transfer impedance Rct and diffusion impedance Rdiff of the graphite anode show a trend of first decreasing and then increasing with the increase of electrode compaction density. The macroscopic compaction density of the electrode directly affects the pore structure and solid-liquid interface state inside the electrode.

[0079] For the non-porous characteristic graphite of Example 1, from the uncompacted state to 1.0 g / cm³ 3 Rct decreased from 135.7 Ω to 114.8 Ω, and Rdiff decreased from 170.1 Ω to 152.5 Ω. Mechanical compression brought the active material particles, conductive agent, and current collector into contact, establishing an electron conduction network. The electrode layer retained its pore volume and tortuosity, allowing for thorough electrolyte wetting and maintaining unobstructed diffusion paths for lithium ions at the solid-liquid interface. At this point, electron conduction and ion mass transfer achieved kinetic matching at the solid-liquid interface, with both Rct and Rdiff exhibiting minimum values. Figure 3 As shown, this indicates that the time constant is at 10. -1 ~10 0 s (corresponding to Rct) and 10 0 ~10 1 The characteristic peak of s (corresponding to Rdiff) at a compaction density of 1.0 g / cm³ 3 At the (red line) point, the peak width and peak height (i.e., the integral area) reach their minimum, consistent with the aforementioned quantitative data. The compacted density increases to 1.4 g / cm³. 3 At that time, Rdiff rose to 171.0 Ω and Rct rose to 153.2 Ω. The higher mechanical pressure caused the collapse of the original pore structure inside the electrode, hindering electrolyte permeation and blocking ion transport pathways. Figure 5 The provided microscopic morphological evidence shows that, under both uncompacted and moderately compacted conditions, cross-sectional images reveal clearly defined ion transport pores between particles; however, when the density reaches 1.4 g / cm³, the pore size decreases. 3 At a compacted density (as shown by the red arrow in the cross-sectional diagram), the pores are completely blocked. The fracturing of graphite particles disrupts the local conductive network, leading to a simultaneous increase in interfacial polarization and mass transfer resistance.

[0080] The porous graphite data from Example 2 show that the minimum values ​​of Rct and Rdiff occur at 1.2 g / cm³. 3 At compacted densities, the values ​​are 131.3Ω and 170.9Ω, respectively. Figure 4 The DRT spectrum shows a compaction density of 1.2 g / cm³. 3 The form of the characteristic peak with the minimum integral area (blue line) is intuitively reflected. Compared with non-porous graphite, porous graphite has a larger internal pore volume and a higher mechanical stress resistance. For example... Figure 6 The nitrogen adsorption and desorption isotherms are shown in the figure. Figure 6 In the diagram, the horizontal axis P / Po represents relative pressure, and the vertical axis ΣΔV represents the cumulative volume of adsorbed gas. Sample B (red line, corresponding to porous graphite) shows a desorption branch that does not coincide with the adsorption branch, exhibiting a distinct H3-type hysteresis loop, characteristic of a typical type IV adsorption isotherm, confirming its well-developed internal mesoporous / porous structure. Sample A (black line), on the other hand, exhibits better closure and no obvious internal pores. Based on these differences in the intrinsic pore structure of porous graphite, the internal pores of porous graphite can buffer greater mechanical stress during compaction, thus achieving a higher density of adsorbed gas at 1.2 g / cm³. 3 At the compacted density, the internal pore structure remains open. The compacted density reaches 1.4 g / cm³. 3 At this point, Rdiff increases to 248.3Ω, at which point the pores become substantially blocked. The impedance change patterns of the two materials confirm that by separating the impedance parameters within a specific time constant range using DRT analytical techniques, it is possible to locate the compaction density node that balances the electrode structure and kinetic performance.

[0081] Test Example 3: Early Warning Characteristics Test of DRT for Over-compaction of Electrodes

[0082] Experimental steps: Reading from Example 4, 1.2 g / cm 3 1.4g / cm 3 With 1.5g / cm 3 The EIS frequency domain data of the electrode assembly coin cells with compacted density were used to perform Kramers and Kronig relationship tests to remove noisy data points that did not meet the causal relationship.

[0083] The frequency domain data is converted into a time constant domain distribution function using the Tikhonov regularization method, generating a DRT spectrum with the time constant as the horizontal axis and the distribution function value as the vertical axis.

[0084] Locate 10 in the DRT spectrum -3 s to 10 -1 The characteristic peaks in the s-interval are recorded, and the time constant positions corresponding to the peak apex are recorded. The absolute value of the SEI film impedance within the frequency band is calculated by area integration.

[0085] Locate 10 in the DRT spectrum 0 s to 10 1 The characteristic peaks in the s-interval are recorded, and the time constant positions corresponding to the peak vertices are recorded. The absolute value of the diffusion impedance within the frequency band is calculated by area integration.

[0086] The impedance values ​​and the time constants corresponding to the peak positions at each compaction density were statistically analyzed.

[0087] Experimental data: Table 3. Characteristic parameters of SEI film impedance and diffusion impedance at various compaction densities in Example 4.

[0088] Conclusion section: According to the data in Table 3, increasing the compaction density altered the values ​​of the SEI film impedance and diffusion impedance, as well as the time constant corresponding to the characteristic peak. (Using 1.2 g / cm³ as an example...) 3 Using the electrode parameters of compaction density as a baseline, the compaction density was increased to 1.4 g / cm³. 3 With 1.5g / cm 3 At that time, the diffusion impedance increased from 168.45Ω to 305.72Ω and 583.16Ω, respectively, and the diffusion peak time constant increased from 1.05×10 0 s offset to 3.26×10 0 s and 7.84×10 0 An increase in the time constant indicates a longer transport path for lithium ions within the solid phase and pores; excessive mechanical pressure leads to the blockage of micropores in the electrode. For example... Figure 5 As shown, when the compaction density reaches over-compaction (1.4 g / cm³), 3 In the cross-sectional morphology of the electrode, the particles are severely compressed and deformed or even mechanically fractured, and the electrode is extremely dense. The above-mentioned macroscopic morphology densification phenomenon is consistent with the electrochemical test results of increased diffusion impedance.

[0089] In addition, 10 -3 s to 10 -1 The SEI film impedance in the s-region is 1.2 g / cm. 3 The Ω increased from 26.83Ω to 1.5g / cm. 3At a impedance of 67.91Ω, the SEI peak time constant also shifted. The increase in impedance and time constant indicates that the graphite particles fractured under mechanical stress. The exposure of internal carbon material triggered an electrolyte reduction reaction, causing the SEI film inside the battery to thicken. Changes in impedance and peak time constant within a specific frequency band can be detected while maintaining the integrity of the battery structure; these changes constitute the basis for determining over-compaction of the electrode.

[0090] Test Example 4: Comparison Test of Impedance Parameter Extraction Accuracy and Optimal Compacted Density Identification

[0091] Experimental steps: The non-porous graphite electrodes from Example 1 and Comparative Example 1 were obtained under uncompacted conditions at a density of 1.0 g / cm³. 3 1.2g / cm 3 and 1.4g / cm 3 Raw EIS frequency domain data under compaction density. The two sets of test data are from the same batch of electrode sheets, differing only in the analytical method.

[0092] The Tikhonov regularization algorithm was applied to the data from Example 1 to transform the frequency domain impedance to the time constant domain. For 10... -1 s to 10 0 s interval and 10 0 s to 10 1 The distribution function in the s-interval is integrated to extract the charge transfer impedance Rct and the diffusion impedance Rdiff.

[0093] The data in Comparative Example 1 are fitted with a complex plane curve using an equivalent circuit model to extract the charge transfer impedance Rct corresponding to the semicircle in the mid-frequency region and the diffusion impedance Rdiff corresponding to the straight line in the low-frequency region.

[0094] Record the impedance values ​​extracted by the two analytical methods under different compaction densities, and compare the compaction densities corresponding to the minimum impedance values ​​calculated by the two methods.

[0095] Experimental data: Table 4 Comparison of Impedance Parameter Extraction and Quantification between Example 1 and Comparative Example 1

[0096] Conclusion section: According to the data in Table 4, the impedance parameters extracted by the equivalent circuit fitting method and the DRT analytical method differ in both value and trend. Comparative Example 1, which uses an equivalent circuit model for fitting, yields a higher Rct value than Example 1. The fitted Rct in the uncompacted state is 231.45 Ω, and the compacted density is 1.0 g / cm³. 3 The Ω dropped to 164.82 Ω at 1.2 g / cm. 3The value reaches a minimum of 158.37 Ω. The fitted Rdiff minimum also corresponds to 1.2 g / cm³. 3 Compacted density. In Example 1, the DRT analytical method was used, and the minimum values ​​of Rct and Rdiff extracted both corresponded to 1.0 g / cm³. 3 The compaction densities are 114.8Ω and 152.5Ω, respectively.

[0097] In EIS frequency domain testing, the time constants of physical contact impedance, SEI film impedance, and charge transfer impedance are similar. The equivalent circuit fitting method, using a fixed combination of components, failed to separate the overlapping physical processes in the frequency domain spectrum. Combined with... Figure 1 and Figure 2 As can be seen, the traditional EIS plot only presents a broad, overlapping semicircle in the mid-to-high frequency region. This overlap easily masks the true decrease in contact impedance with increasing compaction density. The equivalent circuit fitting method includes the physical contact impedance and SEI film polarization impedance in the mid-frequency region Rct, resulting in an overestimation of the extracted impedance values. The contact impedance decreases with increasing compaction density, and the superposition of impedance values ​​masks the charge transfer impedance at 1.0 g / cm³. 3 The inflection point of the increase in compaction density after compaction leads to the situation in Comparative Example 1 due to traditional... Figure 1 The fitting error caused a deviation, and the optimal compaction density was determined to be 1.2 g / cm³. 3 The DRT analytical method transforms frequency domain data into the time constant domain, separates the physical processes with different relaxation time constants, eliminates numerical interference caused by the decrease in contact impedance in adjacent frequency bands, and accurately reflects the dynamic evolution of charge transfer and solid-state diffusion processes.

[0098] Test Example 5: Comparison Test of Electrochemical Capacity Performance and Consistency

[0099] Experimental steps: 1.2 g / cm³ was selected from Example 2. 3 Porous graphite electrode sheets prepared under compacted density, and the 1.5 g / cm³ graphite electrode in Comparative Example 2. 3 Porous graphite electrodes prepared under compacted density were assembled into coin cells. Five parallel samples from each group were tested.

[0100] The assembled battery is placed in the battery test cabinet and left to stand for 12 hours to allow the electrolyte to wet the electrode plates. The test environment temperature is set to 25℃.

[0101] The charge / discharge voltage range was set to 0.01V to 1.5V. Activation charge / discharge was performed at a rate of 0.05C, followed by constant current charge / discharge testing at a rate of 0.2C.

[0102] Record the initial discharge specific capacity of each battery at a 0.2C rate. Calculate the average discharge specific capacity of each group of batteries, and use the standard deviation formula to calculate the capacity distribution deviation of samples within the same batch.

[0103] Experimental data: Table 5. Discharge specific capacity and distribution deviation data of batteries in Example 2 and Comparative Example 2 at 0.2C rate.

[0104] Conclusion section: According to the data in Table 5, the electrode compaction density affects the discharge specific capacity of the battery and its consistency with the same batch. Example 2 shows a compaction density of 1.2 g / cm³. 3 At the compacted density, the average discharge specific capacity of the five parallel samples was 343.1 mAh / g, with a distribution deviation of 0.99 mAh / g. Comparative Example 2 was at 1.5 g / cm³. 3 At the compacted density, the average discharge specific capacity is 319.8 mAh / g, and the distribution deviation increases to 6.95 mAh / g.

[0105] At 1.2 g / cm 3 At compacted density, the ion transport pathways and electron conduction networks within porous graphite electrodes reach kinetic equilibrium. Figure 4 The DRT spectrum (1.2 g / cm) 3 The microscopic dynamic state reflected by the smallest area of ​​the time-polarized characteristic peak is consistent with that of the time-polarized characteristic peak. Figure 6 The nitrogen adsorption and desorption test results show that the well-developed internal pore structure of porous graphite effectively buffers mechanical stress, allowing electrolyte to penetrate into the internal pores of graphite, improving the utilization rate of active materials, and maintaining the initial discharge specific capacity at around 343.1 mAh / g. Mechanical stress did not cause damage to the electrode microstructure, and the performance of batteries in the same batch remained consistent.

[0106] The porous graphite electrode sheet was rolled to 1.5 g / cm³. 3 At that time, mechanical pressure caused the internal pore structure of the electrode to collapse. As mentioned above. Figure 5 The severe compression state visually demonstrated by the microscopic morphology of the electrode cross-section indicates that excessively high compaction density leads to significant structural deterioration. Microscopic pore blockage hinders electrolyte penetration to deeper layers, and some active materials fail to participate in the electrochemical reaction. Graphite particles fracture under stress, exposing the internal carbon surface. This surface increases side reactions with the electrolyte, consuming active lithium to rebuild the SEI film, resulting in a decrease in discharge capacity. Under excessively high compaction, the internal stress distribution of the electrode is uneven, with varying degrees of pore collapse and particle fracture in different regions, leading to increased capacity distribution deviations within the same batch of batteries. The compaction density determined by DRT analysis ensures the electrochemical performance and consistency of the battery.

[0107] Test Example 6: Rate Discharge Performance and Polarization Voltage Test under Different Compaction Densities

[0108] Experimental steps: The sample selected from Example 1 was uncompacted and had a density of 1.0 g / cm³. 3 Compacted density is 1.4 g / cm³ 3 Compacted, non-porous graphite electrodes are assembled into coin cells and connected to a battery testing system.

[0109] The sample was left to stand at a constant temperature of 25°C for 12 hours. The test voltage range was set from 0.01V to 1.5V, and three charge-discharge activation cycles were performed at a rate of 0.1C.

[0110] Constant current and constant voltage charging was performed at a rate of 0.2C, and constant current discharging was performed at rates of 0.2C, 1C and 5C respectively. The discharge specific capacity at each discharge rate was recorded.

[0111] Extract the operating voltage at which the discharge capacity reaches 50% under each discharge rate, and subtract it from the equilibrium voltage at the same discharge depth under a 0.1C rate to obtain the median polarization voltage under each test condition.

[0112] Experimental data: Table 6. Discharge specific capacity and polarization voltage data of electrodes with different compaction densities in Example 1 at different magnification rates.

[0113] Conclusion section: According to the data in Table 6, the electrode compaction density affects the discharge specific capacity and polarization voltage of the battery under high-rate conditions.

[0114] 1.0g / cm 3 At the compacted density, the battery exhibits a discharge specific capacity of 345.4 mAh / g at 0.2C and 234.8 mAh / g at 5C, with a median polarization voltage of 105.7 mV. In the uncompacted state, the discharge specific capacity at 5C is 112.4 mAh / g, and the median polarization voltage is 214.3 mV. The compacted density is 1.4 g / cm³. 3 At 0.2C rate, the discharge specific capacity is 335.4 mAh / g, while at 5C rate, the discharge specific capacity drops to 68.2 mAh / g, and the median polarization voltage rises to 342.1 mV.

[0115] In uncompacted electrodes, the physical contact between particles is insufficient, and an electron conduction network is not established, resulting in ohmic polarization voltage during high-rate discharge. The compaction density is 1.0 g / cm³. 3 At this time, an electron conduction network is formed inside the electrode, while retaining the pores for lithium-ion transport. For example... Figure 3As shown in the DRT analytical curve, at this compaction density, the characteristic peaks representing charge transfer and solid-phase diffusion are both at their minimum values. Simultaneously, combined with... Figure 5 The cross-sectional morphology under moderate compaction shows that the electrode is both dense and retains clear transport channels. Therefore, during high-rate discharge, lithium ions in the electrolyte migrate to the surface of graphite particles, the electron and ion transport rates match, and the battery's working polarization is reduced.

[0116] The compacted density increased to 1.4 g / cm³. 3 hour, Figure 5 The substantial pore blockage and localized particle fracture observed in the cross-sectional morphology indicate that excessive mechanical pressure leads to micropore blockage and elongates the diffusion path in the ion-liquid phase. High current density testing amplifies the limitations of mass transfer resistance in the liquid phase, increasing concentration polarization. The increased polarization voltage causes the operating voltage to reach the discharge cutoff voltage earlier, terminating the discharge process and reducing the usable discharge capacity. Macroscopic electrochemical test results are consistent with... Figure 3 The diffusion impedance variation law obtained by DRT analysis corresponds to the impedance parameters extracted by DRT, which reflect the dynamic transmission limitation state of the electrode under dynamic working conditions.

Claims

1. A method for optimizing the compaction density of graphite anodes based on relaxation time distribution analysis, characterized in that, Includes the following steps: Several groups of dry graphite anode substrates with different gradient compaction densities were prepared. Each group of electrode sheets is assembled into a button cell simulated battery; AC impedance spectroscopy was performed on each group of coin cell analog batteries. A preset AC voltage amplitude was applied in the frequency range of 100kHz to 10mHz to obtain frequency domain impedance data. The frequency domain impedance data is deconvolved using the Tikhonov regularization algorithm to convert the frequency domain impedance data into a relaxation time distribution spectrum with the time constant as the horizontal axis. The location time constant in the distribution spectrum is 10 -1 The area integral is performed on the characteristic peaks in the s to 1s interval to extract the absolute value of the corresponding charge transfer impedance. In the distribution spectrum, the area integration of the characteristic peaks with time constants in the range of 1s to 10s is performed to extract the corresponding absolute value of diffusion impedance. By comparing the absolute values ​​of the charge transfer impedance and the diffusion impedance under different gradient compaction density conditions, the compaction density corresponding to the lowest value of both within the gradient range is determined as the optimal compaction density.

2. The method for optimizing the compaction density of graphite anodes based on relaxation time distribution analysis according to claim 1, characterized in that, Before performing deconvolution calculations using the Tikhonov regularization algorithm, the Kramers-Kronig relationship test is performed on the frequency domain impedance data to remove noisy data points that do not satisfy the causal relationship. The established impedance model of the Tikhonov regularization algorithm is that the AC impedance is equal to the sum of the ohmic internal resistance and the polarization impedance. The value of the regularization parameter of the Tikhonov regularization algorithm is determined by the generalized cross-validation method.

3. The method for optimizing the compaction density of graphite anodes based on relaxation time distribution analysis according to claim 1, characterized in that, The step of extracting the absolute value of impedance from the distribution spectrum further includes: Positioning time constant is 10 -3 s to 10 -1 The area integral is performed on the characteristic peaks in the s-interval to extract the absolute value of the corresponding SEI film impedance. When the increase in compaction density leads to an increase in the absolute value of the extracted diffusion impedance and a shift in the peak position towards a larger time constant, and at the same time the absolute value of the SEI film impedance increases, it is determined that the electrode corresponding to the compaction density is in an over-compacted state.

4. The method for optimizing the compaction density of graphite anodes based on relaxation time distribution analysis according to claim 1, characterized in that, Before performing AC impedance spectroscopy tests on the coin cells in each group, the coin cells in each group were placed in a constant temperature environment at 25°C for 12 hours.

5. The method for optimizing the compaction density of graphite anodes based on relaxation time distribution analysis according to claim 1, characterized in that, The specific implementation method for preparing several groups of dry graphite anode substrates with different gradient compaction densities is as follows: After mixing graphite active material, conductive carbon black as a conductive agent and polyvinylidene fluoride powder as a binder, the mixture is added to a solvent for mechanical dispersion to obtain a uniform negative electrode slurry. The negative electrode slurry is uniformly coated on one side of the current collector to obtain a coated wet film. The coated wet film is dried at a constant temperature to evaporate the solvent, resulting in an electrode sheet that has not been rolled. The unpressed electrode sheet is subjected to gradient rolling using a roller mill to prepare the dry graphite anode substrate electrode sheet with different gradient compaction densities.

6. The method for optimizing the compaction density of graphite negative electrodes based on relaxation time distribution analysis according to claim 5, characterized in that, The solid component in the negative electrode slurry is composed of the following raw materials in parts by weight: 90 to 92 parts of the graphite active material, 3 to 4 parts of the conductive carbon black, and 5 to 6 parts of the polyvinylidene fluoride powder.

7. The method for optimizing the compaction density of graphite negative electrodes based on relaxation time distribution analysis according to claim 6, characterized in that, The graphite active material is non-porous graphite or porous graphite. The BET specific surface area of ​​the non-porous graphite is less than or equal to 5m². 2 / g; The porous graphite has a BET specific surface area greater than 5 m². 2 / g, and possesses a type IV adsorption isotherm and a type H3 hysteresis loop.

8. The method for optimizing the compaction density of graphite negative electrodes based on relaxation time distribution analysis according to claim 5, characterized in that, The process parameters for preparing dry graphite anode substrates are controlled as follows: The solvent used is N-methylpyrrolidone, the current collector is a pure copper foil with a thickness in the range of 8μm to 10μm, the thickness of the wet film is controlled between 100μm and 200μm during the coating stage, and the temperature of the constant temperature drying is controlled at 110℃.

9. The method for optimizing the compaction density of graphite negative electrodes based on relaxation time distribution analysis according to claim 1, characterized in that, The specific implementation method for assembling the electrode sheets of each group into a coin cell simulated battery is as follows: In a glove box filled with high-purity argon, a button cell analog battery is assembled by using the dry graphite negative electrode substrate as the working electrode, a lithium metal sheet as the counter electrode, and a polypropylene microporous membrane as the separator, and injecting a lithium hexafluorophosphate electrolyte containing ethylene carbonate and diethyl carbonate.

10. The method for optimizing the compaction density of graphite anodes based on relaxation time distribution analysis according to claim 1, characterized in that, After determining the optimal compaction density, the process also includes assembling batteries using electrodes with the optimal compaction density, conducting constant current charge-discharge tests at a 0.2C rate, and recording the discharge specific capacity and capacity distribution deviation of samples from the same batch.

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