A sodium ion battery positive electrode material, a screening method thereof, and a sodium ion battery

Abstract

The application provides a sodium ion battery positive electrode material and a screening method thereof, and a sodium ion battery. The screening method comprises the following steps: based on an optimized molar amount a of Na2CO3, obtaining at least six groups of preset molar amounts b0, c0, d0 and e0 of a divalent nickel salt, a divalent manganese salt, a divalent copper salt and a divalent lanthanum salt and corresponding first discharge voltage and first circle discharge specific capacity of a sodium ion battery; inputting the above data into a Bayesian optimization model for optimization training and optimization iteration to obtain optimized molar amounts b 优 、c 优 、d 优 、e 优 of the divalent nickel salt, the divalent manganese salt, the divalent copper salt and the divalent lanthanum salt; and based on the optimized molar amount a and the optimized molar amounts b 优 、c 优 、d 优 、e 优 , a sodium ion battery positive electrode material is prepared. The application screens the molar amount of raw materials of the sodium ion battery positive electrode material by using the Bayesian optimization model, and compared with a traditional trial-and-error search method, the efficiency is high and the effect is good, and finally a sodium ion battery positive electrode material with very good performance can be obtained.

Description

Technical Field

[0001] The present invention relates to, in particular, a sodium ion battery positive electrode material and a screening method thereof, and a sodium ion battery. Background Art

[0002] The rapid development of electric vehicles, photovoltaics, and wind power has created a huge demand for cost-effective electrochemical energy storage. Sodium-ion batteries have attracted widespread attention due to their high volumetric energy density and low cost, and have the potential to replace lithium-ion batteries in the future. However, the energy density and life of sodium-ion batteries are not competitive. 1 / 3 Mn 2 / 3 Typical nickel-manganese-based cathodes composed of nickel-manganese oxide (NiO2) can provide excellent discharge specific capacity due to the outstanding theoretical capacity and conductivity of nickel-manganese oxide. However, unmodified binary metal oxides generally have unsatisfactory cycling stability, especially at specific high specific capacities (close to the theoretical discharge specific capacity), which can lead to lattice expansion and capacity decay.

[0003] Nickel-manganese oxides can be doped with various elements to alleviate strain distribution and mitigate lattice and interlayer expansion issues. However, the complexity of the doping element ratios makes the synthesized materials diverse. Traditional trial-and-error search methods are time-consuming and labor-intensive.

[0004] Lanthanum (La) can be used as a doping element in sodium-ion batteries. Studies have shown that La doping can expand and stabilize the spacing between (de)intercalation layers of alkali metal ions, preventing redox reactions associated with La ions from occurring during charge and discharge. This can improve the material's ionic conductivity and stability, increase the discharge specific capacity, and inhibit capacity decay during cycling. However, there are currently few reports on the application of La in sodium-ion battery cathode materials.

[0005] The contents of the background technology section are merely the technologies known to the inventors and do not necessarily represent the existing technologies in this field. Summary of the Invention

[0006] In response to the problems existing in the prior art, the first aspect of the present application provides a method for screening a positive electrode material for a sodium ion battery. The raw materials for preparing the positive electrode material for a sodium ion battery include Na2CO3, a divalent nickel salt, a divalent manganese salt, a divalent copper salt, and a divalent lanthanum salt. The screening method comprises:

[0007] S1: Determine the optimal molar dosage a of Na2CO3;

[0008] S2: Based on the optimized molar dosage a, obtaining at least six groups of preset molar dosages b0, c0, d0, e0 of divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt, as well as the first discharge voltage of the preset sodium ion battery and the first cycle discharge specific capacity of the preset sodium ion battery; wherein the positive electrode material of the preset sodium ion battery is prepared by the optimized molar dosage a of Na2CO3 and the at least six groups of preset molar dosages b0, c0, d0, e0 of divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt;

[0009] S3: Using the at least six groups of preset molar dosages b0, c0, d0, and e0 as training data, the first discharge voltage of the preset sodium ion battery as an input variable, and the first cycle discharge specific capacity of the preset sodium ion battery as an output variable, the Bayesian optimization model is trained and data iterated to obtain at least three groups of first iteration molar dosages b1, c1, d1, and e1;

[0010] S4: Determine the first optimal molar dosage b based on the first cycle discharge specific capacity of the sodium ion battery corresponding to the at least three groups of first iterative molar dosages b1, c1, d1, and e1. 1优 、c 1优 d 1优 、e 1优 ;

[0011] S5: Based on the first preferred molar amount b 1优 、c 1优 d 1优 、e 1优 The Bayesian optimization model is optimized and iterated using the first discharge voltage of the corresponding sodium ion battery as the input variable and the first cycle discharge capacity of the corresponding sodium ion battery as the output variable to obtain the optimized molar amount b of divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt. 优 、c 优 d 优 、e 优 ;as well as

[0012] S6: Based on the optimized molar dosage a and the optimized molar dosage b 优 、c 优 d 优 、e 优 , prepare positive electrode materials for sodium ion batteries.

[0013] In some embodiments of the present application, step S5 includes:

[0014] S51: the first preferred molar amount b 1优 、c 1优 d 1优 、e 1优As training data, the first discharge voltage of the first sodium ion battery as an input variable, and the first cycle discharge specific capacity of the first sodium ion battery as an output variable, the Bayesian optimization model is optimized and iterated to obtain at least three groups of second iteration molar dosages b2, c2, d2, and e2; wherein the positive electrode material of the first sodium ion battery is Na2CO3 of the optimized molar dosage a and the first preferred molar dosage b 1优 、c 1优 d 1优 、e 1优 It is prepared from divalent nickel salts, divalent manganese salts, divalent copper salts and divalent lanthanum salts;

[0015] S52: Determine whether the first cycle discharge specific capacity of the first sodium ion battery is less than the first cycle discharge specific capacity of the corresponding sodium ion batteries of the at least three groups of second iterative molar dosages b2, c2, d2, and e2;

[0016] S53: If not, use the first preferred molar amount b 1优 、c 1优 d 1优 、e 1优 For the optimized molar dosage b 优 、c 优 d 优 、e 优 .

[0017] In some embodiments of the present application, if the first cycle discharge specific capacity of the first sodium ion battery is less than the first cycle discharge specific capacity of the corresponding sodium ion batteries of the at least three groups of second iterative molar dosages b2, c2, d2, and e2, step S5 further includes:

[0018] S54: Determine the second optimal molar dosage b based on the first cycle discharge specific capacity of the sodium ion battery corresponding to the at least three groups of second iterative molar dosages b2, c2, d2, and e2. 2优 、c 2优 d 2优 、e 2优 ;

[0019] S55: the second preferred molar amount b 2优 、c 2优 d 2优 、e 2优 As training data, the first discharge voltage of the corresponding sodium ion battery is used as input variable, and the first cycle discharge capacity of the corresponding sodium ion battery is used as output variable to repeat steps S51 to S54 until the nth optimal iterative molar dosage b is screened out. n优 、c n优 d n优 、e n优The corresponding first-cycle discharge specific capacity is lower than one or more of the first-cycle discharge specific capacities of all previous sodium-ion batteries, where n ≥ 2;

[0020] S56: Determine the molar dosage of the divalent nickel salt, divalent manganese salt, divalent copper salt, and divalent lanthanum salt corresponding to the sodium ion battery with the highest first-cycle discharge specific capacity among all previous sodium ion batteries as the optimized molar dosage b 优 、c 优 d 优 、e 优 .

[0021] In some embodiments of the present application, the screening method further comprises:

[0022] S7: Optimizing the molar dosage b 优 、c 优 d 优 、e 优 Perform verification, including:

[0023] S71: The optimized molar dosage b 优 、c 优 d 优 、e 优 Input the Bayesian optimization model for verification to obtain at least three sets of verification molar dosages b 验 、c 验 d 验 、e 验 ;

[0024] S72: Determine the optimized molar dosage b 优 、c 优 d 优 、e 优 Whether the first cycle discharge capacity of the corresponding sodium ion battery is greater than the at least three groups of verification molar dosage b 验 、c 验 d 验 、e 验 The corresponding first-cycle discharge specific capacity of the sodium-ion battery.

[0025] In some embodiments of the present application, if the optimized molar dosage b 优 、c 优 d 优 、e 优 The first cycle discharge capacity of the corresponding sodium ion battery is less than the at least three groups of verification molar dosage b 验 、c 验 d 验 、e 验 The first cycle discharge specific capacity of the corresponding sodium ion battery, step S7 further includes:

[0026] S73: Based on the at least three groups of verification molar dosage b 验 、c 验 d 验 、e 验 The first cycle discharge capacity of the corresponding sodium ion battery confirms the first optimal verification molar dosage b 1验优 、c 1验优 d 1验优 、e 1验优 ;

[0027] S74: the first preferably verified molar dosage b 1验优 、c 1验优 d 1验优 、e 1验优 Input the Bayesian optimization model and repeat steps S71 to S73 until the mth optimal verification molar dosage b is obtained. m验优 、c m验优 d m验优 、e m验优 The first cycle discharge specific capacity of the corresponding sodium ion battery is greater than the first cycle discharge specific capacity of the corresponding sodium ion battery of all previously verified molar dosages, where m ≥ 1; and

[0028] S75: with the mth preferably verify molar dosage b m验优 、c m验优 d m验优 、e m验优 As the optimized molar amount b 优 、c 优 d 优 、e 优 .

[0029] The second aspect of the present application provides a sodium ion battery positive electrode material, which is obtained by any of the screening methods described above.

[0030] In some embodiments of the present application, the raw materials of the sodium ion battery positive electrode material include 0.5 mol Na2CO3, 0.3891 mol divalent nickel salt, 0.5033 mol divalent manganese salt, 0.0588 mol divalent copper salt and 0.0488 mol divalent lanthanum salt.

[0031] In some embodiments of the present application, the sodium ion battery positive electrode material is NaNi x Mn y Cu z La (1-x-y-z) O2, the sodium ion battery positive electrode material includes space groups of R-3m and R-3c, wherein x>0, y>0, z>0.

[0032] A third aspect of the present application provides a sodium ion battery, comprising any of the sodium ion battery positive electrode materials described above.

[0033] In some embodiments of the present application, the first-cycle discharge specific capacity of the sodium ion battery is ≥240 mAh / g.

[0034] This application uses a Bayesian optimization model to screen the molar dosage of raw materials for sodium-ion battery positive electrode materials. Compared with the traditional trial-and-error search method, it is more efficient and effective, and can ultimately obtain sodium-ion battery positive electrode materials with very good performance.

[0035] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0037] Figure 1 This is a process flow chart for screening a positive electrode material for a sodium ion battery provided in one embodiment of the present application.

[0038] Figure 2 This is a process flow chart for screening a positive electrode material for a sodium ion battery provided in another embodiment of the present application.

[0039] Figure 3 This is an SEM image of the positive electrode material of the sodium ion battery prepared in the embodiment of the present application.

[0040] Figure 4 This is the XRD pattern of the positive electrode material of the sodium ion battery prepared in the embodiment of the present application.

[0041] Figure 5 It is a graph showing the initial charge and discharge curves of the positive electrode materials of the sodium ion batteries prepared in the examples and comparative examples of the present application.

[0042] Figure 6 This is a SEM image of the positive electrode material of the sodium ion battery prepared in Comparative Example 1 of the present application.

[0043] Figure 7 This is the XRD pattern of the positive electrode material of the sodium ion battery prepared in Comparative Example 1 of the present application. DETAILED DESCRIPTION

[0044] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0045] The disclosure below provides many different embodiments or examples for implementing the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. Of course, they are merely examples and are not intended to limit the present invention. In addition, the present invention may repeat reference numbers and / or reference letters in different examples. Such repetition is for the purpose of simplicity and clarity and does not in itself indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides examples of various specific processes and materials, but those of ordinary skill in the art will recognize the application of other processes and / or the use of other materials.

[0046] In addition, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs. It will also be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and the present invention, and will not be interpreted in an idealized or overly formal sense unless expressly defined as such in this article.

[0047] As used herein, "about" or "approximately" is inclusive of the stated value and means within an acceptable range of deviation from the particular value as determined by one skilled in the art, taking into account the measurement in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement system). For example, "about" can mean within one or more standard deviations, or within ±30%, ±20%, ±10%, or ±5% of the stated value.

[0048] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the described features. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0049] The following description of the embodiments of the present invention is provided in more detail with reference to the accompanying drawings and examples to provide a better understanding of the present invention and its advantages in various aspects. However, the embodiments and examples described below are for illustrative purposes only and are not intended to limit the present invention.

[0050] The sodium ion battery positive electrode material provided in this application is a La-doped sodium ion battery positive electrode material, which is prepared using Na2CO3, divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt. x Mn y Cu z La (1-x-y-z) O2, where x>0, y>0, z>0.

[0051] Optionally, the divalent nickel salt includes one or more of Ni(CH3COO)2, NiCl2, NiSO4, and Ni(NO3)2. Optionally, the divalent manganese salt includes one or more of Mn(CH3COO)2, MnCl2, MnSO4, and Mn(NO3)2. Optionally, the divalent copper salt includes one or more of Cu(CH3COO)2, CuCl2, CuSO4, and Cu(NO3)2. Optionally, the divalent lanthanum salt includes La(CH3COO)2 and / or LaCl2.

[0052] Figure 1 A method for screening positive electrode materials for sodium ion batteries provided in one embodiment of the present application is shown, comprising the following steps S1 to S6.

[0053] This application provides a "physical empirical interactive" integrated method based on a Bayesian optimization model to assist in regulating the element ratio to form interface defects and improve the discharge capacity. This method completes the reverse design of high-capacity sodium-ion battery positive electrode materials in an iterative manner. In each iteration, a modular integrated design is used to fully combine the advantages of multiple machine learning models to achieve the prediction of the first-cycle discharge capacity. At the same time, the interactive mode can guide the selection and training of the machine learning model based on previous parameters, ultimately achieving accelerated optimization.

[0054] S1: Determine the optimal molar dosage a of Na2CO3.

[0055] When preparing the sodium ion battery cathode material of the above chemical formula, the optimized molar amount a of Na2CO3 is 0.5 mol. As the chemical formula of the sodium ion battery cathode material to be prepared is different, the optimized molar amount a of Na2CO3 used is also different.

[0056] S2: Based on the optimized molar dosage a, obtain at least six groups of preset molar dosages b0, c0, d0, e0 of divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt, as well as the first discharge voltage of the preset sodium ion battery and the first cycle discharge specific capacity of the preset sodium ion battery.

[0057] Here, the traditional trial-and-error search method can be used to sequentially determine at least six sets of preset molar amounts b0, c0, d0, and e0 of divalent nickel salt, divalent manganese salt, divalent copper salt, and divalent lanthanum salt based on the optimized molar amount a of Na2CO3. Alternatively, based on experience, at least six sets of preset molar amounts b0, c0, d0, and e0 of divalent nickel salt, divalent manganese salt, divalent copper salt, and divalent lanthanum salt can be determined based on the optimized molar amount a of Na2CO3. The more sets of preset molar amounts of divalent nickel salt, divalent manganese salt, divalent copper salt, and divalent lanthanum salt, the larger the range of initial data for screening, which is more conducive to obtaining a sodium ion battery positive electrode material with better performance.

[0058] Then, at least six groups of corresponding sodium ion battery positive electrode materials with the above preset molar dosages b0, c0, d0, e0 and the optimized molar dosage a were prepared respectively, and then the corresponding sodium ion batteries were prepared using the same negative electrode material and electrolyte.

[0059] Optionally, the present application adopts the co-precipitation-solid phase method to synthesize the positive electrode material of the sodium ion battery. A predetermined molar amount of divalent nickel salt, divalent manganese salt, divalent copper salt, and divalent lanthanum metal salt is prepared into a solution, ammonia water is used as a complexing agent, the pH of the solution during the reaction is controlled at 8.9±0.3, Na2CO3 is used as a precipitant to synthesize the precursor, and then the precursor and a certain proportion of Na2CO3 are mixed and ball-milled for a certain time, and then pressed into a tablet under a pressure of about 10Mpa, first pre-calcined at about 450°C for about 6h and then calcined at about 850°C for about 10h and cooled to room temperature. The positive electrode material, the negative electrode material and the electrolyte are then made into a sodium ion battery.

[0060] Optionally, the negative electrode material of the sodium-ion battery is a sodium sheet. Optionally, the electrode solution of the sodium-ion battery includes 1M NaClO4, allyl carbonate (PC), ethylene carbonate (EC), and 5% fluoroethylene carbonate (FEC), wherein the molar ratio of PC:EC is 1:1.

[0061] The first discharge voltage and the first cycle discharge specific capacity of the corresponding sodium ion battery are measured respectively, and the results are recorded as the first discharge voltage and the first cycle discharge specific capacity of the preset sodium ion battery. That is, the positive electrode material of the preset sodium ion battery is prepared by optimizing the molar dosage a of Na2CO3 and at least six groups of predetermined molar dosages b0, c0, d0, and e0 of a divalent nickel salt, a divalent manganese salt, a divalent copper salt, and a divalent lanthanum salt.

[0062] For example, at least six groups of preset molar amounts b0, c0, d0, and e0 include seven groups of data, namely the first group b 01 、c 01 d 01 、e 01 , Group 2b 02 、c02 d 02 、e 02 , Group 3b 03 、c 03 d 03 、e 03 , Group 4b 04 、c 04 d 04 、e 04 , Group 5b 05 、c 05 d 05 、e 05 , Group 6b 06 、c 06 d 06 、e 06 , Group 7b 07 、c 07 d 07 、e 07 At this time, seven sodium ion batteries need to be prepared respectively. The molar amounts of the raw materials Na2CO3, divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt of the positive electrode material of the first sodium ion battery are a, b and 01 、c 01 d 01 、e 01 The molar amounts of the raw materials Na2CO3, divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt of the positive electrode material of the second sodium ion battery are a, b and 02 、c 02 d 02 、e 02 The molar amounts of the raw materials Na2CO3, divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt of the positive electrode material of the third sodium ion battery are a, b and 03 、c 03 d 03 、e 03 The molar amounts of the raw materials Na2CO3, divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt of the fourth sodium ion battery positive electrode material are a, b and 04 、c 04 d 04 、e 04 The molar amounts of the raw materials Na2CO3, divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt of the fifth sodium ion battery positive electrode material are a, b and 05 、c 05 d 05 、e 05 The molar amounts of the raw materials Na2CO3, divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt of the sixth sodium ion battery positive electrode material are a, b and 06 、c06 d 06 、e 06 The molar amounts of the raw materials Na2CO3, divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt of the seventh sodium ion battery positive electrode material are a, b and 07 、c 07 d 07 、e 07 .

[0063] S3: Using at least six sets of preset molar dosages b0, c0, d0, and e0 as training data, the preset first discharge voltage of the sodium ion battery as the input variable, and the preset first-cycle discharge specific capacity of the sodium ion battery as the output variable, the Bayesian optimization model is trained and data iterated to obtain at least three sets of first-iteration molar dosages b1, c1, d1, and e1.

[0064] The data obtained in step S2 are input into the Bayesian optimization model as training data, input variables, and output variables, respectively. The Bayesian optimization model is trained so that a mapping relationship is established in the Bayesian optimization model between the first-cycle discharge specific capacity and key characteristic parameters such as the molar dosage and first discharge voltage of the divalent nickel salt, divalent manganese salt, divalent copper salt, and divalent lanthanum salt. While the model is being trained, the data is also iterated, so that at least three sets of first-iteration molar dosages b1, c1, d1, and e1 can be obtained. The number of sets of data obtained after iteration can be set as needed.

[0065] S4: Determine the first optimal molar dosage b based on the first cycle discharge specific capacity of the corresponding sodium ion battery of at least three groups of first iterative molar dosages b1, c1, d1, and e1. 1优 、c 1优 d 1优 、e 1优 .

[0066] That is, select the group with the highest first-cycle discharge capacity among the first-cycle discharge capacity of the corresponding sodium ion battery of at least three groups of first-iteration molar dosages b1, c1, d1, and e1, and then use it as the first preferred molar dosage b 1优 、c 1优 d 1优 、e 1优 For example, there are four groups of first-iteration molar dosages b1, c1, d1, and e1, which are the first group b 11 、c 11 d 11 、e 11 , Group 2b 12 、c 12 d 12 、e 12 , Group 3b 13 、c 13 d13 、e 13 and Group 4b 14 、c 14 d 14 、e 14 If the second group b 12 、c 12 d 12 、e 12 The first cycle discharge capacity of the corresponding sodium ion battery is the highest, so the second group b 12 、c 12 d 12 、e 12 As the first preferred molar amount b 1优 、c 1优 d 1优 、e 1优 .

[0067] S5: Based on the first preferred molar dosage b 1优 、c 1优 d 1优 、e 1优 The first discharge voltage of the corresponding sodium ion battery is used as the input variable and the first cycle discharge capacity of the corresponding sodium ion battery is used as the output variable. The Bayesian optimization model is optimized and iterated to obtain the optimized molar dosage b of divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt. 优 、c 优 d 优 , eyou.

[0068] This step is based on the first optimal molar dosage b obtained in the first iteration 1优 、c 1优 d 1优 、e 1优 The Bayesian optimization model was optimized and iterated again based on the first discharge voltage and first cycle discharge capacity of the corresponding sodium ion battery. After multiple training and data iterations, the optimized molar dosage b of divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt was finally obtained. 优 、c 优 d 优 、e 优 .

[0069] Optionally, step S5 may include the following sub-steps S51 to S53.

[0070] S51: the first preferred molar amount b 1优 、c 1优 d 1优 、e 1优The Bayesian optimization model is optimized and iterated using the first discharge voltage of the first sodium ion battery as training data, the first discharge voltage of the first sodium ion battery as input variable, and the first cycle discharge specific capacity of the first sodium ion battery as output variable to obtain at least three sets of second iteration molar dosages b2, c2, d2, and e2.

[0071] The positive electrode material of the first sodium ion battery is obtained by optimizing the molar dosage a of Na2CO3 and the first preferred molar dosage b 1优 、c 1优 d 1优 、e 1优 This step is similar to step S3 and will not be described in detail here.

[0072] S52: Determine whether the first cycle discharge specific capacity of the first sodium ion battery is less than the first cycle discharge specific capacity of the corresponding sodium ion batteries of at least three groups of second iterative molar dosages b2, c2, d2, and e2.

[0073] That is, determine the first optimal molar dosage b selected in the first iteration 1优 、c 1优 d 1优 、e 1优 Whether the first cycle discharge capacity of the corresponding sodium ion battery is the largest.

[0074] S53: If not, use the first preferred molar amount b 1优 、c 1优 d 1优 、e 1优 To optimize the molar dosage b 优 、c 优 d 优 、e 优 .

[0075] When the first iteration is performed, the first optimal molar dosage b is selected. 1优 、c 1优 d 1优 、e 1优 When the first cycle discharge capacity of the corresponding sodium ion battery is the largest, it means that the first optimal molar dosage b selected in the first iteration is 1优 、c 1优 d 1优 、e 1优 The corresponding sodium-ion battery is the one with the best performance. The iteration process ends at this point.

[0076] If the first cycle discharge capacity of the first sodium ion battery is less than the first cycle discharge capacity of the corresponding sodium ion batteries of at least three groups of second iterative molar dosages b2, c2, d2, and e2, step S5 further includes the following steps S54 to S56. 1优 、c 1优 d 1优 、e 1优 The corresponding sodium-ion battery is not the one with the best performance, and further iteration is needed until the one with the best performance is selected.

[0077] S54: If yes, determine the second optimal molar dosage b based on the first cycle discharge specific capacity of the corresponding sodium ion battery of at least three groups of second iterative molar dosages b2, c2, d2, and e2. 2优 、c 2优 d 2优 、e 2优 .

[0078] That is, select the group with the highest first-cycle discharge capacity among the first-cycle discharge capacity of the corresponding sodium ion battery of at least three groups of second-iteration molar dosages b2, c2, d2, and e2, and then use it as the second preferred molar dosage b 2优 、c 2优 d 2优 、e 2优 This step is similar to step S4 and will not be described again here.

[0079] S55: the second preferred molar amount b 2优 、c 2优 d 2优 、e 2优 As training data, the first discharge voltage of the corresponding sodium ion battery is used as input variable, and the first cycle discharge capacity of the corresponding sodium ion battery is used as output variable to repeat steps S51 to S54 until the nth optimal iterative molar dosage b is screened out. n优 、c n优 d n优 、e n优 The corresponding first-cycle discharge specific capacity is lower than one or more of the first-cycle discharge specific capacities of all previous sodium-ion batteries, where n≥2.

[0080] S56: Determine the molar dosage of the divalent nickel salt, divalent manganese salt, divalent copper salt, and divalent lanthanum salt corresponding to the sodium ion battery with the highest first-cycle discharge specific capacity among all previous sodium ion batteries as the optimized molar dosage b 优 、c 优 d 优 、e 优 .

[0081] If the second preferred molar dosage b2优 、c 2优 d 2优 、e 2优 The first cycle discharge capacity of the corresponding sodium ion battery is the highest, so it is used as the optimized molar dosage b 优 、c 优 d 优 、e 优 If it is still not the highest, then steps S51 to S54 are repeated continuously to optimize the model and train it, and the data is continuously optimized and iterated until the group with the highest first-cycle discharge capacity is found, and it is used as the optimized molar dosage b. 优 、c 优 d 优 、e 优 .

[0082] The aforementioned model training and data iteration process can utilize the "exploration" strategy in the Bayesian optimization model. The "exploration" strategy tends to recommend components with greater uncertainty, which is more conducive to improving the accuracy of model predictions.

[0083] S6: Based on the optimized molar dosage a and the optimized molar dosage b 优 、c 优 d 优 、e 优 , prepare positive electrode materials for sodium ion batteries.

[0084] That is, the molar amounts are a and b respectively. 优 、c 优 d 优 、e 优 The sodium ion battery cathode material is prepared by combining Na2CO3, divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt. The obtained sodium ion battery cathode material is the cathode material with the best performance selected.

[0085] After the data iteration is completed (after step S5 is completed), the method provided by the present application may further include step S7: optimizing the molar dosage b 优 、c 优 d 优 、e 优 To verify, such as Figure 2 Step S7 may include the following sub-steps S71 and S72.

[0086] S71: Optimize the molar dosage b 优 、c 优 d 优 、e 优 Input into the Bayesian optimization model for verification and obtain at least three sets of verification molar dosages b 验 、c 验 d 验、e 验 .

[0087] The validation process can utilize the “utilization” strategy in the Bayesian optimization model. The “utilization” strategy tends to recommend components with better prediction results to optimize material properties.

[0088] S72: Determine the optimal molar dosage b 优 、c 优 d 优 、e 优 Is the first cycle discharge capacity of the corresponding sodium ion battery greater than at least three groups of verification molar dosage b 验 、c 验 d 验 、e 验 The corresponding first-cycle discharge specific capacity of the sodium-ion battery.

[0089] That is to verify the optimized molar dosage b 优 、c 优 d 优 、e 优 Is the first cycle discharge capacity of the corresponding sodium ion battery the largest? If so, it proves that the optimized molar dosage b obtained by the aforementioned data iteration process is 优 、c 优 d 优 、e 优 This is the optimal data. The verification process can be ended at this point.

[0090] If the molar dosage b is optimized 优 、c 优 d 优 、e 优 The first cycle discharge capacity of the corresponding sodium ion battery is less than at least three groups of verified molar dosage b 验 、c 验 d 验 、e 验 The first cycle discharge capacity of the corresponding sodium ion battery, step S7 also includes the following steps S73 to S75. At this time, it is explained that the optimized molar dosage b is verified. 优 、c 优 d 优 、e 优 The first cycle discharge capacity of the corresponding sodium ion battery is not the largest, and the optimized molar dosage b obtained by the aforementioned data iteration process is 优 、c 优 d 优 、e 优 Not optimal data.

[0091] S73: Based on at least three groups of verification molar dosage b 验 、c 验 d 验、e 验 The first cycle discharge capacity of the corresponding sodium ion battery confirms the first optimal verification molar dosage b 1验优 、c 1验优 d 1验优 、e 1验优 .

[0092] That is, select at least three groups to verify the molar dosage b 验 、c 验 d 验 、e 验 The first cycle discharge capacity of the corresponding sodium ion battery is the highest among the first cycle discharge capacity, and then it is used as the first optimal verification molar dosage b 1验优 、c 1验优 d 1验优 、e 1验优 This step is similar to step S4 and will not be described again here.

[0093] S74: the first preferably verified molar dosage b 1验优 、c 1验优 d 1验优 、e 1验优 Input into the Bayesian optimization model and repeat steps S71 to S73 until the mth optimal verification molar dosage b is obtained. m验优 、c m验优 d m验优 、e m验优 The first-cycle discharge specific capacity of the corresponding sodium-ion battery is greater than the first-cycle discharge specific capacity of the corresponding sodium-ion battery of all previously verified molar dosages, where m≥1.

[0094] S75: preferably verify the molar dosage b with the mth m验优 、c m验优 d m验优 、e m验优 As the optimized molar amount b 优 、c 优 d 优 、e 优 .

[0095] If the first optimal verification molar dosage b 1验优 、c 1验优 d 1验优 、e 1验优 The first cycle discharge capacity of the corresponding sodium ion battery is the highest, so it is used as the optimized molar dosage b 优 、c 优 d 优 、e 优 If it is still not the highest, then repeat steps S71 to S73 to verify the data until the group with the highest first cycle discharge capacity is found and used as the optimized molar dosage b.优 、c 优 d 优 、e 优 .

[0096] This application uses a machine learning model to screen the molar dosage of raw materials for sodium-ion battery positive electrode materials. Compared with the traditional trial-and-error search method, it is more efficient and effective, and can ultimately obtain sodium-ion battery positive electrode materials with very good performance.

[0097] The present application also provides a sodium ion battery positive electrode material obtained by the above screening method.

[0098] Optionally, when the positive electrode material of the sodium ion battery to be prepared is NaNi x Mn y Cu z La (1-x-y-z) O2, x>0, y>0, z>0, the optimized molar dosage b selected by the above method 优 、c 优 d 优 、e 优 The raw materials at this time include 0.5 mol Na2CO3, 0.3891 mol divalent nickel salt, 0.5033 mol divalent manganese salt, 0.0588 mol divalent copper salt and 0.0488 mol divalent lanthanum salt.

[0099] Optionally, the sodium ion battery cathode material obtained by screening in the present application includes space groups R-3m and R-3c, while the existing La-free cathode material only has the R-3m space group. The presence of the phase structure of the cathode material of the present application can form a disordered transition metal layer, thereby affecting the order of sodium ions and vacancies, and further affecting the ion migration during the intercalation and deintercalation process, so that the performance of the prepared sodium ion battery is very good.

[0100] The present application further provides a sodium ion battery comprising the above-mentioned sodium ion battery cathode material. The first cycle discharge specific capacity of the sodium ion battery cathode material screened and obtained in the present application can be ≥240 mAh / g, which is about 35% higher than that of existing La-free cathode materials.

[0101] The present invention will be described below with reference to specific embodiments. The process condition values ​​taken in the following examples and comparative examples are exemplary, and their acceptable numerical ranges are as shown in the aforementioned summary of the invention. For process parameters not particularly noted, conventional techniques can be used. Unless otherwise specified, reagents and instruments used in the technical scheme provided by the present invention can be purchased from conventional channels or the market. It should be noted that, in the absence of conflict, the features in the embodiments in this application and the embodiments can be combined with each other.

[0102] Example

[0103] This example uses a Bayesian optimization model to screen sodium ion battery cathode materials. The specific steps are as follows:

[0104] 1) Determine the target sodium ion battery cathode material as NaNi x Mn y Cu z La (1-x-y-z) O2, x>0, y>0, z>0. According to the chemical formula, the optimal molar amount a of Na2CO3 is confirmed to be 0.5 mol.

[0105] 2) Through experimental experience, six sets of preset molar amounts b0, c0, d0, and e0 were determined, as shown in Table 1.

[0106] Then, the co-precipitation-solid phase method was used to synthesize the positive electrode material. The specific process was as follows: predetermined molar amounts of Ni(CH3COO)2, Mn(CH3COO)2, Cu(CH3COO)2 and La(CH3COO)2 metal salts were prepared into a solution, ammonia water was used as a complexing agent, the pH of the solution was controlled at 8.9±0.3 during the reaction, and Na2CO3 was used as a precipitant to synthesize the precursor. The precursor was further mixed with a certain proportion of Na2CO3 and ball-milled for a certain period of time. After being pressed into tablets under a pressure of about 10 MPa, it was pre-calcined at about 450°C for about 6 hours, and then continued to calcine at about 850°C for about 10 hours and cooled to room temperature.

[0107] Next, a sodium-ion battery was fabricated by combining the positive and negative electrode materials with an electrolyte. The negative electrode material was a sodium flake, and the electrolyte consisted of 1M NaClO₄, propylene carbonate (PC), ethylene carbonate (EC), and 5% fluoroethylene carbonate (FEC), with a PC:EC molar ratio of 1:1. The sodium-ion battery's first discharge voltage and first-cycle discharge specific capacity were measured; see Table 1 for specific data.

[0108] 3) The six groups of molar dosages b0, c0, d0, and e0 in Table 1 were used as training data, the first discharge voltage of the corresponding sodium ion battery was used as the input variable, and the first cycle discharge specific capacity of the corresponding sodium ion battery was used as the output variable to train and iterate the Bayesian optimization model to obtain three groups of first iteration molar dosages b1, c1, d1, and e1. The specific data are shown in Table 2.

[0109] The same coprecipitation-solid phase method as in step 2) was used to prepare sodium ion batteries by changing only the molar amounts of Ni(CH3COO)2, Mn(CH3COO)2, Cu(CH3COO)2 and La(CH3COO)2. The first discharge voltage and first cycle discharge specific capacity of the batteries were measured. The specific data are shown in Table 2.

[0110] From Table 2, it can be seen that the first cycle discharge capacity of group 1-1 is the largest, which is 243.3 mAh / g. Therefore, the data of group 1-1 is taken as the first preferred molar dosage b. 1优 、c 1优 d 1优 、e 1优 .

[0111] 3) The Bayesian optimization model was optimized and iterated using the data of group 1-1 as training data, the first discharge voltage of the corresponding sodium ion battery as input variable, and the first cycle discharge specific capacity of the corresponding sodium ion battery as output variable to obtain five groups of second iteration molar dosages b2, c2, d2, and e2. The specific data are shown in Table 3.

[0112] The sodium ion battery was prepared by the coprecipitation-solid phase method in step 2), only the molar amounts of Ni(CH3COO)2, Mn(CH3COO)2, Cu(CH3COO)2 and La(CH3COO)2 were changed, and its first discharge voltage and first cycle discharge specific capacity were measured. The specific data are shown in Table 3.

[0113] Comparing Table 2 and Table 3, it can be seen that the first cycle discharge specific capacity of the sodium ion battery corresponding to Group 1-1 in Table 2 is higher than the first cycle discharge specific capacity of the sodium ion battery corresponding to the five groups of data in Table 3. Therefore, Group 1-1 data is determined as the optimized molar dosage b 优 、c 优 d 优 、e 优 .

[0114] 5) The data of group 1-1 were input into the Bayesian optimization model again for verification, and five groups of verification molar dosages b were obtained. 验 、c 验 d 验 、e 验 , see Table 4 for specific data.

[0115] The coprecipitation-solid phase method in step 2) was used to prepare sodium ion batteries by changing only the molar amounts of Ni(CH3COO)2, Mn(CH3COO)2, Cu(CH3COO)2 and La(CH3COO)2. The first discharge voltage and first cycle discharge specific capacity of the batteries were measured. The specific data are shown in Table 4.

[0116] By comparing Table 2 and Table 4, it can be seen that the first-cycle discharge specific capacity of the sodium-ion battery corresponding to Group 1-1 in Table 2 is higher than the first-cycle discharge specific capacity of the sodium-ion battery corresponding to the five groups of data in Table 4, so the verification process is terminated.

[0117] 6) Using the coprecipitation-solid phase method in step 2), the molar amounts of Ni(CH3COO)2, Mn(CH3COO)2, Cu(CH3COO)2 and La(CH3COO)2 are set to group 1-1 data to prepare a sodium ion battery. The battery obtained is the sodium ion battery NaNi with the best performance. 0.3891 Mn 0.5033 Cu 0.0588 La 0.0488 O2(NMCL).

[0118] Figure 3 The SEM image of the cathode material of the sodium ion battery with the best performance is shown. Figure 4 Its XRD pattern is shown, Figure 5 Its initial charge and discharge curves are shown.

[0119] Comparative Example 1

[0120] This comparative example prepares a La-free positive electrode material NaNi 0.3891 Mn 0.5521 Cu 0.0588 O2 (NMC). The difference between it and the best performance sodium ion battery obtained in the embodiment is that the raw material does not contain La (CH3COO) 2, and the molar amount of the raw material is adaptively adjusted. The first cycle discharge capacity of the prepared sodium ion battery is 175.5 mAh / g, and the initial charge and discharge curve is shown in FIG. Figure 5 The SEM and XRD patterns of the positive electrode material of the sodium ion battery are shown in Figure 6 and Figure 7 .

[0121] Comparative Example 2

[0122] This comparative example prepares a Ce-doped positive electrode material with the chemical formula of NaNi 0.3891 Mn 0.5033 Cu 0.0588 Ce 0.0488 O2(NMCC), which differs from the embodiment in that La(CH3COO)2 is replaced by Ce(CH3COO)2. The initial charge and discharge curve of the prepared sodium ion battery is shown in Figure 5 .

[0123] Comparative Example 3

[0124] This comparative example prepares a Ce-doped positive electrode material with the chemical formula of NaNi 0.3891 Mn 0.5033 Cu 0.0588 Nd 0.0488 O2(NMCN), which differs from the embodiment in that La(CH3COO)2 is replaced by Nd(CH3COO)2. The initial charge and discharge curve of the prepared sodium ion battery is shown in Figure 5.

[0125] Table 1

[0126]

[0127] Table 2

[0128]

[0129]

[0130] Table 3

[0131]

[0132] Table 4

[0133]

[0134]

[0135] As shown in Tables 1 to 4, the above examples identified the optimal element ratio after synthesizing only 19 samples. This optimal element ratio resulted in a first-cycle discharge capacity of 243.3 mAh / g for the cathode material assembled into a half-cell, significantly exceeding the first-cycle discharge capacity of sodium-ion batteries reported in prior art.

[0136] from Figure 3 and Figure 6 It can be seen that the positive electrode material obtained by screening in the present application has a layered structure, while the layered structure of the positive electrode material of Comparative Example 1 is not very good.

[0137] from Figure 4 and Figure 7 It can be seen that the positive electrode material introduced with the La element obtained by screening in this application has two phases with space groups R-3m (O3) and R-3c (asterisks in the figure). The existence of this special structure leads to the formation of a disordered transition metal layer, which in turn affects the order of sodium ions and vacancies, and further affects the ion migration during the insertion and extraction process. In contrast, the positive electrode material of Comparative Example 1 only has a pure phase with space group R-3m and a long-range ordered structure.

[0138] from Figure 5 It can be seen that the charge and discharge performance of the sodium ion battery obtained by screening in the present application is the best, among which the charge and discharge performance of the sodium ion battery prepared in Comparative Example 1 is second, and the charge and discharge performance of the sodium ion battery prepared in Comparative Example 2 is the worst.

[0139] Obviously, the above embodiments are merely examples for the purpose of clearly illustrating the present invention and are not intended to limit the embodiments. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all embodiments here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for screening positive electrode materials for sodium ion batteries, characterized in that: The raw materials for preparing the sodium ion battery positive electrode material include Na2CO3, divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt, and the screening method includes: S1: Determine the optimal molar dosage a of Na2CO3; S2: Based on the optimized molar dosage a, obtaining at least six groups of preset molar dosages b0, c0, d0, e0 of divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt, as well as a preset first discharge voltage of the sodium ion battery and the preset first cycle discharge specific capacity of the sodium ion battery; wherein the positive electrode material of the preset sodium ion battery is prepared by the optimized molar dosage a of Na2CO3 and the at least six groups of preset molar dosages b0, c0, d0, e0 of divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt; S3: Using the at least six groups of preset molar dosages b0, c0, d0, and e0 as training data, the first discharge voltage of the preset sodium ion battery as an input variable, and the first cycle discharge specific capacity of the preset sodium ion battery as an output variable, the Bayesian optimization model is trained and data iterated to obtain at least three groups of first iteration molar dosages b1, c1, d1, and e1; S4: Determine the first optimal molar dosage b based on the first cycle discharge specific capacity of the sodium ion battery corresponding to the at least three groups of first iterative molar dosages b1, c1, d1, and e1. 1优 、c 1优 d 1优 、e 1优 ; S5: Based on the first preferred molar amount b 1优 、c 1优 d 1优 、e 1优 The Bayesian optimization model is optimized and iterated using the first discharge voltage of the corresponding sodium ion battery as the input variable and the first cycle discharge capacity of the corresponding sodium ion battery as the output variable to obtain the optimized molar amount b of divalent nickel salt, divalent manganese salt, divalent copper salt and divalent lanthanum salt. 优 、c 优 d 优 、e 优 ; as well as S6: Based on the optimized molar dosage a and the optimized molar dosage b 优 、c 优 d 优 、e 优 , preparation of positive electrode materials for sodium ion batteries; Wherein, when the positive electrode material of the sodium ion battery is NaNi x Mn y Cu z La （1-x-y-z） O2, the sodium ion battery positive electrode material includes the space groups of R-3m and R-3c, x>0, y>0, z>0, the optimized molar dosage a is 0.5 mol, the optimized molar dosage b 优 、c 优 d 优 、e 优 They are 0.3891 mol, 0.5033 mol, 0.0588 mol and 0.0488 mol respectively.

2. The screening method according to claim 1, wherein Step S5 includes: S51: the first preferred molar amount b 1优 、c 1优 d 1优 、e 1优 As training data, the first discharge voltage of the first sodium ion battery as an input variable, and the first cycle discharge specific capacity of the first sodium ion battery as an output variable, the Bayesian optimization model is optimized and iterated to obtain at least three groups of second iteration molar dosages b2, c2, d2, and e2; wherein the positive electrode material of the first sodium ion battery is Na2CO3 of the optimized molar dosage a and the first preferred molar dosage b 1优 、c 1优 d 1优 、e 1优 It is prepared from divalent nickel salts, divalent manganese salts, divalent copper salts and divalent lanthanum salts; S52: Determine whether the first cycle discharge specific capacity of the first sodium ion battery is less than the first cycle discharge specific capacity of the corresponding sodium ion batteries of the at least three groups of second iterative molar dosages b2, c2, d2, and e2; S53: If not, use the first preferred molar amount b 1优 、c 1优 d 1优 、e 1优 For the optimized molar dosage b 优 、c 优 d 优 、e 优 .

3. The screening method according to claim 2, characterized in that If the first cycle discharge specific capacity of the first sodium ion battery is less than the first cycle discharge specific capacity of the corresponding sodium ion batteries of the at least three groups of second iterative molar dosages b2, c2, d2, and e2, step S5 further includes: S54: Determine the second optimal molar dosage b based on the first cycle discharge specific capacity of the sodium ion battery corresponding to the at least three groups of second iterative molar dosages b2, c2, d2, and e2. 2优 、c 2优 d 2优 、e 2优 ; S55: the second preferred molar amount b 2优 、c 2优 d 2优 、e 2优 As training data, the first discharge voltage of the corresponding sodium ion battery is used as the input variable, and the first cycle discharge specific capacity of the corresponding sodium ion battery is used as the output variable to repeat steps S51 to S54 until the nth optimal iterative molar dosage b is screened out. n优 、c n优 d n优 、e n优 The corresponding first-cycle discharge specific capacity is lower than one or more of the first-cycle discharge specific capacities of all previous sodium-ion batteries, where n ≥ 2; S56: Determine the molar dosage of the divalent nickel salt, divalent manganese salt, divalent copper salt, and divalent lanthanum salt corresponding to the sodium ion battery with the highest first-cycle discharge specific capacity among all previous sodium ion batteries as the optimized molar dosage b 优 、c 优 d 优 、e 优 .

4. The screening method according to claim 1, wherein Also includes: S7: Optimizing the molar dosage b 优 、c 优 d 优 、e 优 Perform verification, including: S71: The optimized molar dosage b 优 、c 优 d 优 、e 优 Input the Bayesian optimization model for verification to obtain at least three sets of verification molar dosages b 验 、c 验 d 验 、e 验 ; S72: Determine the optimized molar dosage b 优 、c 优 d 优 、e 优 Whether the first cycle discharge capacity of the corresponding sodium ion battery is greater than the at least three groups of verification molar dosage b 验 、c 验 d 验 、e 验 The corresponding first-cycle discharge specific capacity of the sodium-ion battery.

5. The screening method according to claim 4, characterized in that If the optimized molar dosage b 优 、c 优 d 优 、e 优 The first cycle discharge capacity of the corresponding sodium ion battery is less than the at least three groups of verification molar dosage b 验 、c 验 d 验 、e 验 The first cycle discharge specific capacity of the corresponding sodium ion battery, step S7 further includes: S73: Based on the at least three groups of verification molar dosage b 验 、c 验 d 验 、e 验 The first cycle discharge capacity of the corresponding sodium ion battery confirms the first optimal verification molar dosage b 1验优 、c 1验优 d 1验优 、e 1验优 ; S74: The first preferably verified molar dosage b 1验优 、c 1验优 d 1验优 、e 1验优 Input the Bayesian optimization model and repeat steps S71 to S73 until the mth optimal verification molar dosage b is obtained. m验优 、c m验优 d m验优 、e m验优 The first cycle discharge specific capacity of the corresponding sodium ion battery is greater than the first cycle discharge specific capacity of the corresponding sodium ion battery of all previously verified molar dosages, where m ≥ 1; and S75: with the mth preferably verify molar dosage b m验优 、c m验优 d m验优 、e m验优 As the optimized molar amount b 优 、c 优 d 优 、e 优 .

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