Production optimization method, device and equipment for lithium battery products and medium

Abstract

The invention discloses a lithium battery product-oriented production optimization method, device and equipment and a medium, and belongs to the field of lithium battery production and manufacturing. The method comprises the following steps: firstly, obtaining a carbon emission factor and a material input range of each production flow in a product manufacturing stage, constructing a target function for minimizing the total carbon emission according to the carbon emission factor and the material input range, and comprehensively considering actual constraints such as equipment continuous operation, cost control and reasonable material consumption to establish a lithium battery product production optimization model; and finally, the material input amount of each process is calculated through a solver so as to guide production adjustment. Therefore, by implementing the method, the problem that the production efficiency of the link is difficult to improve while the link with the maximum emission reduction potential is subjected to emission reduction in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of production optimization of lithium battery products, and particularly to a production optimization method, device, equipment and medium for lithium battery products. Background Art

[0002] In the field of production optimization of lithium battery products, enterprises usually focus on how to improve the performance, safety and production efficiency of batteries, while striving to reduce production costs. This field involves a complete chain from raw material processing, electrode preparation, cell assembly to charging and discharging. The process parameters and equipment selection in each link will directly affect the quality and economy of the final product. In recent years, with the global attention to climate change, how to reduce energy consumption and greenhouse gas emissions in the production process has also become an important research direction in this field.

[0003] In the prior art, many carbon footprint accounting methods mainly focus on the direct energy consumption in the product manufacturing process itself, such as calculating the emissions corresponding to the consumption of electricity and natural gas in the factory. In addition, after obtaining the carbon emission data, most of the existing production optimization methods often only make local adjustments, such as replacing the energy-saving model of a single device or adjusting the temperature setting of a certain process. Due to the lack of an analysis tool for jointly modeling raw material selection, energy structure, process coupling and overall carbon emissions, these optimization measures are usually isolated and fragmentary. Therefore, there are two prominent problems in the current lithium battery industry: one is that the carbon footprint accounting is incomplete and inconsistent, resulting in enterprises being unable to accurately evaluate the environmental performance of their own products and difficult to meet the increasingly strict product carbon label requirements internationally; the other is that there is a lack of systematic optimization means for carbon emission reduction in the production process. Enterprises often invest resources in technological transformation, but cannot accurately locate the links with the greatest emission reduction potential, and the overall carbon reduction effect is limited and may conflict with the production cost control target. Summary of the Invention

[0004] The present invention provides a production optimization method, device, equipment and medium for lithium battery products, which can solve the problem in the prior art that it is difficult to reduce emissions in the link with the greatest emission reduction potential while improving the production efficiency of this link.

[0005] In a first aspect, an embodiment of the present invention provides a production optimization method for lithium battery products, including: Obtain the carbon emission factors of each production process and the upper and lower limits corresponding to the input amounts of each material in the product manufacturing stage of the lithium battery products; According to the carbon emission factors of each production process, using the input amounts of each material as decision variables, and then constructing an objective function to minimize the total carbon emissions in the product manufacturing stage of the lithium battery products; Based on the upper and lower limits corresponding to the input of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input of each material. Based on the objective function, the continuous production constraints, the production cost constraints, and the input constraints corresponding to the input quantities of each material, an optimization model for lithium battery product production is established. The lithium battery product production optimization model is solved by a preset solver to obtain the material input of each production process of the lithium battery product, and production optimization is carried out based on the material input of each production process.

[0006] This application provides a data foundation for the entire optimization process by acquiring the specific carbon emission factors of each production stage in the product manufacturing process. This allows enterprises to clearly see the emission contribution of different processes, changing the previous situation of crude calculation. Secondly, it sets the consumption of various materials in production as adjustable decision variables and directly establishes a mathematical function with the goal of reducing total carbon emissions. This approach transforms the emission reduction target into a clear and calculable engineering problem. Next, this application simultaneously considers the practical requirements of continuous equipment operation, the enterprise's production cost constraints, and the reasonable scope of use of various materials. By establishing these constraints, it ensures that the final optimized solution is truly feasible. Then, this application integrates the objective and constraints to establish a complete optimization model. This model systematically describes the complex relationship between production, emissions, and costs, enabling enterprises to find the optimal solution from a global perspective. Finally, through calculation using a standard solver, this application can quickly obtain the optimal solution for how much material should be input in each process. The factory can directly adjust the parameter settings on the production line based on these results. This helps companies achieve low-carbon production processes in a systematic and data-driven manner, effectively reducing carbon emissions while ensuring normal production and cost control.

[0007] As a preferred example of the first aspect, before obtaining the carbon emission factors of each production process in the product manufacturing stage of the lithium battery product, the method further includes: Obtain the full life-cycle carbon footprint accounting data of the lithium battery product; By comparing the carbon emissions at each stage in the full life cycle carbon footprint accounting data, the product manufacturing stage is identified as the target for production optimization.

[0008] In this preferred example, carbon footprint data of batteries is acquired throughout the entire process from raw materials to disposal. This step helps companies establish a complete product carbon emission profile, moving beyond the previous limitation of focusing only on specific stages. Then, by systematically comparing the carbon emissions at each stage, it becomes clear which stage contributes the most. Finally, based on the above comparison results, this application can clearly identify the product manufacturing stage as the primary optimization target.

[0009] As a preferred example of the first aspect, the construction of an objective function based on the carbon emission factors of each of the production processes, using the input amount of each material as a decision variable, and then minimizing the total carbon emissions in the product manufacturing stage of the lithium battery product, includes: Based on the carbon emission factors of each production process, and using the input amount of each material as a decision variable, an objective function is constructed to minimize the total carbon emissions during the product manufacturing stage of the lithium battery product. The expression of the objective function is as follows: in, This refers to the total carbon emissions during the manufacturing phase of the lithium battery product. Let be the carbon emission factor corresponding to the i-th material in the j-th production process. Let i be the amount of material i input in the j-th production process.

[0010] In this preferred example, the objective function points out the direction for emission reduction to the enterprise. Based on the results, the enterprise can adjust the process parameters of high-emission processes or optimize the usage of specific materials, thus transforming the overall goal of reducing carbon emissions into specific and actionable improvement actions.

[0011] As a preferred example of the first aspect, the establishment of continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material based on the upper and lower limits corresponding to the input quantities of each material includes: Based on the upper and lower limits corresponding to the input quantities of each material, continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material are established. The expression for the continuous production constraints is as follows: in, For the electrical power input corresponding to the j-th production process, Let N be the total electricity input during the product manufacturing stage, and N be the number of production processes in the product manufacturing stage. and These represent the lower and upper limits of the operating power of the production equipment, respectively.

[0012] In this preferred example, the first requirement is that the total power consumption of all processes must equal a reasonable total power consumption. This total power consumption is averaged over production time, and the average value must fall within the equipment's allowable power range. This ensures that the machine will not operate under overload or idle inefficiently. Furthermore, this application describes the inherent correlation of power consumption between different processes using mathematical formulas. For example, the power consumption of subsequent main processes must maintain a reasonable ratio with the power consumption of the preceding pre-processing process. This reflects that in actual production, the processing depth of the preceding process directly affects the energy consumption required by the subsequent process. These detailed regulations bring a key benefit: they ensure that the optimal solution calculated by the model is not just a theoretical figure, but an executable solution that fully considers equipment capacity, production rhythm, and process connections.

[0013] As a preferred example of the first aspect, the establishment of continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material based on the upper and lower limits corresponding to the input quantities of each material includes: Based on the upper and lower limits corresponding to the input quantities of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material. The expression for the production cost constraint is as follows: in, This is the price conversion factor. As the upper limit of production costs, Let be the total input amount of the i-th material.

[0014] In this preferred example, the added cost constraint provides an important safety net for the enterprise. While pursuing carbon emission reduction, it clearly sets a cost red line. This condition is expressed by a simple formula: multiplying the consumption of all materials by their respective price coefficients, the total cost cannot exceed the enterprise's set budget limit. This is equivalent to embedding a cost controller within the optimization model.

[0015] This approach offers a direct benefit: it ensures that the resulting low-carbon production solution is within the company's cost tolerance. Companies don't need to worry about significantly increasing environmental spending, rendering the solution unfeasible.

[0016] As a preferred example of the first aspect, the establishment of continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material based on the upper and lower limits corresponding to the input quantities of each material includes: Based on the upper and lower limits corresponding to the input of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input of each material; among them, the input constraints corresponding to the input of each material include natural gas input constraints, phosphoric acid input constraints, copper foil input constraints, polyethylene input constraints, and methanol input constraints.

[0017] In this preferred example, establishing input constraints corresponding to the input amounts of each material ensures the stability of battery product quality. The input amounts of important auxiliary materials such as polyethylene and methanol are fixed within an optimal range, which ensures that each batch of products meets consistent performance standards and reduces quality problems caused by material fluctuations.

[0018] As a preferred example of the first aspect, the preset solver can be any one of CPLEX, Gurobi, or COPT.

[0019] In this preferred example, the efficient computational power of a mature solver ensures the practical feasibility of the optimization process. Even with complex production models and a large number of variables, these tools can quickly calculate the optimal solution. Enterprises do not need to wait excessively to obtain an executable production adjustment plan.

[0020] Secondly, the present invention provides a production optimization device for lithium battery products, comprising: a data acquisition module, a first processing module, a second processing module, a third processing module, and a production optimization module; The data acquisition module is used to acquire the carbon emission factor of each production process in the product manufacturing stage of the lithium battery product, as well as the upper and lower limits of the input of each material. The first processing module is used to construct an objective function based on the carbon emission factors of each production process, using the input amount of each material as a decision variable, and then minimizing the total carbon emissions in the product manufacturing stage of the lithium battery product. The second processing module is used to establish continuous production constraints, production cost constraints, and input constraints corresponding to the input of each material based on the upper and lower limits corresponding to the input of each material. The third processing module is used to establish a lithium battery product production optimization model based on the objective function, the continuous production constraint, the production cost constraint, and the input constraint corresponding to the input of each material. The production optimization module is used to solve the lithium battery product production optimization model through a preset solver, obtain the material input of each production process of the lithium battery product, and perform production optimization based on the material input of each production process.

[0021] As a preferred example of the second aspect, before obtaining the carbon emission factors of each production process in the product manufacturing stage of the lithium battery product, the method further includes: Obtain the full life-cycle carbon footprint accounting data of the lithium battery product; By comparing the carbon emissions at each stage in the full life cycle carbon footprint accounting data, the product manufacturing stage is identified as the target for production optimization.

[0022] As a preferred example of the second aspect, the construction of an objective function based on the carbon emission factors of each of the production processes, using the input amount of each material as a decision variable, and then minimizing the total carbon emissions in the product manufacturing stage of the lithium battery product, includes: Based on the carbon emission factors of each production process, and using the input amount of each material as a decision variable, an objective function is constructed to minimize the total carbon emissions during the product manufacturing stage of the lithium battery product. The expression of the objective function is as follows: in, This refers to the total carbon emissions during the manufacturing phase of the lithium battery product. Let be the carbon emission factor corresponding to the i-th material in the j-th production process. Let i be the amount of material i input in the j-th production process.

[0023] As a preferred example of the second aspect, the establishment of continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity based on the upper and lower limits corresponding to the input quantities of each material includes: Based on the upper and lower limits corresponding to the input quantities of each material, continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material are established. The expression for the continuous production constraints is as follows: in, For the electrical power input corresponding to the j-th production process, Let N be the total electricity input during the product manufacturing stage, and N be the number of production processes in the product manufacturing stage. and These represent the lower and upper limits of the operating power of the production equipment, respectively.

[0024] As a preferred example of the second aspect, the establishment of continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity based on the upper and lower limits corresponding to the input quantities of each material includes: Based on the upper and lower limits corresponding to the input quantities of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material. The expression for the production cost constraint is as follows: in, This is the price conversion factor. As the upper limit of production costs, Let be the total input amount of the i-th material.

[0025] As a preferred example of the second aspect, the establishment of continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity based on the upper and lower limits corresponding to the input quantities of each material includes: Based on the upper and lower limits corresponding to the input of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input of each material; among them, the input constraints corresponding to the input of each material include natural gas input constraints, phosphoric acid input constraints, copper foil input constraints, polyethylene input constraints, and methanol input constraints.

[0026] As a preferred example of the second aspect, the preset solver can be any one of CPLEX, Gurobi, or COPT.

[0027] In summary, this application provides a data foundation for the entire optimization process by obtaining the specific carbon emission factors of each production stage in the product manufacturing process. This allows enterprises to clearly see the emission contribution of different processes, changing the previous situation of crude calculation. Secondly, it sets the consumption of various materials in production as adjustable decision variables and directly establishes a mathematical function with the goal of reducing total carbon emissions. This approach transforms the emission reduction target into a clear and calculable engineering problem. Next, this application simultaneously considers the practical requirements of continuous equipment operation, the enterprise's production cost constraints, and the reasonable scope of use of various materials. By establishing these constraints, it ensures that the final optimized solution is truly feasible. Then, this application integrates the objective and constraints to establish a complete optimization model. This model systematically describes the complex relationship between production, emissions, and costs, enabling enterprises to find the optimal solution from a global perspective. Finally, through calculation using a standard solver, this application can quickly obtain the optimal solution for how much material should be input in each process. The factory can directly adjust the parameter settings on the production line based on these results. This helps companies achieve low-carbon production processes in a systematic and data-driven manner, effectively reducing carbon emissions while ensuring normal production and cost control.

[0028] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the production optimization method for lithium battery products of the present invention.

[0029] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the production optimization method for lithium battery products of the present invention. Attached Figure Description

[0030] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0031] Figure 1 A flowchart illustrating an embodiment of a production optimization method for lithium battery products provided by the present invention; Figure 2 The lithium-ion battery industry chain diagram provided for this invention; Figure 3 A boundary diagram for studying the carbon emissions throughout the entire life cycle of a lithium-ion battery, as an embodiment of a production optimization method for lithium battery products provided by this invention; Figure 4 A framework diagram of a resource task network for an embodiment of a production optimization method for lithium battery products provided by the present invention; Figure 5 This is a module structure diagram of one embodiment of a production optimization device for lithium battery products provided by the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0034] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0035] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0036] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0037] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0038] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0039] It should be noted that lithium battery products are specifically lithium-ion batteries. Lithium-ion batteries hold a place in two major sectors: new energy vehicles and tablet computers. Driven by low-carbon requirements and the trend of globalization, the number of lithium-ion battery companies and their production volume are expected to increase steadily. Figure 2 The diagram shows the lithium-ion battery industry chain.

[0040] Lithium-ion batteries have lithium-ion compounds embedded in the positive electrode, and are therefore known as cyclic-chargeable or clean batteries. They utilize the movement of lithium ions between the positive and negative electrodes, offering many advantages such as fast charging, high energy density, safety, and reliability. In recent years, lithium iron phosphate batteries have become the most common lithium-ion battery product in the Chinese electric vehicle battery market.

[0041] Example 1 See Figure 1 To address the problem in existing technologies of simultaneously improving production efficiency at the stage with the greatest emission reduction potential, an embodiment of the present invention provides a production optimization method for lithium battery products, comprising: S1. Obtain the carbon emission factor of each production process in the product manufacturing stage of the lithium battery product, and the upper and lower limits of the input of each material. As a preferred embodiment, before obtaining the carbon emission factors of each production process in the product manufacturing stage of the lithium battery product, the method further includes: Obtain the full life-cycle carbon footprint accounting data of the lithium battery product; By comparing the carbon emissions at each stage in the full life cycle carbon footprint accounting data, the product manufacturing stage is identified as the target for production optimization.

[0042] Specifically, to fully illustrate the process of identifying the product manufacturing stage as the target for production optimization, the following scheme will be used as an example: This embodiment aims at the carbon emissions of lithium iron phosphate batteries throughout their entire life cycle. By calculating the carbon emissions in four stages—raw material acquisition, product manufacturing, product transportation, and recycling and disposal—it assesses the carbon footprint of lithium iron phosphate batteries throughout their entire life cycle, providing a basis for carbon reduction strategies in the lithium battery industry.

[0043] Carbon emissions from the raw material acquisition stage of lithium-ion battery products are mainly generated by the mining of raw materials such as lithium ore, phosphoric acid, and methanol. Carbon emissions from the manufacturing stage are mainly generated by the preparation of positive and negative electrodes and the assembly of battery components. Carbon emissions from the transportation stage are mainly generated by the transportation process itself. Carbon emissions from the recycling and disposal stage are mainly generated by heavy-duty truck transportation and product dismantling. The research boundaries of carbon emissions throughout the entire life cycle of lithium-ion batteries are as follows: Figure 3 As shown.

[0044] In this embodiment, the main sources of data collection for lithium battery products are enterprise data, statistical yearbook data, relevant literature, and papers. The data collection is described according to the stages of lithium battery raw material acquisition, production and assembly, and recycling and replacement.

[0045] In the raw material acquisition stage, the raw materials and energy consumed are calculated from the start of ore mining. The input data list for the raw material acquisition stage of lithium-ion batteries is shown in Table 1.

[0046] Table 1. Partial Material and Energy Data in the Raw Material Acquisition Stage of Lithium-ion Battery Products In the product manufacturing stage, the product manufacturing stage of lithium-ion batteries mainly consists of two steps: positive and negative electrode preparation and battery component assembly. The input data list for the positive and negative electrode preparation step in the product manufacturing stage of lithium-ion batteries is shown in Table 2.

[0047] Table 2. Input Data List for Positive and Negative Electrode Preparation Table 3 shows the list of input data for the battery component assembly stage in the manufacturing process of lithium-ion batteries.

[0048] Table 3 Input Data List for Battery Component Assembly The list of input data for the transportation stage of lithium-ion battery products is shown in Table 4.

[0049] Table 4. List of Input Data for Lithium-ion Battery Products During Transportation The recycling and processing of lithium-ion batteries generally involves the following steps: First, waste batteries are collected and transported to a waste battery processing plant. Then, processes such as discharging, testing, disassembly, reassembly, and casing replacement are performed. Taking the recycling of 1 kWh of waste lithium-ion batteries as an example, the input data list for the recycling and processing stage is shown in Table 5.

[0050] Table 5. Input Data List for Lithium-ion Battery Product Recycling and Processing Stage Next, we will conduct carbon footprint accounting for lithium battery products. Table 6 lists the summary data of the consumption of relevant materials corresponding to the entire life cycle of lithium-ion battery products, mainly involving the consumption of raw materials, energy and materials related to carbon accounting.

[0051] Table 6 Summary of Carbon Accounting-Related Material Consumption in the Lithium-ion Battery Product Supply Chain Table 7 lists the carbon footprint calculation results for the entire life cycle of lithium-ion battery products. The calculation shows that the total CO2 emissions of 1 kWh lithium-ion battery product over its entire life cycle are approximately 33.31 kg. Of this, the carbon emissions during the raw material acquisition stage account for 91.14% of the total life cycle carbon emissions, making it the primary contributor to the carbon footprint of the production process. The manufacturing stage is the second largest contributor, accounting for 9.03% of the total life cycle carbon emissions. Furthermore, the carbon emissions during the transportation stage are relatively small, accounting for only 0.02% of the total life cycle carbon emissions.

[0052] Table 7 Carbon Emission Calculation Table for Lithium-ion Battery Products Analysis shows that the raw material acquisition and manufacturing stages are the two most critical phases for reducing carbon emissions throughout the entire production process for lithium-ion batteries. Measures should be taken to reduce carbon emissions, starting with these two stages. For example, clean energy can be used to replace fossil fuels, production processes can be optimized, and energy-saving and emission-reduction technologies can be promoted. Among these, the manufacturing stage is particularly suitable for production optimization. S2. Based on the carbon emission factors of each production process, and using the input of each material as a decision variable, an objective function is constructed to minimize the total carbon emissions in the product manufacturing stage of the lithium battery product. It should be noted that, in order to more accurately describe the material flow, energy flow, and process coupling relationships in the production process of typical industry products, this study introduces the Resource-Task Network (RTN) modeling method as a unified theoretical foundation. RTN, through resource nodes, task nodes, and their connections, can accurately characterize complex production systems with multiple stages, resources, and constraints. Its framework is as follows: Figure 4 As shown. As a preferred embodiment, the step of constructing an objective function based on the carbon emission factors of each production process, using the input amount of each material as a decision variable, and then minimizing the total carbon emissions in the product manufacturing stage of the lithium battery product, includes: Based on the carbon emission factors of each production process, and using the input amount of each material as a decision variable, an objective function is constructed to minimize the total carbon emissions during the product manufacturing stage of the lithium battery product. The expression of the objective function is as follows: in, This refers to the total carbon emissions during the manufacturing phase of the lithium battery product. Let be the carbon emission factor corresponding to the i-th material in the j-th production process. Let i be the amount of material i input in the j-th production process.

[0053] For example, the decision variables for optimizing carbon emissions are derived based on the lithium battery product manufacturing process, as shown in Table 8. Modeling is performed with the goal of minimizing carbon emissions from producing 1 kWh of lithium battery products. Based on the above production design process, the decision variables for low-carbon optimized production of lithium battery products are derived, with values ​​calculated based on carbon footprint accounting.

[0054] Table 8. Decision Variables and Their Ranges S3. Based on the upper and lower limits corresponding to the input of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input of each material. As a preferred embodiment, the step of establishing continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity based on the upper and lower limits corresponding to the input quantity of each material includes: Based on the upper and lower limits corresponding to the input quantities of each material, continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material are established. The expression for the continuous production constraints is as follows: in, For the electrical power input corresponding to the j-th production process, Let N be the total electricity input during the product manufacturing stage, and N be the number of production processes in the product manufacturing stage. and These represent the lower and upper limits of the operating power of the production equipment, respectively.

[0055] As a preferred embodiment, the step of establishing continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity based on the upper and lower limits corresponding to the input quantity of each material includes: Based on the upper and lower limits corresponding to the input quantities of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material. The expression for the production cost constraint is as follows: in, This is the price conversion factor. As the upper limit of production costs, Let be the total input amount of the i-th material.

[0056] As a preferred embodiment, the step of establishing continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity based on the upper and lower limits corresponding to the input quantity of each material includes: Based on the upper and lower limits corresponding to the input of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input of each material; among them, the input constraints corresponding to the input of each material include natural gas input constraints, phosphoric acid input constraints, copper foil input constraints, polyethylene input constraints, and methanol input constraints.

[0057] Specifically, the expressions for the natural gas input constraint, the phosphoric acid input constraint, the copper foil input constraint, the polyethylene input constraint, and the methanol input constraint are as follows: ① Phosphoric acid input constraints in, It's the added phosphoric acid. Indicates the lower limit of phosphoric acid input. This indicates the upper limit for phosphoric acid input.

[0058] ② Copper foil input constraints in, It is the copper foil that is inserted. This indicates the lower limit for copper foil input. This indicates the upper limit for copper foil input.

[0059] ③Polyethylene input constraints in, It is polyethylene that has been added. This indicates the lower limit for polyethylene input. This indicates the upper limit for polyethylene input.

[0060] ④ Methanol input constraints in, It's the methanol that was added. This indicates the lower limit for methanol input. This indicates the upper limit for methanol input.

[0061] S4. Based on the objective function, the continuous production constraint, the production cost constraint, and the input constraint corresponding to the input of each material, establish a lithium battery product production optimization model; S5. Solve the lithium battery product production optimization model using a preset solver to obtain the material input quantities for each production process of the lithium battery product, and optimize the production based on the material input quantities for each production process.

[0062] In a preferred embodiment, the preset solver can be any one of CPLEX, Gurobi, or COPT.

[0063] Specifically, to verify the reliability of this embodiment, the following experimental results are used as an example for illustration: A photovoltaic product production optimization model oriented towards carbon emission reduction was established based on MATLAB and solved using the CPLEX solver. The model shows that producing 1 kWh of lithium-ion battery product emits approximately 19.26 kg of CO2. This application, based on an optimization model for lithium-ion battery product input materials for low-carbon production, reduces the input of electricity, natural gas, phosphoric acid, copper foil, polyethylene, and methanol while ensuring the cost of lithium-ion battery products, thereby reducing carbon emissions during the lithium-ion battery production stage. After production optimization using the model proposed in this study, compared with the average carbon emission calculated from the carbon footprint of lithium-ion battery production (approximately 33.31 kg), the proposed scheme reduces CO2 by approximately 14.05 kg per kWh of lithium iron phosphate battery product production. The solution results demonstrate that the model proposed in this project can effectively reduce carbon emissions during the lithium-ion battery production process.

[0064] Specifically, the production optimization based on the input of each material in each production process can be implemented through the following preferred methods: The material input quantities obtained from the optimization model are not simply "theoretical values," but are transformed into actual production instructions through the following mechanism, the specific steps of which are shown below: ① Optimize result analysis and parameter mapping. The decision variables output by the model need to be converted into executable parameters of the production equipment.

[0065] ② Production execution and real-time monitoring: Optimization plans are distributed to the production line through the MES (Manufacturing Execution System).

[0066] This application provides a data foundation for the entire optimization process by acquiring the specific carbon emission factors of each production stage in the product manufacturing process. This allows enterprises to clearly see the emission contribution of different processes, changing the previous situation of crude calculation. Secondly, it sets the consumption of various materials in production as adjustable decision variables and directly establishes a mathematical function with the goal of reducing total carbon emissions. This approach transforms the emission reduction target into a clear and calculable engineering problem. Next, this application simultaneously considers the practical requirements of continuous equipment operation, the enterprise's production cost constraints, and the reasonable scope of use of various materials. By establishing these constraints, it ensures that the final optimized solution is truly feasible. Then, this application integrates the objective and constraints to establish a complete optimization model. This model systematically describes the complex relationship between production, emissions, and costs, enabling enterprises to find the optimal solution from a global perspective. Finally, through calculation using a standard solver, this application can quickly obtain the optimal solution for how much material should be input in each process. The factory can directly adjust the parameter settings on the production line based on these results. This helps companies achieve low-carbon production processes in a systematic and data-driven manner, effectively reducing carbon emissions while ensuring normal production and cost control.

[0067] Example 2 like Figure 3 As shown, based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides a production optimization device for lithium battery products, including: a data acquisition module 51, a first processing module 52, a second processing module 53, a third processing module 54, and a production optimization module 55; The data acquisition module 51 is used to acquire the carbon emission factor of each production process in the product manufacturing stage of the lithium battery product, and the upper and lower limits of the input of each material. The first processing module 52 is used to construct an objective function based on the carbon emission factors of each production process, using the input amount of each material as a decision variable, and then minimizing the total carbon emissions in the product manufacturing stage of the lithium battery product. The second processing module 53 is used to establish continuous production constraints, production cost constraints and input constraints corresponding to each material input quantity based on the upper and lower limits corresponding to the input quantity of each material. The third processing module 54 is used to establish a lithium battery product production optimization model based on the objective function, the continuous production constraint, the production cost constraint, and the input constraint corresponding to the input of each material. The production optimization module 55 is used to solve the lithium battery product production optimization model through a preset solver, obtain the material input of each production process of the lithium battery product, and perform production optimization based on the material input of each production process.

[0068] As a preferred embodiment, before obtaining the carbon emission factors of each production process in the product manufacturing stage of the lithium battery product, the method further includes: Obtain the full life-cycle carbon footprint accounting data of the lithium battery product; By comparing the carbon emissions at each stage in the full life cycle carbon footprint accounting data, the product manufacturing stage is identified as the target for production optimization.

[0069] As a preferred embodiment, the step of constructing an objective function based on the carbon emission factors of each production process, using the input amount of each material as a decision variable, and then minimizing the total carbon emissions in the product manufacturing stage of the lithium battery product, includes: Based on the carbon emission factors of each production process, and using the input amount of each material as a decision variable, an objective function is constructed to minimize the total carbon emissions during the product manufacturing stage of the lithium battery product. The expression of the objective function is as follows: in, This refers to the total carbon emissions during the manufacturing phase of the lithium battery product. Let be the carbon emission factor corresponding to the i-th material in the j-th production process. Let i be the amount of material i input in the j-th production process.

[0070] As a preferred embodiment, the step of establishing continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity based on the upper and lower limits corresponding to the input quantity of each material includes: Based on the upper and lower limits corresponding to the input quantities of each material, continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material are established. The expression for the continuous production constraints is as follows: in, For the electrical power input corresponding to the j-th production process, Let N be the total electricity input during the product manufacturing stage, and N be the number of production processes in the product manufacturing stage. and These represent the lower and upper limits of the operating power of the production equipment, respectively.

[0071] As a preferred embodiment, the step of establishing continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity based on the upper and lower limits corresponding to the input quantity of each material includes: Based on the upper and lower limits corresponding to the input quantities of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material. The expression for the production cost constraint is as follows: in, This is the price conversion factor. As the upper limit of production costs, Let be the total input amount of the i-th material.

[0072] As a preferred embodiment, the step of establishing continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity based on the upper and lower limits corresponding to the input quantity of each material includes: Based on the upper and lower limits corresponding to the input of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input of each material; among them, the input constraints corresponding to the input of each material include natural gas input constraints, phosphoric acid input constraints, copper foil input constraints, polyethylene input constraints, and methanol input constraints.

[0073] In a preferred embodiment, the preset solver can be any one of CPLEX, Gurobi, or COPT.

[0074] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 1, but not limited to these descriptions.

[0075] In summary, this application provides a data foundation for the entire optimization process by obtaining the specific carbon emission factors of each production stage in the product manufacturing process. This allows enterprises to clearly see the emission contribution of different processes, changing the previous situation of crude calculation. Secondly, it sets the consumption of various materials in production as adjustable decision variables and directly establishes a mathematical function with the goal of reducing total carbon emissions. This approach transforms the emission reduction target into a clear and calculable engineering problem. Next, this application simultaneously considers the practical requirements of continuous equipment operation, the enterprise's production cost constraints, and the reasonable scope of use of various materials. By establishing these constraints, it ensures that the final optimized solution is truly feasible. Then, this application integrates the objective and constraints to establish a complete optimization model. This model systematically describes the complex relationship between production, emissions, and costs, enabling enterprises to find the optimal solution from a global perspective. Finally, through calculation using a standard solver, this application can quickly obtain the optimal solution for how much material should be input in each process. The factory can directly adjust the parameter settings on the production line based on these results. This helps companies achieve low-carbon production processes in a systematic and data-driven manner, effectively reducing carbon emissions while ensuring normal production and cost control.

[0076] It is understood that the above-described apparatus embodiments correspond to the method embodiments of the present invention, and can implement the production optimization method for lithium battery products provided by any of the above-described method embodiments of the present invention.

[0077] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0078] Example 3 Based on the above embodiments of the production optimization method for lithium battery products, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the production optimization method for lithium battery products according to any embodiment of the present invention.

[0079] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0080] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0081] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0082] Example 4 Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the production optimization method for lithium battery products described in any of the above-described method embodiments of the present invention.

[0083] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0084] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A production optimization method for lithium battery products, characterized in that, include: Obtain the carbon emission factor of each production process in the product manufacturing stage of the lithium battery product, and the upper and lower limits of the input of each material; Based on the carbon emission factors of each production process, the input of each material is used as the decision variable, and then the objective function is constructed to minimize the total carbon emissions in the product manufacturing stage of the lithium battery product. Based on the upper and lower limits corresponding to the input of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input of each material. Based on the objective function, the continuous production constraints, the production cost constraints, and the input constraints corresponding to the input quantities of each material, an optimization model for lithium battery product production is established. The lithium battery product production optimization model is solved by a preset solver to obtain the material input of each production process of the lithium battery product, and production optimization is carried out based on the material input of each production process.

2. The production optimization method for lithium battery products as described in claim 1, characterized in that, Before obtaining the carbon emission factors of each production process in the product manufacturing stage of the lithium battery product, the method further includes: Obtain the full life-cycle carbon footprint accounting data of the lithium battery product; By comparing the carbon emissions at each stage in the full life cycle carbon footprint accounting data, the product manufacturing stage is identified as the target for production optimization.

3. The production optimization method for lithium battery products as described in claim 1, characterized in that, The step of constructing an objective function based on the carbon emission factors of each production process, using the input amount of each material as a decision variable, and then minimizing the total carbon emissions in the product manufacturing stage of the lithium battery product includes: Based on the carbon emission factors of each production process, and using the input amount of each material as a decision variable, an objective function is constructed to minimize the total carbon emissions during the product manufacturing stage of the lithium battery product. The expression of the objective function is as follows: in, This refers to the total carbon emissions during the manufacturing phase of the lithium battery product. Let be the carbon emission factor corresponding to the i-th material in the j-th production process. Let i be the amount of material i input in the j-th production process.

4. The production optimization method for lithium battery products as described in claim 1, characterized in that, The establishment of continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity, based on the upper and lower limits corresponding to the input quantities of each material, includes: Based on the upper and lower limits corresponding to the input quantities of each material, continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material are established. The expression for the continuous production constraints is as follows: in, For the electrical power input corresponding to the j-th production process, Let N be the total electricity input during the product manufacturing stage, and N be the number of production processes in the product manufacturing stage. and These represent the lower and upper limits of the operating power of the production equipment, respectively.

5. The production optimization method for lithium battery products as described in claim 1, characterized in that, The establishment of continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity, based on the upper and lower limits corresponding to the input quantities of each material, includes: Based on the upper and lower limits corresponding to the input quantities of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input quantities of each material. The expression for the production cost constraint is as follows: in, This is the price conversion factor. As the upper limit of production costs, Let be the total input amount of the i-th material.

6. The production optimization method for lithium battery products as described in claim 1, characterized in that, The establishment of continuous production constraints, production cost constraints, and input constraints corresponding to each material input quantity, based on the upper and lower limits corresponding to the input quantities of each material, includes: Based on the upper and lower limits corresponding to the input of each material, establish continuous production constraints, production cost constraints, and input constraints corresponding to the input of each material; among them, the input constraints corresponding to the input of each material include natural gas input constraints, phosphoric acid input constraints, copper foil input constraints, polyethylene input constraints, and methanol input constraints.

7. A production optimization method for lithium battery products as described in any one of claims 1 to 6, characterized in that, The preset solver can be any one of CPLEX, Gurobi, or COPT.

8. A production optimization device for lithium battery products, characterized in that, include: The system includes a data acquisition module, a first processing module, a second processing module, a third processing module, and a production optimization module. The data acquisition module is used to acquire the carbon emission factor of each production process in the product manufacturing stage of the lithium battery product, as well as the upper and lower limits of the input of each material. The first processing module is used to construct an objective function based on the carbon emission factors of each production process, using the input amount of each material as a decision variable, and then minimizing the total carbon emissions in the product manufacturing stage of the lithium battery product. The second processing module is used to establish continuous production constraints, production cost constraints, and input constraints corresponding to the input of each material based on the upper and lower limits corresponding to the input of each material. The third processing module is used to establish a lithium battery product production optimization model based on the objective function, the continuous production constraint, the production cost constraint, and the input constraint corresponding to the input of each material. The production optimization module is used to solve the lithium battery product production optimization model through a preset solver, obtain the material input of each production process of the lithium battery product, and perform production optimization based on the material input of each production process.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the production optimization method for lithium battery products as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the production optimization method for lithium battery products as described in any one of claims 1-7.

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