High-valence doped mixed sodium ferric phosphate positive electrode material for sodium-ion battery and performance test method of high-valence doped mixed sodium ferric phosphate positive electrode material

Abstract

The invention relates to a high-valence doped mixed sodium ferric phosphate positive electrode material of a sodium ion battery and a performance testing method of the high-valence doped mixed sodium ferric phosphate positive electrode material. The high-valence doped mixed sodium ferric phosphate positive electrode material of the sodium ion battery comprises an inner core and a carbon layer coating the inner core, the inner core comprises a mixed sodium ferric phosphate material; the molecular formula of the mixed sodium ferric phosphate material is Na Fe < b-x > M < x > (PO4) < c > P < 2 > O < 7 >; wherein a, b and c are two groups of associated values which are respectively 4, 3 and 2 and 3, 2 and 1, and x is more than 0 and less than or equal to 0.27; m is at least one of V, Nb, Ta, Ce and Hf; the high-valence doped mixed sodium ferric phosphate positive electrode material for the sodium-ion battery is particles with the average particle size of 1-10 microns. The high-valence doped mixed sodium ferric phosphate positive electrode material of the sodium ion battery provided by the invention has better performance parameters such as rate capability and specific discharge capacity of the material.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries, and particularly relates to a high-valence doped mixed sodium iron phosphate cathode material for sodium-ion batteries and a performance testing method thereof. Background Art

[0002] The cathode material is an important component of the battery and is responsible for storing and releasing cations during the charge and discharge process. Its performance directly affects key indicators such as the energy density, cycle life, safety, and cost of the battery. The cathode materials in the prior art mainly include various lithium-ion batteries. However, due to the more abundant reserves of sodium resources in nature, the cost of sodium-ion batteries may be further reduced. Therefore, the development of sodium-ion batteries has attracted more and more attention. The current sodium-ion cathode materials still have the problem of low performance, and the material performance needs to be repeatedly tested during the production and manufacturing process. Summary of the Invention

[0003] Based on this, in view of the above technical problems, it is necessary to provide a high-valence doped mixed sodium iron phosphate cathode material for sodium-ion batteries and a performance testing method thereof.

[0004] In a first aspect, the present application provides a high-valence doped mixed sodium iron phosphate cathode material for sodium-ion batteries, including a core and a carbon layer coating the core;

[0005] The core includes a mixed sodium iron phosphate material;

[0006] The molecular formula of the mixed sodium iron phosphate material is: Na a Fe b-x M x (PO4) c P2O7;

[0007] Where a, b, c are two sets of associated values of 4, 3, 2 and 3, 2, 1 respectively, and 0 < x ≤ 0.27; M is at least one of V, Nb, Ta, Ce, and Hf;

[0008] The high-valence doped mixed sodium iron phosphate cathode material for sodium-ion batteries is particles with an average particle size of 1 μm to 10 μm.

[0009] In one embodiment, the high-valence doped mixed sodium iron phosphate cathode material for sodium-ion batteries is prepared by the following method:

[0010] Dissolve phosphate, iron salt, sodium salt, M salt, and carbon source in deionized water respectively to obtain a mixed solution; where M is at least one of V, Nb, Ta, Ce, and Hf;

[0011] Dry the mixed solution to obtain a powdery mixed precursor;

[0012] The powdery mixed precursor is heat-treated under an inert reducing atmosphere to obtain a mixed sodium iron phosphate cathode material.

[0013] In one embodiment, the phosphate is one or a mixture of two or more of sodium dihydrogen phosphate, acid sodium pyrophosphate, ammonium dihydrogen phosphate, sodium phosphate, and sodium pyrophosphate;

[0014] The iron salt is one or a mixture of two or more of iron phosphate, iron pyrophosphate, and iron nitrate;

[0015] The sodium salt is one or a mixture of two or more of sodium pyrophosphate, sodium phosphate, sodium dihydrogen phosphate, and sodium acetate;

[0016] The M salt is one or a mixture of two or more of tantalum pentoxide, tantalum pentachloride, potassium tantalate, tantalum hydroxide, tantalum carbide, hafnium dioxide, cerium dioxide, niobium pentachloride, and vanadium pentoxide;

[0017] The carbon source is one or a mixture of two or more of citric acid, phenolic resin, oxalic acid, and ascorbic acid; the carbon source accounts for 1-10 wt.% in the material dissolved in deionized water.

[0018] In a second aspect, the present application provides a performance testing method, including:

[0019] Step 101, preparing a sodium-ion battery and performing the first charge-discharge cycle at the first charge-discharge rate of the sodium-ion battery, and recording the battery capacity of the sodium-ion battery as Q1, which is used as the reference capacity; wherein, the positive electrode sheet of the sodium-ion battery uses the high-valence doped mixed sodium iron phosphate cathode material of the sodium-ion battery according to any one of the first aspects of the present application;

[0020] Step 102, after the first charge-discharge cycle is completed, performing 100-1000 charge-discharge acceleration cycles at the second charge-discharge rate to accelerate the battery aging process, wherein the second charge-discharge rate is greater than the first charge-discharge rate;

[0021] After the charge-discharge acceleration cycle is completed, return to the first charge-discharge rate again to perform the charge-discharge cycle, and record the current battery capacity Q2;

[0022] Step 103, repeating Step 102 until the battery capacity Qn of the nth charge-discharge cycle at the first charge-discharge rate is less than or equal to the preset battery capacity threshold, wherein the preset battery capacity threshold is 80% of the reference capacity;

[0023] Step 104, calculating the average battery capacity during the test according to the battery capacities Q1, Q2,..., Qn recorded in each cycle;

[0024] According to the battery capacity and average battery capacity recorded in each cycle, the changing trend of the battery capacity with the number of cycles is analyzed by statistical methods to obtain the life prediction values of sodium-ion batteries under different charge-discharge conditions.

[0025] In one embodiment, the negative electrode of the sodium-ion battery is made of hard carbon, the electrolyte is a sodium salt solution of 1 mol / L NaClO4, and the solvent of the sodium salt solution is ethylene carbonate and dimethyl carbonate with a volume ratio of 1:1.

[0026] In one embodiment, the positive electrode plate of the sodium-ion battery is prepared by the following method:

[0027] Weigh and mix the high-valence doped mixed sodium iron phosphate cathode material, super-P, and binder of the sodium-ion battery according to the mass ratio of 8:1:1 to obtain a mixed material;

[0028] Dissolve the mixed material in N-methylpyrrolidone to obtain a solution;

[0029] Apply the solution on the aluminum foil, and place it in a vacuum drying oven at 100 °C for drying and heat preservation for 10 h to obtain the positive electrode plate of the sodium-ion battery.

[0030] In one embodiment, the first charge-discharge rate is 1C, and the second charge-discharge rate is 2C to 100C.

[0031] In one embodiment, the current 1C is 129 mA / g.

[0032] In one embodiment, according to the battery capacity and average battery capacity recorded in each cycle, the changing trend of the battery capacity with the number of cycles is analyzed by statistical methods to obtain the life prediction values of sodium-ion batteries under different charge-discharge conditions, including:

[0033] Based on the battery capacity and average battery capacity recorded in each cycle, a linear regression model corresponding to the battery capacity is constructed; the expression of the linear regression model is:

[0034] Y = β0 + β1X + ε

[0035] Among them, Y represents the battery capacity, X represents the number of cycles, β0 is the intercept, β1 is the slope, and ε is the error term;

[0036] Based on the preset charge-discharge conditions and the preset minimum battery capacity, the number of cycles corresponding to the battery capacity in the linear regression model is determined, and the life prediction value of the sodium-ion battery is determined according to the number of cycles; among them, the life prediction value is the duration when the battery capacity of the sodium-ion battery reaches the preset minimum battery capacity.

[0037] In one embodiment, according to the battery capacity and average battery capacity recorded in each cycle, the change trend of the battery capacity with the number of cycles is analyzed by statistical methods to obtain the life prediction values of the sodium-ion battery under different charge and discharge conditions, including:

[0038] According to the battery capacity and average battery capacity recorded in each cycle, use non-linear SVM to predict the life prediction value of the sodium-ion battery.

[0039] The above-mentioned high-valence doped mixed sodium iron phosphate cathode material for sodium-ion battery and its performance testing method improve the performance of the cathode material by doping high-valence metals to replace part of the iron. The cathode electrode sheet of the sodium-ion battery is prepared by using the high-valence doped mixed sodium iron phosphate cathode material, and the battery capacity after cyclic charge and discharge of the sodium-ion battery under different charge and discharge conditions is recorded. The change trend of the battery capacity with the number of cycles is analyzed by statistical methods to obtain the life prediction values of the sodium-ion battery under different charge and discharge conditions, which can improve the test accuracy and thus contribute to improving the performance of the sodium-ion battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 SEM (Scanning Electron Microscope) image of a tantalum-iron-based sodium iron pyrophosphate composite cathode material provided by the present invention;

[0042] Figure 2 XRD (X-ray Diffraction) pattern of the tantalum-iron-based sodium iron pyrophosphate composite cathode material prepared in Example 1 of the present invention;

[0043] Figure 3 Charge and discharge curve of the button cell assembled in Example 1 of the present invention at a rate of 0.1C;

[0044] Figure 4 For the present invention Figure 5 1C cycle curve of the button cell assembled in Example 2;

[0045] Figure 5 Flowchart of a performance testing method for a high-valence doped mixed sodium iron phosphate cathode material for sodium-ion battery provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to make the objectives, technical solutions and advantages of this application more clear and understandable, the following further details this application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above description of the drawings are intended to cover non-exclusive inclusion.

[0048] In the description of the embodiments of this application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "a plurality of" is more than two, unless otherwise specifically defined.

[0049] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appearing in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0050] In the description of the embodiments of this application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.

[0051] In the description of the embodiments of this application, the term "a plurality of" refers to more than two (including two). Similarly, "a plurality of groups" refers to more than two groups (including two groups), and "a plurality of pieces" refers to more than two pieces (including two pieces).

[0052] Unless otherwise stated or there is a contradiction, the terms or phrases used in this application have the following meanings:

[0053] Particle size: For spherical particles, the particle size refers to the diameter of the spherical particle. For non-spherical particles, such as particles with an olivine morphology, the particle size generally refers to the equivalent particle size of the non-spherical particle (generally abbreviated as particle size), and the particle size measured by a laser particle size analyzer is the equivalent diameter of the particle. Among them, the equivalent particle size means that when a certain physical property of a particle is the same as or similar to that of a homogeneous spherical particle, the diameter of the spherical particle is used to represent the diameter of this actual particle. Unless otherwise specified or there are contradictions, the particle size in this application represents the equivalent particle size.

[0054] Sodium-ion batteries (SIBs) have an increasingly high demand for capacity and tap density during the commercialization process. High capacity and high tap density mean higher energy density after being assembled into a battery. Currently, the electrode materials of SIBs face some huge challenges, such as insurmountable structural instability, sluggish ion diffusion, low working voltage, and low energy / power density, etc. To solve these problems, researchers mainly focus on designing and preparing new electrode materials with high adaptability and reversibility during the large sodium ion intercalation / extraction process. Transition metal oxides (TMO) with a layered structure and polyanionic compounds (PAC) are two of the most promising candidate materials for the positive electrode materials of SIBs. The theoretical capacity of PAC materials is slightly lower than that of TMO materials (such as NaMnO2, P2-Na2 / 3Fe 1 / 2 Mn 1 / 2 O2 and vanadium oxides, etc.), but PAC materials usually have more excellent cycle life than TMO materials and can meet the requirements of grid-scale energy storage for ultra-long cycle life. Therefore, if the comprehensive electrochemical performance of PAC materials (including high-rate capability, specific capacity, and working voltage) is improved, their prospects in the application field of SIBs will be very broad.

[0055] To solve the problem of the low performance of traditional sodium ion positive electrode materials, this application provides a high-valence doped mixed sodium iron phosphate positive electrode material for sodium ion batteries and its performance testing method.

[0056] In one embodiment, this application provides a high-valence doped mixed sodium iron phosphate positive electrode material for sodium ion batteries, and its molecular formula is: Na a Fe b-x M x (PO4) c P2O7@C.

[0057] Specifically, the high-valence doped mixed sodium iron phosphate positive electrode material for sodium ion batteries includes a core and a carbon layer coating the core;

[0058] The core includes a mixed sodium iron phosphate material;

[0059] The molecular formula of the mixed sodium iron phosphate material is: Na a Fe b-x M x (PO4) c P2O7;

[0060] where a, b, and c are two sets of related values of 4, 3, 2 and 3, 2, 1 respectively, 0 < x ≤ 0.27; M is at least one of V, Nb, Ta, Ce, and Hf;

[0061] The high-valence doped mixed sodium iron phosphate cathode material for sodium-ion batteries is particles with an average particle size of 1 μm to 10 μm.

[0062] In this embodiment, x represents the stoichiometric ratio, and the values of x can independently be 0.01, 0.05, 0.08, 0.1, 0.15, 0.2, 0.25, 0.27. Preferably, 0 < x ≤ 0.27.

[0063] It can be understood that when the values of a, b, and c are 3, 2, 1 and x = 0, the expression of the mixed sodium iron phosphate cathode material is Na3Fe2PO4P2O7@C, which means that the mixed sodium iron phosphate cathode material does not contain any high-valence doped metals. In the actual production process, the mixed sodium iron phosphate cathode material provided in this application replaces part of the iron with high-valence doped metals to improve the performance of the cathode material and sodium-ion batteries.

[0064] In one of the embodiments, the mixed sodium iron phosphate cathode material is prepared by the following method:

[0065] Step 101, dissolve phosphate, iron salt, sodium salt, M salt, and carbon source in deionized water respectively to obtain a mixed solution; where M is at least one of V, Nb, Ta, Ce, and Hf;

[0066] Step 102, perform a drying treatment on the mixed solution to obtain a powdery mixed precursor;

[0067] Step 103, perform a heat treatment on the powdery mixed precursor in an inert reducing atmosphere to obtain the mixed sodium iron phosphate cathode material.

[0068] Among them, the phosphate is one or a mixture of two or more of sodium dihydrogen phosphate, acid pyrophosphate sodium, ammonium dihydrogen phosphate, sodium phosphate, and sodium pyrophosphate, preferably sodium dihydrogen phosphate;

[0069] The iron salt is one or a mixture of two or more of iron phosphate, iron pyrophosphate, and iron nitrate, preferably iron nitrate;

[0070] The sodium salt is one or a mixture of two or more of sodium pyrophosphate, sodium phosphate, sodium dihydrogen phosphate, and sodium acetate, preferably sodium dihydrogen phosphate;

[0071] The M salt is one or a mixture of two or more of tantalum pentoxide, tantalum pentachloride, potassium tantalate, tantalum hydroxide, tantalum carbide, hafnium dioxide, cerium dioxide, niobium pentachloride, and vanadium pentoxide, preferably tantalum pentachloride;

[0072] The carbon source is one or a mixture of two or more of citric acid, phenolic resin, oxalic acid, and ascorbic acid, preferably citric acid;

[0073] The proportion of the carbon source in the material dissolved in deionized water in step 101 is 1-10 wt.%, preferably 7 wt.%;

[0074] In one embodiment, the negative electrode of the sodium-ion battery uses hard carbon, the electrolyte is a sodium salt solution of 1 mol / L NaClO4, and the solvent of the sodium salt solution is ethylene carbonate and dimethyl carbonate with a volume ratio of 1:1.

[0075] The positive electrode sheet of the sodium-ion battery is prepared by the following method:

[0076] Step 201, weigh and mix the mixed sodium iron phosphate positive electrode material, super-P, and binder according to the mass ratio of 8:1:1 to obtain a mixed material;

[0077] Step 202, dissolve the mixed material in N-methylpyrrolidone to obtain a solution;

[0078] Step 203, coat the solution on the aluminum foil, and place it in a vacuum drying oven at 100 °C for drying and heat preservation for 10 h to obtain the positive electrode sheet of the sodium-ion battery.

[0079] Based on the above preparation method of the mixed sodium iron phosphate positive electrode material, the following results are obtained by using the following multiple examples, comparative examples, and test examples to prepare the mixed sodium iron phosphate positive electrode material:

[0080] Example 1

[0081] Take 4 mol of sodium dihydrogen phosphate, 2.91 mol of iron(III) nitrate nonahydrate, 0.09 mol of tantalum pentachloride, and 4 mol of citric acid and dissolve them in deionized water. After spray drying, a precursor powder is obtained. Heat the precursor powder in a tube furnace to 300 °C and keep it for 6 hours, then cool it with the furnace and heat it to 550 °C and keep it for 8 hours. Protect it with a mixed gas of argon and hydrogen throughout the process, with a heating rate of 2 °C / min. After cooling with the furnace, the required Na4Fe 2.91 Ta 0.09 (PO4)2P2O7@C positive electrode composite material.

[0082] Example 2

[0083] Dissolve 4 mol of sodium dihydrogen phosphate, 2.88 mol of iron(III) nitrate nonahydrate, 0.09 mol of tantalum pentachloride, 0.015 mol of vanadium pentoxide, and 4 mol of citric acid in deionized water. After spray drying, a precursor powder is obtained. Heat the precursor powder in a tubular furnace at a rate of 2 °C / min to 300 °C and hold for 6 hours. After cooling with the furnace, heat it again at a rate of 2 °C / min to 550 °C and hold for 8 hours. Protect the whole process with a mixed gas of argon and hydrogen. After cooling with the furnace, the required Na4Fe 2.91 Ta 0.09 V 0.03 (PO4)2P2O7@C cathode composite material is obtained.

[0084] Example 3

[0085] Dissolve 4 mol of sodium dihydrogen phosphate, 2.88 mol of iron(III) nitrate nonahydrate, 0.09 mol of tantalum pentachloride, 0.03 mol of zirconium oxide, and 4 mol of citric acid in deionized water. After spray drying, a precursor powder is obtained. Heat the precursor powder in a tubular furnace to 300 °C and hold for 6 hours. After cooling with the furnace, heat it again to 550 °C and hold for 8 hours. Protect the whole process with a mixed gas of argon and hydrogen. The heating rate is 2 °C / min. After cooling with the furnace, the required Na4Fe 2.91 Ta 0.09 Zr 0.03 (PO4)2P2O7@C cathode composite material is obtained.

[0086] Example 4

[0087] Dissolve 4 mol of sodium dihydrogen phosphate, 2.88 mol of iron(III) nitrate nonahydrate, 0.09 mol of tantalum pentachloride, 0.03 mol of niobium pentachloride, and 4 mol of citric acid in deionized water. After spray drying, a precursor powder is obtained. Heat the precursor powder in a tubular furnace to 300 °C and hold for 6 hours. After cooling with the furnace, heat it again to 550 °C and hold for 8 hours. Protect the whole process with a mixed gas of argon and hydrogen. The heating rate is 2 °C / min. After cooling with the furnace, the required Na4Fe 2.91 Ta 0.09 Nb 0.03 (PO4)2P2O7@C cathode composite material is obtained.

[0088] Example 5

[0089] Dissolve 4 mol of sodium dihydrogen phosphate, 2.88 mol of ferric nitrate nonahydrate, 0.09 mol of tantalum pentachloride, 0.03 mol of cerium dioxide, and 4 mol of citric acid in deionized water. After spray drying, a precursor powder is obtained. Heat the precursor powder in a tube furnace to 300 °C and hold for 6 hours, then cool with the furnace and heat again to 550 °C and hold for 8 hours. Protect with a mixed gas of argon and hydrogen throughout the process, with a heating rate of 2 °C / min. After cooling with the furnace, the required Na4Fe 2.91 Ta 0.09 Ce 0.03 (PO4)2P2O7@C cathode composite material.

[0090] Example 6

[0091] Dissolve 4 mol of sodium dihydrogen phosphate, 2.88 mol of ferric nitrate nonahydrate, 0.09 mol of tantalum pentachloride, 0.03 mol of hafnium dioxide, and 4 mol of citric acid in deionized water. After spray drying, a precursor powder is obtained. Heat the precursor powder in a tube furnace to 300 °C and hold for 6 hours, then cool with the furnace and heat again to 550 °C and hold for 8 hours. Protect with a mixed gas of argon and hydrogen throughout the process, with a heating rate of 2 °C / min. After cooling with the furnace, the required Na4Fe 2.91 Ta 0.09 Hf 0.03 (PO4)2P2O7@C cathode composite material.

[0092] Example 7

[0093] Dissolve 4 mol of sodium dihydrogen phosphate, 2.88 mol of ferric nitrate nonahydrate, 0.09 mol of tantalum pentachloride, 0.015 mol of vanadium pentoxide in deionized water. After spray drying, a precursor powder is obtained. Heat the precursor powder in a tube furnace to 300 °C and hold for 6 hours, then cool with the furnace and heat again to 550 °C and hold for 8 hours. Protect with a mixed gas of argon and hydrogen throughout the process, with a heating rate of 2 °C / min. After cooling with the furnace, the required Na4Fe 2.91 Ta 0.09 V 0.03 (PO4)2P2O7 cathode composite material.

[0094] Comparative Example 1

[0095] Dissolve 4 mol of sodium dihydrogen phosphate, 2.88 mol of ferric nitrate nonahydrate, 0.09 mol of tantalum pentachloride, 0.015 mol of vanadium pentoxide, and 4 mol of citric acid in deionized water. After spray drying, a precursor powder is obtained. Heat the precursor powder in a tube furnace at a rate of 10 °C / min to 200 °C and hold for 6 hours, then cool with the furnace and heat again at a rate of 5 °C / min to 400 °C and hold for 8 hours. Protect with a mixed gas of argon and hydrogen throughout the process. After cooling with the furnace, the required Na4Fe2.91 Ta 0.09 V 0.03 (PO4)2P2O7@C-I composite cathode material.

[0096] Comparative Example 2

[0097] Dissolve 4 mol of sodium dihydrogen phosphate, 2.88 mol of ferric nitrate nonahydrate, 0.09 mol of tantalum pentachloride, 0.015 mol of vanadium pentoxide, and 4 mol of citric acid in deionized water. After spray drying, the precursor powder is obtained. The precursor powder is heated in a tube furnace at a rate of 10 °C / min to 200 °C and held for 6 hours, then cooled with the furnace and heated again at a rate of 5 °C / min to 600 °C and held for 8 hours. The whole process is protected by a mixed gas of argon and hydrogen. After cooling with the furnace, the required Na4Fe 2.91 Ta 0.09 V 0.03 (PO4)2P2O7@C-II composite cathode material.

[0098] Comparative Example 3

[0099] Dissolve 4 mol of sodium dihydrogen phosphate, 2.88 mol of ferric nitrate nonahydrate, 0.09 mol of tantalum pentachloride, 0.015 mol of vanadium pentoxide, and 4 mol of citric acid in deionized water. After spray drying, the precursor powder is obtained. The precursor powder is heated in a tube furnace at a rate of 10 °C / min to 400 °C and held for 6 hours, then cooled with the furnace and heated again at a rate of 5 °C / min to 700 °C and held for 8 hours. The whole process is protected by a mixed gas of argon and hydrogen. After cooling with the furnace, the required Na4Fe 2.91 Ta 0.09 V 0.03 (PO4)2P2O7@C-III composite cathode material.

[0100] The comparison results of the mixed sodium iron phosphate cathode materials prepared in each example and comparative example and their corresponding 1C gram capacities are shown in Table 1:

[0101] Table 1: Performance tests of Examples 1 to 7 and Comparative Examples 1 to 3

[0102]

[0103] Test Example 1

[0104] Perform scanning electron microscopy observation (FESEM, JSM-6700F) on the Na4Fe 2.91 Ta 0.09 V 0.03 (PO4)2P2O7@C composite cathode material prepared in Example 2, and the specific image is as Figure 1 shown.

[0105] Based on a preparation method of a mixed sodium iron phosphate cathode material provided by this application, the prepared Na4Fe 2.91 Ta 0.09 V 0.03 (PO4)2P2O7@C composite cathode material as a whole shows spherical particles with particle sizes between 1 and 10 μm. Figure 1 It shows that this preparation method can synthesize a high-valence doped mixed sodium iron phosphate cathode material with smaller size and uniform distribution.

[0106] Test Example 2

[0107] The Na4Fe 2.91 Ta 0.09 V 0.03 (PO4)2P2O7@C cathode composite material prepared in Example 2 was tested by X-ray diffraction (SHIMADZU XRD-7000). The experimental conditions are as follows: copper target (λ = 0.1518 nm), 2θ angle range is 5 - 60°, and the XRD pattern is as Figure 2 shown.

[0108] From Figure 3 the XRD pattern in, it can be seen that Na4Fe 2.91 Ta 0.09 V 0.03 (PO4)2P2O7@C cathode composite material has a pure phase crystal structure of Pn21a space group with high crystallinity (PDF standard card number: PDF#89-0579). No obvious impurity diffraction peaks were observed, indicating that the addition of a small amount of tantalum and vanadium elements did not affect the crystal structure of this material. This result shows that this experimental method can prepare a high-valence doped mixed sodium iron phosphate cathode material with pure phase and high crystallinity.

[0109] Test Example 3

[0110] The button cell prepared in Example 2 was tested for charge and discharge. The current of 1C is 129 mA / g, and the charge and discharge temperature is room temperature. As Figure 3 shown, Figure 4 is the charge and discharge curve of the button cell assembled in Example 2. The charge and discharge current is 1C, and the charge and discharge voltage range is 1.8 - 3.8V. During the whole charge and discharge process, the charge and discharge curve is smooth and complete, indicating that the battery can be charged and discharged well; the charge specific capacity is 105 mAh / g, and the discharge specific capacity is 102 mAh / g. The above data shows that this high-valence doped mixed sodium iron phosphate cathode material has a high discharge specific capacity.

[0111] As Figure 4 shown, Figure 41C cycling curve of the button battery assembled in Example 2. The charge-discharge voltage range is 1.8 - 3.8V. Before the formal 1C cycle, it is first activated with a small current of 0.1C for 3 cycles. It can be seen from the figure that after 1000 cycles, the capacity retention rate is 93.7%.

[0112] Through the above method, the high-valence doped mixed sodium iron phosphate cathode material of the present invention mixes and dissolves phosphate, iron salt, sodium salt, tantalum salt, and salt A in a certain stoichiometric ratio, and uses spray drying to prepare a precursor. The obtained precursor is heat-treated in a reducing atmosphere to finally obtain a high-valence doped mixed sodium iron phosphate cathode material. The obtained nanomaterial has good electrochemical behavior and is used as the cathode of a rechargeable sodium-ion battery.

[0113] In the process of preparing the material of the present invention, tantalum element is added. This method not only improves the rate performance and discharge specific capacity of the material, but also finds that doping some other elements on the basis of tantalum and iron has an impact on the cycle stability of the material. This discovery provides an idea for improving the material performance.

[0114] As Figure 5 shown, a performance test method for a high-valence doped mixed sodium iron phosphate cathode material of a sodium-ion battery provided by this application may include the following steps:

[0115] Step 101, prepare a sodium-ion battery, and perform the first charge-discharge cycle at the first charge-discharge rate of the sodium-ion battery, and record the battery capacity of the sodium-ion battery as Q1, which is used as the reference capacity; wherein, the positive electrode sheet of the sodium-ion battery uses a mixed sodium iron phosphate cathode material.

[0116] Specifically, the mixed sodium iron phosphate cathode material can be evenly coated on the current collector to prepare an electrode sheet. Subsequently, after the electrode sheet is dried and pressed, it is used as the positive electrode sheet to assemble a sodium-ion battery. The sodium-ion battery includes components such as a positive electrode, a negative electrode, a separator, and an electrolyte. After the sodium-ion battery is assembled, the first charge-discharge cycle test is carried out. During the test process, the first charge-discharge rate is used to activate the active substances inside the battery and evaluate its electrochemical performance. After the first charge-discharge cycle is completed, record the battery capacity of the sodium-ion battery as Q1. This capacity value will be used as the reference capacity for subsequent tests and evaluations, and is used to compare and analyze the performance of the battery under different conditions.

[0117] In one of the embodiments, the negative electrode of the sodium-ion battery uses hard carbon, and the electrolyte is a sodium salt solution of 1mol / L NaClO4. The solvent of the sodium salt solution is a mixed solution of ethylene carbonate and dimethyl carbonate with a volume ratio of 1:1. Optionally, an additive of 5% fluoroethylene carbonate is additionally added to the electrolyte.

[0118] Among them, the charge-discharge rate (C-rate) is an index used to measure the charge-discharge speed of a battery. It represents the current value required for the battery to discharge its rated capacity within a specified time, or the current value required to fully charge the battery within a specified time. 1C represents the current intensity at which the battery discharges or is fully charged with its rated capacity in 1 hour. For example, for a battery with a capacity of 10 Ah (ampere-hour), the charging current of 1C is 10A, which means that when charging with a current of 10A, the battery can be fully charged in 1 hour; the discharging current of 1C is also 10A, and when discharging with a current of 10A, the rated capacity of the battery can be discharged in 1 hour.

[0119] Step 102, after the first charge-discharge cycle ends, perform 100 to 1000 charge-discharge acceleration cycles at the second charge-discharge rate to accelerate the battery aging process, where the second charge-discharge rate is greater than the first charge-discharge rate;

[0120] After the charge-discharge acceleration cycle ends, return to the first charge-discharge rate to perform charge-discharge cycles again, and record the current battery capacity Q2.

[0121] Specifically, through the charge-discharge cycle experiment in the laboratory, the charge-discharge cycle of the battery in actual use can be simulated to test the battery life of the sodium-ion battery in actual use. In actual use, users may charge and discharge the sodium-ion battery at a high rate greater than the first charge-discharge rate. Among them, high-rate charging can shorten the charging time, but may cause problems such as battery heating and polarization, affecting the battery life and safety. For example, when quickly charging an electric vehicle, if the charging rate is too high, the chemical reaction inside the battery may be too intense, causing the battery temperature to rise rapidly, and this will accelerate the battery aging in the long run.

[0122] Therefore, in the performance test method, the second charge-discharge rate is set to be greater than the first charge-discharge rate to test the service life of the battery. In one embodiment, the first charge-discharge rate is 1C, and the second charge-discharge rate is 2C to 100C. Exemplarily, during the electrochemical performance test, the current of 1C is 129 mA / g, and the charge-discharge temperature is room temperature. Preferably, in the performance test method provided in this application, the second charge-discharge rate is 100C.

[0123] Step 103, repeat Step 102 until the battery capacity Qn of the nth charge-discharge cycle at the first charge-discharge rate is less than or equal to the preset battery capacity threshold, where the preset battery capacity threshold is 80% of the reference capacity.

[0124] Specifically, after multiple repeated cycles, the performance of the sodium-ion battery gradually degrades during charge and discharge. Among them, when the battery capacity of the sodium-ion battery drops to a preset battery capacity threshold, it can indicate the end of the test, and the recorded data can be used to predict the service life of the sodium-ion battery.

[0125] Step 104, calculate the average battery capacity during the test according to the battery capacities Q1, Q2, ……, Qn recorded in each cycle;

[0126] According to the battery capacities recorded in each cycle and the average battery capacity, analyze the change trend of the battery capacity with the number of cycles through statistical methods to obtain the life prediction values of the sodium-ion battery under different charge and discharge conditions.

[0127] Specifically, according to the battery capacities Q1, Q2, ……, Qn recorded in each cycle, the change trend of the battery capacity with the number of cycles can be analyzed through statistical methods such as establishing a mathematical model, so as to predict the predicted life of the sodium-ion battery.

[0128] Among them, the predicted life can be the cycle life of the sodium-ion battery. The cycle life of the battery refers to the number of charge and discharge cycles that the battery can experience before the battery capacity drops to a certain specified value (generally a certain percentage of the initial capacity, such as 80%) under a certain charge and discharge regime. For example, for a sodium-ion battery, discharging from a fully charged state to a set cut-off voltage and then charging back to the fully charged state is considered a complete charge and discharge cycle. If after multiple such cycles, the battery capacity drops to 80% of the initial capacity, then the number of cycles experienced before is the cycle life of the battery. In other examples, indicators such as the expected available duration can also be used as the predicted life of the sodium-ion battery, and this application does not make any limitations in this regard.

[0129] A performance test method provided by an embodiment of the present application uses a mixed sodium iron phosphate cathode material to prepare the cathode electrode sheet of a sodium-ion battery, records the battery capacity after cyclic charge and discharge of the sodium-ion battery under different charge and discharge conditions, analyzes the change trend of the battery capacity with the number of cycles through statistical methods, and obtains the life prediction values of the sodium-ion battery under different charge and discharge conditions, which can improve the test accuracy and thus help improve the performance of the sodium-ion battery.

[0130] In one of the embodiments, according to the battery capacities recorded in each cycle and the average battery capacity, analyzing the change trend of the battery capacity with the number of cycles through statistical methods to obtain the life prediction values of the sodium-ion battery under different charge and discharge conditions may include the following steps:

[0131] Step 601, construct a linear regression model corresponding to the battery capacity according to the battery capacities recorded in each cycle and the average battery capacity; the expression of the linear regression model is:

[0132] Y = β0 + β1X + ε

[0133] Wherein, Y represents the battery capacity, X represents the number of cycles, β0 is the intercept, β1 is the slope, and ε is the error term;

[0134] Step 602, based on the preset charge-discharge conditions and the preset minimum battery capacity, determine the number of cycles corresponding to the battery capacity in the linear regression model, and determine the life prediction value of the sodium-ion battery according to the number of cycles; wherein, the life prediction value is the duration when the battery capacity of the sodium-ion battery reaches the preset minimum battery capacity.

[0135] Specifically, when there is a linear relationship between the life prediction value of the battery and the number of charge-discharge cycles, the linear regression model can be used to predict the life prediction value of the sodium-ion battery. Among them, when the relationship between two variables is a linear function relationship, and the image drawn according to their relationship is a straight line, the relationship between the two variables can be called linear.

[0136] Exemplarily, a linear regression model corresponding to the battery capacity can be constructed by recording the battery capacity and the average battery capacity in each cycle under different charge-discharge conditions. Through this linear regression model, the number of charge-discharge cycles for each cycle can correspond to a battery capacity value. Further, based on the preset charge-discharge conditions and the preset minimum battery capacity, determine the number of cycles corresponding to the battery capacity in the linear regression model, and determine the life prediction value of the sodium-ion battery according to the number of cycles. The life prediction value can be the corresponding number of cycles or the duration when the battery capacity of the sodium-ion battery reaches the preset minimum battery capacity.

[0137] In one embodiment, according to the battery capacity and the average battery capacity recorded in each cycle, analyze the change trend of the battery capacity with the number of cycles by statistical methods to obtain the life prediction value of the sodium-ion battery under different charge-discharge conditions, including:

[0138] Use non-linear SVM to predict the life prediction value of the sodium-ion battery according to the battery capacity and the average battery capacity recorded in each cycle.

[0139] Specifically, when the relationship between the battery capacity and the life prediction value is non-linear, non-linear SVM (Support Vector Machine) can be used to predict the life prediction value of the sodium-ion battery. Among them, when the relationship between two variables is not a linear function relationship, and the image drawn according to their relationship is not a straight line, the relationship between the two variables can be called non-linear.

[0140] Support Vector Machine (SVM) is a supervised learning model widely used in classification and regression analysis. SVM is particularly suitable for high-dimensional data and performs excellently in dealing with complex non-linear data. Specifically, the goal of SVM is to find an optimal decision boundary (or hyperplane) to maximize the separation of data points of different classes with impedance value Z1. For linearly separable data, SVM classifies through a linear hyperplane; for linearly inseparable data, SVM can map the data to a high-dimensional space through the kernel method (Kernel Trick) to make it linearly separable in the high-dimensional space.

[0141] Optionally, the feasible expression of the Support Vector Machine is as follows:

[0142]

[0143]

[0144] In the formula, L max (α) is the objective optimization function term of the Support Vector Machine, α is the Lagrange multiplier and α i ∈[0, C] and where C is the regularization parameter, is the feature vector, y i , y j is the class label corresponding to the feature vector, is the kernel function, is the normal vector of the hyperplane, b is the bias term of the hyperplane, is the feature vector to the distance from the hyperplane.

[0145] Furthermore, the feasible expression of the optional kernel function of the Support Vector Machine can be but not limited to the following:

[0146]

[0147] In the formula, is the Gaussian kernel function, is the Sigmoid kernel function, U is the slope coefficient, and V is the intercept coefficient.

[0148] A performance testing method provided by an embodiment of the present application uses a linear regression model or non-linear statistics to analyze the relationship between battery capacity and the number of charge and discharge cycles. When the relationship between battery capacity and the number of charge and discharge cycles is non-linear, non-linear SVM is used to process and predict the data. Through the kernel method, SVM can effectively process non-linear data; and SVM has strong robustness to some noise and outliers.

[0149] In one embodiment, the present application uses the following test examples to test the predicted battery life of the mixed sodium iron phosphate cathode material and the prepared sodium-ion battery:

[0150] Test Example 1

[0151] a) Prepare a sodium-ion battery, with its cathode using the high-valence doped mixed sodium iron phosphate cathode material as described above, the anode using hard carbon, and the electrolyte being a sodium salt solution of 1 mol / L NaClO4 (the solvent is ethylene carbonate and dimethyl carbonate with a volume ratio of 1:1). Perform the first charge-discharge cycle at the first charge-discharge rate, and record the battery capacity as Q1, which serves as the reference capacity.

[0152] b) After the first charge-discharge cycle ends, perform 200 charge-discharge accelerated cycles at the second charge-discharge rate to accelerate the battery aging process, where the second charge-discharge rate is greater than the first charge-discharge rate. The first charge-discharge rate is 1C, and the second charge-discharge rate is 3C.

[0153] c) After the charge-discharge accelerated cycle ends, return to the first charge-discharge rate to perform the charge-discharge cycle again, and record the battery capacity.

[0154] d) Repeat the processes of steps b) and c) until the battery capacity of a certain charge-discharge cycle at the first charge-discharge rate is less than or equal to the preset battery capacity threshold, and the preset battery capacity threshold is 80% of the reference capacity; during the entire test process, record the battery capacity of each cycle, and calculate the total battery capacity and the average battery capacity.

[0155] e) According to the recorded battery capacity of each cycle and the average battery capacity, analyze the change trend of the battery capacity with the number of cycles through statistical methods to predict the battery life under different charge-discharge conditions; the mathematical models involved include but are not limited to the following specific algorithm models:

[0156] - Linear regression model:

[0157] Y = β0 + β1X + ε

[0158] Where Y represents the battery capacity, X represents the number of cycles, β0 is the intercept, β1 is the slope, and ε is the error term.

[0159] - Nonlinear regression model:

[0160] Y = α + β·e n + ε

[0161] Where α and β are model parameters, n is the nonlinear exponent, and ε is the error term; these models can be used to describe the non-linear change of the battery capacity with the number of cycles.

[0162] - Machine learning algorithms: including but not limited to support vector machines (SVM), random forests, gradient boosting machines (GBM), or neural networks. These algorithms can handle more complex non-linear relationships and learn the patterns of battery capacity decay through training datasets, thereby improving the accuracy and robustness of life prediction;

[0163] Calculate the battery life of the sodium-ion battery.

[0164] Test Example 2

[0165] a) Prepare a sodium-ion battery. Its positive electrode uses the high-valence doped mixed sodium iron phosphate positive electrode material as described above, the negative electrode uses hard carbon, and the electrolyte is a sodium salt solution of 1 mol / L NaClO4 (the solvent is ethylene carbonate and dimethyl carbonate with a volume ratio of 1:1). Conduct the first charge-discharge cycle at the first charge-discharge rate and record the battery capacity as Q1, which serves as the reference capacity;

[0166] b) After the first charge-discharge cycle ends, conduct 500 charge-discharge accelerated cycles at the second charge-discharge rate to accelerate the battery aging process, where the second charge-discharge rate is greater than the first charge-discharge rate. The first charge-discharge rate is 0.5C, and the second charge-discharge rate is 5C;

[0167] c) After the charge-discharge accelerated cycle ends, return to the first charge-discharge rate again to conduct charge-discharge cycles and record the battery capacity;

[0168] d) Repeat the processes of steps b) and c) until the battery capacity of a certain charge-discharge cycle at the first charge-discharge rate is less than or equal to the preset battery capacity threshold, and the preset battery capacity threshold is 80% of the reference capacity; during the entire test process, record the battery capacity of each cycle and calculate the total battery capacity and the average battery capacity;

[0169] e) According to the recorded battery capacity of each cycle and the average battery capacity, analyze the change trend of the battery capacity with the number of cycles through mathematical model statistical methods to predict the life of the battery under different charge-discharge conditions; the mathematical models involved include but are not limited to the following specific algorithm models:

[0170] - Linear regression model:

[0171] Y = β0 + β1X + ε

[0172] Where Y represents the battery capacity, X represents the number of cycles, β0 is the intercept, β1 is the slope, and ε is the error term;

[0173] - Non-linear regression model:

[0174] Y = α + β·e n + ε

[0175] where α and β are model parameters, n is a non - linear exponent, and ε is an error term; these models can be used to describe the non - linear variation of battery capacity with the number of cycles;

[0176] - Machine learning algorithms: including but not limited to support vector machine (SVM), random forest, gradient boosting machine (GBM), or neural networks. These algorithms can handle more complex non - linear relationships and learn the pattern of battery capacity decay through the training data set, so as to improve the accuracy and robustness of life prediction;

[0177] The battery life of the sodium - ion battery is calculated.

[0178] A high - valence - doped mixed sodium iron phosphate cathode material for sodium - ion batteries and its performance testing method provided by this application. By adding high - valence metal elements such as tantalum element into the sodium iron phosphate material to partially replace the iron element sites, a high - valence - doped mixed sodium iron phosphate cathode material for sodium - ion batteries is prepared. This method and the obtained high - valence - doped mixed sodium iron phosphate cathode material for sodium - ion batteries improve performance parameters such as the rate performance and discharge specific capacity of the material. Further, the battery life of the sodium - ion battery prepared from the high - valence - doped mixed sodium iron phosphate cathode material for sodium - ion batteries is predicted by using support vector machine. When the pattern of battery capacity decay is relatively simple and the data is linearly separable, the linearly separable SVM can find an optimal hyperplane to accurately classify the data. This helps to make accurate life predictions when there is a clear linear relationship between data features and target variables. When the relationship between the statistical battery capacity and the number of charge - discharge cycles is non - linear, the non - linear SVM is used to process and predict the data. Through the kernel method, the SVM can effectively handle non - linear data; and the SVM has strong robustness to some noise and outliers.

[0179] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown sequentially according to the indications of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0180] The above-described embodiments only represent several implementation manners of the embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the embodiments of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A high-valence doped mixed sodium iron phosphate cathode material for sodium-ion batteries, characterized in that, It includes a core and a carbon layer coating the core; The core includes a mixed sodium iron phosphate material; The molecular formula of the mixed sodium iron phosphate material is: Na a Fe b-x M x (PO4) c P2O7; where a, b, and c are two sets of associated values, which are 4, 3, 2 and 3, 2, 1 respectively, 0 < x ≤ 0.27; M is at least one of V, Nb, Ta, Ce, and Hf; The high-valence doped mixed sodium iron phosphate cathode material for the sodium-ion battery is particles with an average particle size of 1 μm to 10 μm.

2. The high-valence doped mixed sodium iron phosphate cathode material for sodium ion battery according to claim 1, characterized in that The high-valence doped mixed sodium iron phosphate cathode material for the sodium-ion battery is prepared by the following method: Dissolve phosphate, iron salt, sodium salt, M salt, and carbon source in deionized water respectively to obtain a mixed solution; where M is at least one of V, Nb, Ta, Ce, and Hf; Perform a drying treatment on the mixed solution to obtain a powdery mixed precursor; Perform a heat treatment on the powdery mixed precursor in an inert reducing atmosphere to obtain the mixed sodium iron phosphate cathode material.

3. The high-valence doped mixed sodium iron phosphate cathode material for the sodium-ion battery according to claim 2, wherein: The phosphate is one or a mixture of two or more of sodium dihydrogen phosphate, acid sodium pyrophosphate, ammonium dihydrogen phosphate, sodium phosphate, and sodium pyrophosphate; The iron salt is one or a mixture of two or more of iron phosphate, ferric pyrophosphate, and ferric nitrate; The sodium salt is one or a mixture of two or more of sodium pyrophosphate, sodium phosphate, sodium dihydrogen phosphate, and sodium acetate; The M salt is one or a mixture of two or more of tantalum pentoxide, tantalum pentachloride, potassium tantalate, tantalum hydroxide, tantalum carbide, hafnium dioxide, cerium dioxide, niobium pentachloride, and vanadium pentoxide; The carbon source is one or a mixture of two or more of citric acid, phenolic resin, oxalic acid, and ascorbic acid; the carbon source accounts for 1 to 10 wt.% in the materials dissolved in deionized water.

4. A performance testing method, characterized in that, It includes the following steps: Step 101, prepare a sodium-ion battery, and perform the first charge-discharge cycle at the first charge-discharge rate of the sodium-ion battery, and record the battery capacity of the sodium-ion battery as Q1, which is used as the reference capacity; wherein, the positive electrode plate of the sodium-ion battery uses the high-valence doped mixed sodium iron phosphate cathode material according to any one of claims 1-3; Step 102, after the first charge-discharge cycle ends, perform 100 to 1000 charge-discharge acceleration cycles at the second charge-discharge rate to accelerate the battery aging process, wherein the second charge-discharge rate is greater than the first charge-discharge rate; After the charge-discharge acceleration cycle ends, return to the first charge-discharge rate again to perform the charge-discharge cycle, and record the current battery capacity Q2; Step 103, repeat Step 102 until the battery capacity Qn of the nth charge-discharge cycle at the first charge-discharge rate is less than or equal to the preset battery capacity threshold, wherein the preset battery capacity threshold is 80% of the reference capacity; Step 104, calculate the average battery capacity during the test according to the battery capacities Q1, Q2,..., Qn recorded in each cycle; According to the battery capacity recorded in each cycle and the average battery capacity, analyze the change trend of the battery capacity with the number of cycles by statistical methods to obtain the life prediction value of the sodium-ion battery under different charge-discharge conditions.

5. The method according to claim 4, wherein The negative electrode of the sodium-ion battery uses hard carbon, and the electrolyte is a sodium salt solution of 1 mol / L NaClO4. The solvent of the sodium salt solution is ethylene carbonate and dimethyl carbonate with a volume ratio of 1:

1.

6. The method according to claim 4, characterized in that, The positive electrode plate of the sodium-ion battery is prepared by the following method: Weigh and mix the high-valence doped mixed sodium iron phosphate cathode material, super-P, and binder of the sodium-ion battery according to the mass ratio of 8:1:1 to obtain a mixed material; Dissolve the mixed material in N-methylpyrrolidone to obtain a solution; Apply the solution on the aluminum foil, and place it in a vacuum drying oven at 100 °C for drying and heat preservation for 10 h to obtain the positive electrode plate of the sodium-ion battery.

7. According to the method described in claim 4, wherein: The first charge-discharge rate is 1C, and the second charge-discharge rate is 2C to 100C.

8. The method according to claim 7, characterized in that: The current 1C is 129 mA / g.

9. The method according to claim 4, characterized in that The method of analyzing the change trend of the battery capacity with the number of cycles by statistical methods according to the battery capacity recorded in each cycle and the average battery capacity to obtain the life prediction value of the sodium-ion battery under different charge-discharge conditions includes: Construct a linear regression model corresponding to the battery capacity according to the battery capacity recorded in each cycle and the average battery capacity; the expression of the linear regression model is: Y = β0 + β1X + ε Wherein, Y represents the battery capacity, X represents the number of cycles, β0 is the intercept, β1 is the slope, and ε is the error term; Based on the preset charge-discharge conditions and the preset minimum battery capacity, determine the number of cycles corresponding to the battery capacity in the linear regression model, and determine the life prediction value of the sodium-ion battery according to the number of cycles; wherein, the life prediction value is the duration when the battery capacity of the sodium-ion battery reaches the preset minimum battery capacity.

10. The method according to claim 4, characterized in that, The method of analyzing the change trend of the battery capacity with the number of cycles by statistical methods according to the battery capacity recorded in each cycle and the average battery capacity to obtain the life prediction value of the sodium-ion battery under different charge-discharge conditions includes: Use non-linear SVM to predict the life prediction value of the sodium-ion battery according to the battery capacity recorded in each cycle and the average battery capacity.

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