Design method and device of high-energy-density battery and computer storage medium

Abstract

The invention discloses a design method and device of a high-energy-density battery and a computer storage medium, and relates to the technical field of lithium ion batteries, and the method comprises the following steps: determining a plurality of target LFO particles, and generating a particle size distribution diagram based on all the target LFO particles; based on the particle size distribution diagram and a predetermined target grading model, a particle size distribution result is output, and the particle size distribution result comprises distribution parameters of all the target LFO particles; and generating design parameters of the LFP battery according to a particle size distribution result. Visibly, the particle distribution information of the LFO particles can be intelligently determined in combination with the pre-determined target grading model, and then the corresponding battery design parameters are generated based on the particle distribution information, so that the accuracy and efficiency of the generated battery design parameters are improved; and the design efficiency and the design reliability of the required high-energy density battery can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium ion batteries, and in particular to a design method and device for a high energy density battery, and a computer storage medium. Background Art

[0002] Lithium batteries are widely used in production and life due to their high energy density and environmental protection. At present, lithium batteries mostly use mixed materials of different particle sizes as positive electrode materials. Studies have found that the compaction density of the positive electrode material directly affects the energy density of the battery. The compaction density is related to the closest stacking of the positive electrode material. If the closest stacking of the positive electrode material can be achieved, the compaction density of the positive electrode material can be increased, thereby improving the electrochemical performance of the positive electrode material.

[0003] The current method to improve the energy density of lithium batteries is usually to use a multi-layer coating method to improve the uniformity of lithium supplementation and thus improve the battery energy density. However, the compaction density of lithium agent powder is lower than that of high-compacted LFP powder. Under the demand for high-energy density batteries, thick pole piece design is required. The multi-layer active material setting will increase the thickness of the pole piece. Too thick electrodes will produce serious polarization, affecting the battery cell capacity and energy density. In addition, the actual processing of multi-layer coating is difficult. The mismatch between the compaction density of lithium supplement and LFP powder will lead to an increase in the elongation rate of the pole piece, bringing the risk of belt breakage. Therefore, it is particularly important to provide a new design method for high-energy density batteries to improve the compaction density of LFP powder and the compaction effect to obtain the accuracy of high-energy density batteries. Summary of the invention

[0004] The present invention provides a design method and device for a high energy density battery, and a computer storage medium, which can intelligently determine the particle distribution information of LFO particles in combination with a predetermined target grading model, and then generate corresponding battery design parameters based on the particle distribution information, which is beneficial to improving the accuracy and efficiency of the generated battery design parameters, and further beneficial to improving the design efficiency and design reliability of the required high energy density battery.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses a design method for a high energy density battery, the method comprising:

[0006] determining a plurality of target LFO particles, and generating a particle size distribution diagram based on all of the target LFO particles;

[0007] Based on the particle size distribution graph and a predetermined target grading model, outputting a particle size distribution result, wherein the particle size distribution result includes distribution parameters of all the target LFO particles;

[0008] Based on the particle size distribution results, the design parameters of the LFP battery are generated.

[0009] As an optional embodiment, in the first aspect of the present invention, before outputting the particle size distribution result based on the particle size distribution graph and the predetermined target gradation model, the method further includes:

[0010] Acquire design requirement information, and determine detailed requirement parameters of the LFP battery based on the design requirement information;

[0011] According to the fine demand parameters, a target grading model matching the fine demand parameters is determined in the predetermined grading model, and the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target grading model is triggered.

[0012] As an optional embodiment, in the first aspect of the present invention, after outputting the particle size distribution result based on the particle size distribution graph and the predetermined target gradation model, the method further comprises:

[0013] Determine the current powder compaction density according to the particle size distribution result, and judge whether the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information;

[0014] When it is determined that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information, the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target grading model is retriggered.

[0015] As an optional embodiment, in the first aspect of the present invention, generating the design parameters of the LFP battery according to the particle size distribution result includes:

[0016] Determine a particle size parameter of each of the target LFO particles, and determine a particle distribution parameter corresponding to each of the target LFO particles based on the particle size parameter of each of the target LFO particles and the particle size distribution result;

[0017] According to the particle distribution parameters corresponding to each of the target LFO particles, the design parameters of the LFP battery are generated; wherein the design parameters include the particle distribution information corresponding to all the target LFO particles.

[0018] As an optional embodiment, in the first aspect of the present invention, outputting a particle size distribution result based on the particle size distribution graph and a predetermined target grading model includes:

[0019] Inputting the particle size distribution graph into a predetermined target grading model to obtain a model output result, and determining the particle distribution results of all the target LFO particles according to the model output result;

[0020] Based on the particle distribution results of all the target LFO particles, a particle size distribution result is output.

[0021] As an optional implementation, in the first aspect of the present invention, after determining that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information, the method further includes:

[0022] Analyzing the density difference between the current powder compaction density and the powder compaction required density corresponding to the design requirement information;

[0023] Based on the density difference, the target gradation model is updated, and the operation of outputting a particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model is triggered.

[0024] As an optional implementation, in the first aspect of the present invention, determining a target gradation model matching the fine demand parameter in a predetermined gradation model according to the fine demand parameter comprises:

[0025] According to the fine demand parameters, determining the particle grading ratio parameters of all the target LFO particles;

[0026] According to the particle gradation ratio parameter, the particle ratio parameters corresponding to all the target LFO particles are determined, and a target gradation model matching the particle ratio parameter is determined from all pre-determined gradation models.

[0027] As an optional embodiment, in the first aspect of the present invention, generating a particle size distribution diagram based on all the target LFO particles comprises:

[0028] Acquire a particle scanning result obtained by scanning the target LFO particles by SEM, and generate particle normal distribution information based on the particle scanning result;

[0029] Based on the normal distribution information of the particles, a particle size distribution graph is generated.

[0030] A second aspect of the present invention discloses a design device for a high energy density battery, the device comprising:

[0031] A memory storing executable program code;

[0032] a processor coupled to the memory;

[0033] The processor calls the executable program code stored in the memory to execute the high energy density battery design method disclosed in the first aspect of the present invention.

[0034] The third aspect of the present invention discloses another computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the design method of the high energy density battery disclosed in the first aspect of the present invention.

[0035] A fourth aspect of the present invention discloses yet another design device for a high energy density battery, the device comprising:

[0036] A determination module, used for determining a number of target LFO particles;

[0037] A generating module, for generating a particle size distribution diagram based on all the target LFO particles;

[0038] An output module, configured to output a particle size distribution result based on the particle size distribution graph and a predetermined target gradation model, wherein the particle size distribution result includes distribution parameters of all the target LFO particles;

[0039] The generation module is also used to generate design parameters of the LFP battery according to the particle size distribution result.

[0040] As an optional implementation, in the fourth aspect of the present invention, the device further includes:

[0041] An acquisition module, used for acquiring design requirement information before the output module outputs a particle size distribution result based on the particle size distribution diagram and a predetermined target gradation model;

[0042] The determination module is further used to determine the detailed requirement parameters of the LFP battery based on the design requirement information;

[0043] The determination module is also used to determine a target grading model that matches the fine demand parameters in the predetermined grading model according to the fine demand parameters, and trigger the output module to perform the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target grading model.

[0044] As an optional embodiment, in the fourth aspect of the present invention, the determination module is further used to determine the current powder compaction density according to the particle size distribution result after the output module outputs the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model;

[0045] The device also includes:

[0046] A judgment module is used to judge whether the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information; when it is judged that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information, the output module is re-triggered to execute the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target grading model.

[0047] As an optional embodiment, in the fourth aspect of the present invention, the specific manner in which the generation module generates the design parameters of the LFP battery according to the particle size distribution result includes:

[0048] Determine a particle size parameter of each of the target LFO particles, and determine a particle distribution parameter corresponding to each of the target LFO particles based on the particle size parameter of each of the target LFO particles and the particle size distribution result;

[0049] According to the particle distribution parameters corresponding to each of the target LFO particles, the design parameters of the LFP battery are generated; wherein the design parameters include the particle distribution information corresponding to all the target LFO particles.

[0050] As an optional implementation, in the fourth aspect of the present invention, the output module outputs the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model in a specific manner including:

[0051] Inputting the particle size distribution diagram into a predetermined target grading model to obtain a model output result, and determining the particle distribution results of all the target LFO particles according to the model output result;

[0052] Based on the particle distribution results of all the target LFO particles, a particle size distribution result is output.

[0053] As an optional implementation, in the fourth aspect of the present invention, the device further includes:

[0054] An analysis module, configured to analyze a density difference between the current powder compaction density and the powder compaction requirement density corresponding to the design requirement information after the judgment module determines that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information;

[0055] The updating module is used to update the target gradation model based on the density difference, and trigger the output module to execute the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model.

[0056] As an optional implementation, in the fourth aspect of the present invention, the specific manner in which the determination module determines the target gradation model matching the fine demand parameter in the pre-determined gradation model according to the fine demand parameter includes:

[0057] According to the fine demand parameters, determining the particle grading ratio parameters of all the target LFO particles;

[0058] According to the particle gradation ratio parameter, the particle ratio parameters corresponding to all the target LFO particles are determined, and a target gradation model matching the particle ratio parameter is determined from all pre-determined gradation models.

[0059] As an optional implementation, in the fourth aspect of the present invention, the specific manner in which the generating module generates the particle size distribution diagram based on all the target LFO particles includes:

[0060] Acquire a particle scanning result obtained by scanning the target LFO particles by SEM, and generate particle normal distribution information based on the particle scanning result;

[0061] Based on the normal distribution information of the particles, a particle size distribution graph is generated.

[0062] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0063] In the embodiment of the present invention, a number of target LFO particles are determined, and a particle size distribution diagram is generated based on all target LFO particles; based on the particle size distribution diagram and a predetermined target grading model, a particle size distribution result is output, wherein the particle size distribution result includes distribution parameters of all target LFO particles; and based on the particle size distribution result, the design parameters of the LFP battery are generated. It can be seen that the implementation of the present invention can intelligently determine the particle distribution information of the LFO particles in combination with the predetermined target grading model, and then generate corresponding battery design parameters based on the particle distribution information, which is conducive to improving the accuracy and efficiency of the generated battery design parameters, and thus is conducive to improving the design efficiency and design reliability of the required high energy density battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0065] Figure 1 It is a schematic flow chart of a design method of a high energy density battery disclosed in an embodiment of the present invention;

[0066] Figure 2 It is a flow chart of another method for designing a high energy density battery disclosed in an embodiment of the present invention;

[0067] Figure 3 It is a structural schematic diagram of a design device for a high energy density battery disclosed in an embodiment of the present invention;

[0068] Figure 4 It is a structural schematic diagram of another design device for a high energy density battery disclosed in an embodiment of the present invention;

[0069] Figure 5 It is a structural schematic diagram of another design device for a high energy density battery disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0072] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0073] The present invention discloses a design method and device for a high energy density battery, and a computer storage medium, which can intelligently determine the particle distribution information of LFO particles in combination with a grading model and then generate corresponding battery design parameters, so as to obtain the intelligence and accuracy of the high energy density battery, realize high solid electrode density and high energy density design, and thus help to improve the intelligence and accuracy of the design parameters for generating high energy density batteries. The following are detailed descriptions.

[0074] Embodiment 1

[0075] See also Figure 1 , Figure 1 1 is a flow chart of a design method for a high energy density battery disclosed in an embodiment of the present invention. Figure 1 The described high energy density battery design method can be applied to a high energy density battery design device, wherein the high energy density battery design device can be integrated in a local server or a cloud server, which is not limited in the embodiments of the present invention. Figure 1 As shown, the design method of the high energy density battery may include the following operations:

[0076] 101. Determine a number of target LFO particles, and generate a particle size distribution diagram based on all the target LFO particles.

[0077] In an embodiment of the present invention, optionally, the target LFO particles may include lithium iron-rich LFO particles; wherein, lithium iron-rich (LFO) particles are an important lithium supplement additive for the positive electrode of lithium-ion batteries, mainly used in the field of lithium-ion batteries, and can improve the problem of reduced first coulombic efficiency of the negative electrode, thereby improving the capacity, cycle life and energy density of the lithium battery.

[0078] In the embodiment of the present invention, optionally, the particle sizes corresponding to each target LFO particle may be different.

[0079] In an embodiment of the present invention, optionally, a particle size distribution diagram can be used to describe the size characteristics of the particle material. Further, the particle size distribution diagram can reflect the proportion of particles of different sizes in a particle group. For lithium iron phosphate LFO particles, the particle size distribution directly affects their application performance in lithium-ion batteries. Appropriate particle size distribution helps to improve the material's tap density, specific surface area, and lithium ion diffusion rate, thereby improving the battery's energy density, cycle stability, and charge and discharge efficiency.

[0080] 102. Based on the particle size distribution diagram and a predetermined target grading model, output a particle size distribution result.

[0081] In the embodiment of the present invention, the particle size distribution result includes distribution parameters of all target LFO particles.

[0082] In the embodiment of the present invention, optionally, the particle size distribution result may also include one or more of the particle size corresponding to each target LFO particle, the number of particles corresponding to each target LFO particle, and the proportion of particle sizes corresponding to all target LFO particles.

[0083] In the embodiment of the present invention, optionally, the predetermined target gradation model may include one of a three-level gradation model, a four-level gradation model, and a five-level gradation model.

[0084] 103. Generate the design parameters of LFP battery based on the particle size distribution results.

[0085] In the embodiment of the present invention, the design parameters of the LFP battery may optionally include one or more of the addition ratio, addition quantity, and addition distribution position of all target LFO particles. Further optionally, the design parameters of the LFP battery are used to control the particle size matching mode of the lithium iron phosphate and lithium iron-rich lithium oxide particles to achieve the most densely packed state of the particles, thereby achieving a design balance between high compaction and electrode thickness.

[0086] In the embodiment of the present invention, further optional, LFP battery, full name is lithium iron phosphate battery (Lithium Iron Phosphate Battery), is a lithium ion battery with lithium iron phosphate (LiFePO4) as the positive electrode material.

[0087] In the embodiment of the present invention, it is further optional that during the production of LFP batteries, LFO particles of different particle sizes and addition ratios are carefully selected, and premixed by dry mixing or gradually added during stirring to effectively disperse the particles, achieve optimal particle distribution, and realize high solid electrode density and high energy density design.

[0088] It can be seen that implementation Figure 1The described design method of a high energy density battery can determine several target LFO particles and generate a particle size distribution map based on all target LFO particles, output the particle size distribution result based on the particle size distribution map and the target grading model, and generate the design parameters of the LFP battery according to the particle size distribution result. The particle size distribution result can be output based on the intelligently generated particle size distribution map and the target grading model, which is conducive to improving the accuracy and reliability of the output particle size distribution result. The design parameters of the LFP battery are generated based on the particle size distribution result, so that the output slurry after mixing can achieve the optimal grading, and the pole piece compaction density can be further effectively improved under the original LFP powder compaction density, so as to achieve a high compaction density pole piece design and a high energy density battery design. The most densely packed state of the particles is achieved by controlling the particle size matching of lithium iron phosphate and lithium-rich lithium iron oxide particles, so as to achieve a design balance between high compaction and electrode thickness. The precise particle size distribution helps to improve the capacity, cycle stability and charge and discharge efficiency of the LFP battery, and the appropriate particle size grading can optimize the diffusion path of lithium ions and improve the energy density of the battery. Furthermore, the particle size distribution of the LFO particles can be controlled to improve the safety of battery operation.

[0089] Embodiment 2

[0090] See also Figure 2 , Figure 2 1 is a flow chart of another high energy density battery design method disclosed in an embodiment of the present invention. Figure 2 The described high energy density battery design method can be applied to a high energy density battery design device, wherein the high energy density battery design device can be integrated in a local server or a cloud server, which is not limited in the embodiments of the present invention. Figure 2 As shown, the design method of the high energy density battery may include the following operations:

[0091] 201. Determine a number of target LFO particles, and generate a particle size distribution diagram based on all the target LFO particles.

[0092] 202. Obtain design requirement information, and determine detailed requirement parameters of the LFP battery based on the design requirement information.

[0093] In an embodiment of the present invention, optionally, the design requirement information includes at least battery design requirement information; wherein the battery design requirement information may include one or more of capacity requirement information, voltage requirement information, discharge rate requirement information, charging rate requirement information, and energy density requirement information.

[0094] In an embodiment of the present invention, optionally, determining the fine requirement parameters of the LFP battery based on the design requirement information may include: extracting design fineness information from the design requirement information, determining the fine requirement according to the design fineness information, and determining the fine requirement parameters of the LFP battery according to the fine requirement.

[0095] In the embodiment of the present invention, optionally, the design requirement information may be acquired in real time, or may be acquired periodically according to a preset time period, or may be acquired when it is necessary to generate the design parameters of the LFP battery or generate the particle size distribution results, which is not specifically limited in the embodiment of the present invention.

[0096] 203. According to the refined demand parameters, a target gradation model matching the refined demand parameters is determined from the pre-determined gradation models.

[0097] In the embodiment of the present invention, optionally, the pre-determined gradation model may include a three-level gradation model, a four-level gradation model, and a five-level gradation model; wherein the volume proportion of primary particles with a particle size less than 150 nm corresponding to the three-level gradation model is 35-45%, the volume proportion of primary particles with a particle size greater than 200 nm and less than 500 nm corresponding to the three-level gradation model is 45-65%, and the volume proportion of primary particles with a particle size greater than 1000 nm and less than 1500 nm corresponding to the three-level gradation model is less than 10%;

[0098] The volume proportion of primary particles with a particle size less than 100nm corresponding to the four-level gradation model is 30-45%, the volume proportion of primary particles with a particle size greater than 100nm and less than 300nm corresponding to the four-level gradation model is 35-45%, the volume proportion of primary particles with a particle size greater than 500nm and less than 700nm corresponding to the four-level gradation model is 15-20%, and the volume proportion of primary particles with a particle size greater than 1000nm and less than 1500nm corresponding to the four-level gradation model is less than 8%;

[0099] The volume proportion of primary particles with a particle size less than 100nm corresponding to the five-level gradation model is 30-35%, the volume proportion of primary particles with a particle size greater than 100nm and less than 250nm corresponding to the five-level gradation model is 25-30%, the volume proportion of primary particles with a particle size greater than 250nm and less than 400nm corresponding to the five-level gradation model is 25-30%, the volume proportion of primary particles with a particle size greater than 600nm and less than 800nm ​​corresponding to the five-level gradation model is 5-15%, and the volume proportion of primary particles with a particle size greater than 1000nm and less than 1500nm corresponding to the five-level gradation model is <5%.

[0100] In the embodiment of the present invention, optionally, the fineness corresponding to the four-level gradation model is greater than the fineness corresponding to the three-level gradation model, and the fineness corresponding to the five-level gradation model is greater than the fineness corresponding to the four-level gradation model.

[0101] 204. Based on the particle size distribution diagram and a predetermined target gradation model, output a particle size distribution result.

[0102] 205. Generate design parameters of LFP battery based on particle size distribution results.

[0103] In the embodiment of the present invention, for the detailed description of step 201 and step 204-step 205, please refer to the other descriptions of step 101-step 103 in the first embodiment, and the embodiment of the present invention will not be repeated.

[0104] It can be seen that implementation Figure 2 The described design method of a high energy density battery can obtain design requirement information and determine the fine requirement parameters of the LFP battery based on the design requirement information, determine a target grading model matching the fine requirement parameters in a predetermined grading model according to the fine requirement parameters, and trigger the execution of an operation of outputting a particle size distribution result based on a particle size distribution diagram and a target grading model, can intelligently determine the fine requirement parameters of the LFP battery and then determine a matching target grading model, which is beneficial to improving the accuracy and reliability of determining the target grading model, and is beneficial to improving the intelligence and efficiency of determining the target grading model, and can output the corresponding particle size distribution result through the determined target grading model to generate the design parameters of the LFP battery, can make the powder compaction density and energy density corresponding to the design parameters of the generated LFP battery match the fine requirement parameters, which is beneficial to improving the accuracy and reliability of the design parameters of the generated LFP battery, and through reasonable material ratio and particle size distribution, it is helpful to improve the safety performance of the battery and reduce the risks such as thermal runaway, and by optimizing the material grading, it can improve the cycle stability of the battery and extend its service life, and can also help improve the safety of battery operation by controlling the particle size distribution of the LFO particles.

[0105] In an optional embodiment, after outputting the particle size distribution result based on the particle size distribution graph and the predetermined target gradation model, the method further includes:

[0106] According to the particle size distribution results, determine the current powder compaction density, and judge whether the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information;

[0107] When it is determined that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information, the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model is re-triggered.

[0108] In this optional embodiment, further optionally, when it is determined that the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information, the step of generating the design parameters of the LFP battery according to the particle size distribution result is directly triggered.

[0109] In this optional embodiment, optionally, the above-mentioned determination of whether the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information may include:

[0110] Calculate the density difference between the current powder compaction density and the powder compaction requirement density corresponding to the design requirement information, and determine whether the density difference is less than or equal to a preset density difference threshold;

[0111] When it is determined that the density difference is less than or equal to the preset density difference threshold, it is determined that the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information; when it is determined that the density difference is greater than the preset density difference threshold, it is determined that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information; or,

[0112] Determine whether the current powder compaction density is the same as the powder compaction requirement density corresponding to the design requirement information. When it is determined that the current powder compaction density is the same as the powder compaction requirement density corresponding to the design requirement information, determine that the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information; when it is determined that the current powder compaction density is not the same as the powder compaction requirement density corresponding to the design requirement information, determine that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information.

[0113] It can be seen that the implementation of this optional embodiment can determine the current powder compaction density according to the particle size distribution result, and judge whether the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information. If it does not match, the operation of outputting the particle size distribution result based on the particle size distribution diagram and the target grading model is re-triggered. By determining the compaction density of the powder based on the output results of the particle size distribution diagram and the target grading model, the density of the battery material can be controlled more accurately, and before outputting the particle size distribution result, it can be judged whether the current powder compaction density matches the powder requirement density corresponding to the design requirement information, which can be beneficial to the accuracy and reliability of the subsequent output of the particle size distribution result and the subsequent generation of the design parameters of the LFP battery, and by comparing the current powder compaction density with the powder compaction requirement density in the design requirement information, it can be beneficial to ensure that the performance of the material meets the expected design standards, and the particle size distribution and grading model can be adjusted to meet the design requirements. Through this circular feedback mechanism, the compaction density of the material can be continuously optimized, the overall quality and performance of the battery can be improved, and the safety of the battery operation can be improved by controlling the particle size distribution of the LFO particles.

[0114] In another optional embodiment, the design parameters of the LFP battery are generated according to the particle size distribution result, including:

[0115] Determine a particle size parameter of each target LFO particle, and determine a particle distribution parameter corresponding to each target LFO particle based on the particle size parameter of each target LFO particle and the particle size distribution result;

[0116] According to the particle distribution parameters corresponding to each target LFO particle, the design parameters of the LFP battery are generated; wherein the design parameters include the particle distribution information corresponding to all the target LFO particles.

[0117] In this optional embodiment, optionally, the particle size parameter of each target LFO particle includes at least a particle size value corresponding to each target LFO particle.

[0118] In this optional embodiment, optionally, the determining of the particle distribution parameters corresponding to each target LFO particle based on the particle size parameters and the particle size distribution result of each target LFO particle may include:

[0119] Based on the particle size parameters and particle size distribution results of each target LFO particle, a simulated distribution operation is performed on all target LFO particles to obtain a simulated distribution result; it is determined whether the simulated distribution result matches the preset particle distribution condition; when it is determined that the simulated distribution result matches the preset particle distribution condition, the particle distribution parameters corresponding to each target LFO particle are determined based on the simulated distribution result.

[0120] In this optional embodiment, further optionally, when it is determined that the simulated distribution result does not match the preset particle distribution condition, the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target grading model is re-triggered.

[0121] In this optional embodiment, optionally, the particle distribution parameters corresponding to each target LFO particle include at least the position distribution parameters corresponding to the target LFO particle.

[0122] In this optional embodiment, further optionally, the particle distribution information corresponding to all target LFO particles includes at least one or more of particle quantity distribution information, particle size position distribution information, and particle size ratio distribution information corresponding to the LFO particles.

[0123] It can be seen that the implementation of this optional embodiment can determine the particle size parameters of each target LFO particle and determine the particle distribution parameters corresponding to each target LFO particle based on the particle size parameters of each target LFO particle and the particle size distribution results. The design parameters of the LFP battery are generated according to the particle distribution parameters corresponding to each target LFO particle. By determining the particle size parameters of each target LFO particle, it is possible to achieve fine control of the battery material and make the battery design more accurate. The determination of the particle distribution parameters helps to optimize the internal structure and layout of the battery, thereby improving the performance of the battery. By accurately controlling the particle distribution, the diffusion path of lithium ions can be optimized, and the energy density and cycle stability of the battery can be improved. Accurate design can also improve the utilization rate of materials, which can not only reduce the production cost of the battery but also further improve the economic benefits. Detailed particle distribution information is helpful for quality control in the production process, ensuring the consistency and stability of the battery. By accurately controlling the particle distribution, the production process can be simplified and the production efficiency can be improved. Reasonable particle distribution design can reduce the capacity decay of the battery during the cycle. By optimizing the particle distribution, the stress inside the battery can be reduced and the rate of battery aging can be reduced. Furthermore, by controlling the particle size distribution of the LFO particles, it can be beneficial to improve the safety of battery operation.

[0124] In yet another optional embodiment, based on the particle size distribution diagram and a predetermined target gradation model, a particle size distribution result is output, including:

[0125] Inputting the particle size distribution diagram into a predetermined target grading model to obtain a model output result, and determining the particle distribution results of all target LFO particles based on the model output result;

[0126] Based on the particle distribution results of all target LFO particles, a particle size distribution result is output.

[0127] In this optional embodiment, optionally, the model output result includes at least a particle distribution parameter corresponding to each target LFO particle included in the particle size distribution diagram; wherein the particle distribution parameter corresponding to each target LFO particle includes a particle distribution position parameter.

[0128] In this optional embodiment, optionally, determining the particle distribution results of all target LFO particles based on the model output results may include: determining the particle distribution parameters corresponding to each target LFO particle based on the model output results, and generating the particle distribution results of all target LFO particles based on the particle distribution parameters corresponding to each target LFO particle.

[0129] It can be seen that the implementation of this optional embodiment can input the particle size distribution diagram into the target grading model to obtain the model output result, and determine the particle distribution results of all target LFO particles according to the model output result, and output the particle size distribution result based on the particle distribution results of all target LFO particles. By using the target grading model, the particle size distribution of LFO particles can be predicted more accurately, which is conducive to improving the accuracy and reliability of determining the particle distribution results of the target LFO particles, thereby facilitating improving the accuracy and reliability of the subsequent output particle size distribution results. The model output results can provide a scientific basis for decision makers, which is conducive to further improving the accuracy and reliability of the subsequent output particle size distribution results, thereby achieving fine control of battery materials and making battery design more accurate. Accurate particle size distribution can help improve the capacity, cycle stability and charge and discharge efficiency of LFP batteries, and suitable particle size grading can optimize the diffusion path of lithium ions and improve the energy density of batteries. Furthermore, the particle size distribution of LFO particles can be controlled to help improve the safety of battery operation.

[0130] In yet another optional embodiment, after determining that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information, the method further includes:

[0131] Analyze the density difference between the current powder compaction density and the powder compaction required density corresponding to the design requirement information;

[0132] Based on the density difference, the target gradation model is updated, and the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model is triggered.

[0133] In this optional embodiment, optionally, the powder compaction requirement density corresponding to the design requirement information can be a high energy density of 420 to 480Wh / L.

[0134] In this optional embodiment, optionally, updating the target gradation model based on the density difference may include: determining a target adjustment model that matches the density difference from all predetermined gradation models based on the density difference, and updating the target gradation model based on the target adjustment model.

[0135] In this optional embodiment, optionally, for example, if the current target gradation model is a three-level gradation model and the density difference is relatively large, the target gradation model is updated to a five-level gradation model.

[0136] It can be seen that the implementation of this optional embodiment can analyze the density difference between the current powder compaction density and the powder compaction requirement density corresponding to the design requirement information, and update the target gradation model based on the density difference and trigger the execution of the operation of outputting the particle size distribution result based on the particle size distribution diagram and the target gradation model. By analyzing the difference between the current powder compaction density and the design requirement density, the compaction density of the powder can be more accurately controlled to ensure that the compaction density of the powder meets the design requirements. The target gradation model is updated based on the density difference, which can make the model more in line with the actual production conditions and improve the prediction accuracy and practicality of the model. By adjusting the particle size distribution to adapt to the density requirements, the consistency and quality of the final product can be improved. When the density does not match, the production parameters can be quickly responded and adjusted, which enhances the flexibility and adaptability of the production process. The precise particle size distribution helps to improve the capacity, cycle stability and charge and discharge efficiency of the LFP battery, and the appropriate particle size grading can optimize the diffusion path of lithium ions and improve the energy density of the battery. It can also help improve the safety of battery operation by controlling the particle size distribution of the LFO particles.

[0137] In yet another optional embodiment, according to the fine demand parameter, determining a target gradation model matching the fine demand parameter from the pre-determined gradation model includes:

[0138] According to the detailed demand parameters, the particle grading ratio parameters of all target LFO particles are determined;

[0139] According to the particle gradation ratio parameters, the particle ratio parameters corresponding to all target LFO particles are determined, and a target gradation model matching the particle ratio parameters is determined from all pre-determined gradation models.

[0140] In this optional embodiment, optionally, the particle grading ratio parameters of all target LFO particles include at least the particle size ratio parameters corresponding to all target LFO particles, wherein the particle size ratio parameters include the number of particles corresponding to each particle size, and can also include the particle content of each particle size range, and key indicators such as average particle size, standard deviation, D10, D50, and D90 are obtained by analysis.

[0141] In this optional embodiment, optionally, determining the particle ratio parameters corresponding to all target LFO particles according to the particle grading ratio parameters may include: determining the particle ratio parameters according to the particle grading ratio parameters, and determining the particle ratio parameters corresponding to all target LFO particles based on the particle ratio parameters.

[0142] In this optional embodiment, optionally, the above-mentioned determination of a target grading model matching the particle proportion parameters from all predetermined grading models may include: determining a particle proportion accuracy parameter based on the particle proportion parameters, and determining a matching target grading model based on the particle proportion accuracy parameters.

[0143] It can be seen that the implementation of this optional embodiment can determine the particle grading ratio parameters of all target LFO particles according to the fine demand parameters, determine the particle ratio parameters corresponding to all target LFO particles according to the particle grading ratio parameters and determine a matching target grading model in all grading models, and can ensure the precise matching of the grading ratio parameters of the LFO particles with the fine demand parameters based on the particle grading ratio parameters, and optimize the charge and discharge performance, cycle stability and energy density of the battery by precisely controlling the particle grading, and can determine the matching target grading model by the particle ratio parameters, and can match the accuracy corresponding to the target grading model determined by the particle ratio parameters. , which is conducive to realizing the rapid determination of the grading model, thereby helping to improve the accuracy and reliability of the grading model, as well as helping to improve the intelligence and efficiency of the grading model. By using the target grading model, the particle size distribution of the LFO particles can be predicted more accurately, which is conducive to improving the accuracy and reliability of the particle distribution results of the target LFO particles, thereby helping to improve the accuracy and reliability of the subsequent output particle size distribution results. The model output results can provide a scientific basis for decision makers, which is conducive to further improving the accuracy and reliability of the subsequent output particle size distribution results, and further, by controlling the particle size distribution of the LFO particles, it can help to improve the safety of battery operation.

[0144] In yet another optional embodiment, based on all target LFO particles, a particle size distribution map is generated, including:

[0145] Obtaining a particle scanning result obtained by scanning a target LFO particle with an SEM, and generating particle normal distribution information based on the particle scanning result;

[0146] Generate a particle size distribution diagram based on the particle normal distribution information.

[0147] In this optional embodiment, optionally, the particle normal distribution information may include the distribution information of all target LFO particles, wherein, for lithium iron-rich LFO particles, the particle size distribution should be normally distributed or close to normally distributed to ensure the uniformity of particle size; further, the particle size distribution characteristic values ​​of LFO particles may include parameters such as D10, D50 and D90, which respectively represent the particle diameters corresponding to the cumulative distribution of 10%, 50% and 90%; wherein, D10: indicates that 10% of the particle diameters are smaller than this value, reflecting the proportion of fine particles in the particle group; D50 (median diameter): indicates that 50% of the particle diameters are smaller than this value, which is the central trend indicator of the particle size distribution. For LFO particles, D50 is generally recommended to be in the range of 4.0μm to 18.0μm; D90: indicates that 90% of the particle diameters are smaller than this value, reflecting the proportion of coarse particles in the particle group.

[0148] In this optional embodiment, optionally, SEM usually refers to a scanning electron microscope (Scanning Electron Microscope), which is a high-resolution microscope that can provide detailed images of the sample surface. SEM uses an electron beam instead of a light beam to scan the sample, so it can achieve higher magnification and resolution than traditional optical microscopes.

[0149] In this optional embodiment, optionally, the particle normal distribution information at least includes normal distribution image information corresponding to all target LFO particles.

[0150] It can be seen that the implementation of this optional embodiment can obtain the particle scanning results obtained by SEM scanning the target LFO particles, and generate particle normal distribution information based on the particle scanning results, and generate a particle size distribution map based on the particle normal distribution information, which can provide a high-resolution particle surface image through SEM, which is helpful to accurately measure the particle size and shape of the particles, and the particle size distribution map generated by the SEM scanning results can intuitively display the distribution of particle size, which is beneficial to improve the accuracy and reliability of the obtained particle normal distribution information, thereby helping to improve the accuracy and reliability of the obtained particle size distribution map, and based on the SEM scanning results and the particle size distribution map, data-driven decisions can be made to improve the scientificity and accuracy of the decision-making, and the particle size distribution map provides an intuitive reference for quality control, which is helpful to timely discover and solve problems in the production process, and can adjust the particle grading based on the particle size distribution map, thereby helping to improve the intelligence and accuracy of the design parameters for generating LFP batteries, and further can also help to improve the safety of battery operation by controlling the particle size distribution of LFO particles.

[0151] Embodiment 3

[0152] See also Figure 3 , Figure 3Schematic diagram of a design device for a high energy density battery disclosed in an embodiment of the present invention. Figure 3 As shown, the design device of the high energy density battery may include:

[0153] A determination module 301 is used to determine a number of target LFO particles;

[0154] A generating module 302, for generating a particle size distribution map based on all target LFO particles;

[0155] An output module 303 is used to output a particle size distribution result based on the particle size distribution diagram and a predetermined target gradation model, wherein the particle size distribution result includes distribution parameters of all target LFO particles;

[0156] The generation module 302 is further used to generate design parameters of the LFP battery according to the particle size distribution result.

[0157] It can be seen that implementation Figure 3 The described device can determine several target LFO particles and generate a particle size distribution map based on all target LFO particles, output the particle size distribution result based on the particle size distribution map and the target grading model, and generate the design parameters of the LFP battery according to the particle size distribution result. The particle size distribution result can be output based on the intelligently generated particle size distribution map and the target grading model, which is conducive to improving the accuracy and reliability of the output particle size distribution result. The design parameters of the LFP battery are generated based on the particle size distribution result, so that the output slurry after mixing can achieve the optimal grading, and the pole piece compaction density is further effectively improved under the original LFP powder compaction density, so as to achieve a high compaction density pole piece design and a high energy density battery design. The most densely packed state of the particles is achieved by controlling the particle size matching mode of lithium iron phosphate and lithium iron-rich lithium ferrate, so as to achieve a design balance between high compaction and electrode thickness, and the capacity, cycle stability and charge and discharge efficiency of the LFP battery are improved through precise particle size distribution, and the diffusion path of lithium ions can be optimized through appropriate particle size grading, so as to improve the energy density of the battery, and further, the particle size distribution of the LFO particles can be controlled to improve the safety of battery operation.

[0158] In an optional embodiment, if Figure 4 As shown, the device also includes:

[0159] The acquisition module 304 is used to acquire design requirement information before the output module 303 outputs the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model;

[0160] The determination module 301 is further used to determine the detailed requirement parameters of the LFP battery based on the design requirement information;

[0161] The determination module 301 is also used to determine a target gradation model that matches the fine demand parameters in the predetermined gradation model according to the fine demand parameters, and trigger the output module 303 to perform an operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model.

[0162] It can be seen that implementation Figure 4 The described device can obtain design requirement information and determine the fine requirement parameters of the LFP battery based on the design requirement information, determine the target grading model matching the fine requirement parameters in the pre-determined grading model according to the fine requirement parameters, and trigger the execution of the operation of outputting the particle size distribution result based on the particle size distribution diagram and the target grading model, can intelligently determine the fine requirement parameters of the LFP battery and then determine the matching target grading model, which is conducive to improving the accuracy and reliability of determining the target grading model, as well as improving the intelligence and efficiency of determining the target grading model, and can output the corresponding particle size distribution result through the determined target grading model to generate the design parameters of the LFP battery, which can make the powder compaction density and energy density corresponding to the design parameters of the generated LFP battery match the fine requirement parameters, which is conducive to improving the accuracy and reliability of the design parameters of the generated LFP battery, and through reasonable material ratio and particle size distribution, it is helpful to improve the safety performance of the battery and reduce the risks such as thermal runaway, and by optimizing the material grading, it can improve the cycle stability of the battery and extend its service life, and it can also help improve the safety of battery operation by controlling the particle size distribution of the LFO particles.

[0163] In another optional embodiment, Figure 4 As shown, the determination module 301 is further used to determine the current powder compaction density according to the particle size distribution result after the output module 303 outputs the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model;

[0164] The device also includes:

[0165] The judgment module 305 is used to judge whether the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information; when it is judged that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information, the output module 303 is re-triggered to execute the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target grading model.

[0166] It can be seen that implementation Figure 4The described device can determine the current powder compaction density according to the particle size distribution result, and judge whether the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information. If it does not match, the operation of outputting the particle size distribution result based on the particle size distribution diagram and the target grading model is re-triggered. By determining the compaction density of the powder based on the output results of the particle size distribution diagram and the target grading model, the density of the battery material can be controlled more accurately, and before outputting the particle size distribution result, it can be judged whether the current powder compaction density matches the powder requirement density corresponding to the design requirement information, which can be beneficial to the accuracy and reliability of the subsequent output of the particle size distribution result and the subsequent generation of the design parameters of the LFP battery. By comparing the current powder compaction density with the powder compaction requirement density in the design requirement information, it can be beneficial to ensure that the performance of the material meets the expected design standards, and the particle size distribution and grading model can be adjusted to meet the design requirements. Through this circular feedback mechanism, the compaction density of the material can be continuously optimized, the overall quality and performance of the battery can be improved, and the safety of the battery operation can be improved by controlling the particle size distribution of the LFO particles.

[0167] In yet another optional embodiment, Figure 4 As shown, the specific method of generating the design parameters of the LFP battery by the generation module 302 according to the particle size distribution result includes:

[0168] Determine a particle size parameter of each target LFO particle, and determine a particle distribution parameter corresponding to each target LFO particle based on the particle size parameter of each target LFO particle and the particle size distribution result;

[0169] According to the particle distribution parameters corresponding to each target LFO particle, the design parameters of the LFP battery are generated; wherein the design parameters include the particle distribution information corresponding to all the target LFO particles.

[0170] It can be seen that implementation Figure 4The described device can determine the particle size parameters of each target LFO particle and determine the particle distribution parameters corresponding to each target LFO particle based on the particle size parameters of each target LFO particle and the particle size distribution results, and generate the design parameters of the LFP battery according to the particle distribution parameters corresponding to each target LFO particle. By determining the particle size parameters of each target LFO particle, it is possible to achieve fine control of the battery material and make the battery design more accurate. The determination of the particle distribution parameters helps to optimize the internal structure and layout of the battery, thereby improving the performance of the battery, and by accurately controlling the particle distribution, the energy density and cycle stability of the battery are improved. Accurate design can also improve the utilization rate of materials, which can not only reduce the production cost of the battery but also further improve the economic benefits. Detailed particle distribution information helps quality control in the production process, ensures the consistency and stability of the battery, and through accurate control of the particle distribution, simplifies the production process and improves production efficiency. Reasonable particle distribution design can reduce the capacity decay of the battery during the cycle. By optimizing the particle distribution, the stress inside the battery can be reduced and the rate of battery aging can be reduced. Furthermore, by controlling the particle size distribution of the LFO particles, it is beneficial to improve the safety of battery operation.

[0171] In yet another optional embodiment, Figure 4 As shown, the output module 303 outputs the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model in a specific manner including:

[0172] Inputting the particle size distribution diagram into a predetermined target grading model to obtain a model output result, and determining the particle distribution results of all target LFO particles based on the model output result;

[0173] Based on the particle distribution results of all target LFO particles, a particle size distribution result is output.

[0174] It can be seen that implementation Figure 4The described device can input the particle size distribution diagram into the target grading model to obtain the model output result, and determine the particle distribution results of all target LFO particles according to the model output result, and output the particle size distribution result based on the particle distribution results of all target LFO particles. By using the target grading model, the particle size distribution of LFO particles can be predicted more accurately, which is conducive to improving the accuracy and reliability of determining the particle distribution results of the target LFO particles, thereby helping to improve the accuracy and reliability of the subsequent output particle size distribution results. The model output results can provide a scientific basis for decision makers, which is conducive to further improving the accuracy and reliability of the subsequent output particle size distribution results, so that fine control of battery materials can be achieved to make battery design more accurate. Accurate particle size distribution can help improve the capacity, cycle stability and charge and discharge efficiency of LFP batteries, and suitable particle size grading can optimize the diffusion path of lithium ions and improve the energy density of batteries. Furthermore, the particle size distribution of LFO particles can be controlled to help improve the safety of battery operation.

[0175] In yet another optional embodiment, Figure 4 As shown, the device also includes:

[0176] The analysis module 306 is used to analyze the density difference between the current powder compaction density and the powder compaction density corresponding to the design requirement information after the judgment module 305 determines that the current powder compaction density does not match the powder compaction density corresponding to the design requirement information;

[0177] The updating module 307 is used to update the target gradation model based on the density difference, and trigger the output module 303 to execute the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model.

[0178] It can be seen that implementation Figure 4The described device can analyze the density difference between the current powder compaction density and the powder compaction requirement density corresponding to the design requirement information, and update the target gradation model based on the density difference and trigger the execution of the operation of outputting the particle size distribution result based on the particle size distribution diagram and the target gradation model. By analyzing the difference between the current powder compaction density and the design requirement density, the compaction density of the powder can be more accurately controlled to ensure that the compaction density of the powder meets the design requirements. Updating the target gradation model based on the density difference can make the model more in line with actual production conditions, improve the prediction accuracy and practicality of the model, and improve the consistency and quality of the final product by adjusting the particle size distribution to adapt to the density requirements. When the density does not match, the production parameters can be quickly responded and adjusted, thereby enhancing the flexibility and adaptability of the production process. The precise particle size distribution helps to improve the capacity, cycle stability and charge and discharge efficiency of the LFP battery, and the appropriate particle size grading can optimize the diffusion path of lithium ions and improve the energy density of the battery. Furthermore, the particle size distribution of the LFO particles can be controlled to help improve the safety of battery operation.

[0179] In yet another optional embodiment, Figure 4 As shown, the specific manner in which the determination module 301 determines the target gradation model matching the fine demand parameter in the pre-determined gradation model according to the fine demand parameter includes:

[0180] According to the detailed demand parameters, the particle grading ratio parameters of all target LFO particles are determined;

[0181] According to the particle gradation ratio parameters, the particle ratio parameters corresponding to all target LFO particles are determined, and a target gradation model matching the particle ratio parameters is determined from all pre-determined gradation models.

[0182] It can be seen that implementation Figure 4The described device can determine the particle grading ratio parameters of all target LFO particles according to the fine demand parameters, determine the particle ratio parameters corresponding to all target LFO particles according to the particle grading ratio parameters and determine a matching target grading model among all grading models, can ensure the precise matching of the grading ratio parameters of the LFO particles with the fine demand parameters based on the particle grading ratio parameters, and can optimize the charge and discharge performance, cycle stability and energy density of the battery by precisely controlling the particle grading, and can determine the matching target grading model through the particle ratio parameters, and can match the accuracy corresponding to the determined target grading model with the accuracy corresponding to the particle ratio parameters, which is beneficial It is useful for realizing rapid determination of the grading model, thereby facilitating improvement of the accuracy and reliability of the grading model, as well as improvement of the intelligence and efficiency of the grading model. By using the target grading model, the particle size distribution of the LFO particles can be predicted more accurately, which is helpful for improving the accuracy and reliability of the particle distribution results of the target LFO particles, thereby helping to improve the accuracy and reliability of the subsequent output particle size distribution results. The model output results can provide a scientific basis for decision makers, which is helpful for further improving the accuracy and reliability of the subsequent output particle size distribution results, and further, the particle size distribution of the LFO particles can be controlled to improve the safety of battery operation.

[0183] In yet another optional embodiment, Figure 4 As shown, the specific manner in which the generation module 302 generates the particle size distribution diagram based on all target LFO particles includes:

[0184] Obtaining a particle scanning result obtained by scanning a target LFO particle with an SEM, and generating particle normal distribution information based on the particle scanning result;

[0185] Generate a particle size distribution diagram based on the particle normal distribution information.

[0186] It can be seen that implementation Figure 4The described device can perform a scanning operation on all target LFO particles through SEM to obtain particle scanning results, and generate particle normal distribution information based on the particle scanning results, and generate a particle size distribution map based on the particle normal distribution information. It can provide a high-resolution particle surface image through SEM, which is helpful to accurately measure the particle size and shape of the particles. The particle size distribution map generated by the SEM scanning results can intuitively display the distribution of particle size, which is helpful to improve the accuracy and reliability of the obtained particle normal distribution information, thereby helping to improve the accuracy and reliability of the obtained particle size distribution map. Based on the SEM scanning results and the particle size distribution map, data-driven decisions can be made to improve the scientificity and accuracy of the decision-making. The particle size distribution map provides an intuitive reference for quality control, which is helpful to timely discover and solve problems in the production process and can adjust the particle grading based on the particle size distribution map, thereby helping to improve the intelligence and accuracy of the design parameters for generating LFP batteries. Furthermore, it can also help to improve the safety of battery operation by controlling the particle size distribution of LFO particles.

[0187] Embodiment 4

[0188] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of a structure of another high energy density battery design device disclosed in an embodiment of the present invention. Figure 5 As shown, the design device of the high energy density battery may include:

[0189] A memory 401 storing executable program codes;

[0190] a processor 402 coupled to the memory 401;

[0191] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the method for designing a high energy density battery described in the first embodiment of the present invention or the second embodiment of the present invention.

[0192] Embodiment 5

[0193] An embodiment of the present invention discloses a computer storage medium storing computer instructions. When the computer instructions are called, they are used to execute the steps in the high energy density battery design method described in Embodiment 1 or Embodiment 2 of the present invention.

[0194] Embodiment 6

[0195] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the high energy density battery design method described in Example 1 or Example 2.

[0196] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0197] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0198] Finally, it should be noted that the design method and device of a high energy density battery and the computer storage medium disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A design method for a high energy density battery, characterized in that: The method comprises: determining a plurality of target LFO particles, and generating a particle size distribution diagram based on all of the target LFO particles; Based on the particle size distribution graph and a predetermined target grading model, outputting a particle size distribution result, wherein the particle size distribution result includes distribution parameters of all the target LFO particles; Based on the particle size distribution results, the design parameters of the LFP battery are generated.

2. The design method of a high energy density battery according to claim 1, characterized in that: Before outputting the particle size distribution result based on the particle size distribution graph and the predetermined target gradation model, the method further includes: Acquire design requirement information, and determine detailed requirement parameters of the LFP battery based on the design requirement information; According to the fine demand parameters, a target grading model matching the fine demand parameters is determined in the predetermined grading model, and the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target grading model is triggered.

3. The design method of a high energy density battery according to claim 2, characterized in that: After outputting the particle size distribution result based on the particle size distribution graph and the predetermined target gradation model, the method further includes: Determine the current powder compaction density according to the particle size distribution result, and judge whether the current powder compaction density matches the powder compaction requirement density corresponding to the design requirement information; When it is determined that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information, the operation of outputting the particle size distribution result based on the particle size distribution diagram and the predetermined target grading model is retriggered.

4. The design method of a high energy density battery according to claim 1, characterized in that: Generating the design parameters of the LFP battery according to the particle size distribution result includes: Determine a particle size parameter of each of the target LFO particles, and determine a particle distribution parameter corresponding to each of the target LFO particles based on the particle size parameter of each of the target LFO particles and the particle size distribution result; According to the particle distribution parameters corresponding to each of the target LFO particles, the design parameters of the LFP battery are generated; wherein the design parameters include the particle distribution information corresponding to all the target LFO particles.

5. The design method of a high energy density battery according to claim 1, characterized in that: The outputting of the particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model includes: Inputting the particle size distribution diagram into a predetermined target grading model to obtain a model output result, and determining the particle distribution results of all the target LFO particles according to the model output result; Based on the particle distribution results of all the target LFO particles, a particle size distribution result is output.

6. The design method of a high energy density battery according to claim 3, characterized in that: When it is determined that the current powder compaction density does not match the powder compaction requirement density corresponding to the design requirement information, the method further includes: Analyzing the density difference between the current powder compaction density and the powder compaction required density corresponding to the design requirement information; Based on the density difference, the target gradation model is updated, and the operation of outputting a particle size distribution result based on the particle size distribution diagram and the predetermined target gradation model is triggered.

7. The design method of a high energy density battery according to claim 2, characterized in that: The step of determining a target gradation model matching the fine demand parameter from a predetermined gradation model according to the fine demand parameter includes: According to the fine demand parameters, determining the particle grading ratio parameters of all the target LFO particles; According to the particle gradation ratio parameter, the particle ratio parameters corresponding to all the target LFO particles are determined, and a target gradation model matching the particle ratio parameter is determined from all pre-determined gradation models.

8. The design method of a high energy density battery according to claim 1, characterized in that: The step of generating a particle size distribution diagram based on all the target LFO particles comprises: Acquire a particle scanning result obtained by scanning the target LFO particles by SEM, and generate particle normal distribution information based on the particle scanning result; Based on the normal distribution information of the particles, a particle size distribution graph is generated.

9. A design device for a high energy density battery, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the high energy density battery design method as described in any one of claims 1-8.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the design method of a high energy density battery as described in any one of claims 1-8.

Images