Lithium battery pollution reduction and carbon reduction collaborative optimization method, device and equipment based on LCA and storage medium

Abstract

The invention provides a lithium battery pollution reduction and carbon reduction collaborative optimization method, device and equipment based on LCA and a storage medium. Raw material composition, sintering temperature, energy consumption, tail gas pollutant concentration and key process parameters are collected in real time through an Internet of Things sensor network and fused with data of a life cycle evaluation database to generate standardized life cycle data so as to accurately reflect carbon emission and pollutant emission conditions. And calculating a carbon emission value and a pollution emission value in the production process based on the data to form a combined environment evaluation index, establishing an objective function according to the combined environment evaluation index, and solving by adopting a multi-objective optimization algorithm to obtain optimized process parameters meeting preset performance constraints. Optimized parameters are then transmitted to a production control system, real-time adjustment of key process parameters is achieved, production data are continuously monitored, closed-loop control is formed, collaborative optimization of pollution reduction and carbon reduction is ensured, and the problem that overall optimization is difficult to achieve in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of pollution reduction and carbon emission reduction of lithium batteries, and particularly relates to a method, device, equipment and storage medium for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA. Background Art

[0002] With the wide application of lithium-ion batteries in fields such as new energy vehicles and energy storage systems, the carbon emissions and pollutant emissions during their production process have attracted global attention. Existing technologies mainly focus on reducing the carbon footprint, while ignoring the collaborative control of pollutant emissions (such as heavy metals, fluorides, etc.), resulting in difficulty in achieving the overall optimization effect of pollution reduction and carbon emission reduction in actual applications. In addition, traditional life cycle assessment (LCA) relies on static databases (such as Ecoinvent), which are often significantly disjointed from the real-time production data in the workshop, and it is impossible to dynamically optimize process parameters online, making it difficult to reflect the environmental benefits in real time. The EU's "New Battery Regulation" clearly requires the disclosure of the carbon footprint of the entire battery life cycle, and China's "dual carbon" goal further strengthens the urgency of collaborative governance of pollution reduction and carbon emission reduction. Therefore, there is an urgent need for a closed-loop optimization technology that integrates dynamic LCA and real-time data-driven to solve the deficiencies of existing methods in collaborative control, dynamic correction, and closed-loop feedback.

[0003] In view of this, the present application is proposed. Summary of the Invention

[0004] The present invention discloses a method, device, equipment and storage medium for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA, aiming to solve the problem of difficulty in achieving the overall optimization of pollution reduction and carbon emission reduction.

[0005] The first embodiment of the present invention provides a method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA, including:

[0006] S1: Obtain real-time data collected by an Internet of Things sensor network deployed on a lithium battery production line, where the real-time data includes raw material composition, sintering temperature, energy consumption, pollutant concentration in the tail gas, and key process parameters;

[0007] S2: Perform data fusion on the real-time data and the data stored in a preset life cycle assessment database. Wherein, the data fusion process is: using preset data normalization processing, data smoothing filtering, and outlier rejection algorithms, synchronously fuse the collected real-time data with the historical data stored in the life cycle assessment database to generate life cycle data in a standard format, and the life cycle data in the standard format is used to reflect the actual situation of carbon emissions and pollution emissions during the production process;

[0008] S3: Calculate the carbon emission value and pollution emission value during the production process according to the standardized life cycle data to form a joint environmental evaluation index;

[0009] S4: Establish an objective function according to the combined environmental evaluation index, and use a multi-objective optimization algorithm to solve the objective function to obtain optimized process parameters that meet the preset performance constraints. Among them, the objective function is used to specify the collaborative optimization requirements for carbon emissions and pollution emissions;

[0010] S5: Transmit the optimized process parameters to the production control system to adjust the real-time key process parameters on the production line, monitor the production control system, collect the actual data of the production process after adjustment, and feedback the actual data to step S1 to form a closed-loop control.

[0011] Preferably, the multi-objective optimization algorithm is an improved NSGA-III algorithm, which processes the physical constraints and performance constraints of the production process by introducing a penalty function during the search process and outputs a set of candidate process parameter sets containing Pareto optimal solutions;

[0012] Among them, the optimization objectives of the objective function are to minimize the carbon emission value and minimize the pollution emission value. The weight values of the carbon emission value and the pollution emission value are dynamically adjusted based on the fuzzy multi-criteria decision-making algorithm, and the constraint conditions are the parameters of the production process.

[0013] Preferably, the closed-loop control is specifically:

[0014] Use the continuously collected real-time data to compare with the preset optimization objectives, and achieve error correction and re-optimization control by setting dynamic thresholds, so that the entire production process is always in the best pollution reduction and carbon emission reduction state.

[0015] The second embodiment of the present invention provides a collaborative optimization device for pollution reduction and carbon emission reduction of lithium batteries based on LCA, including:

[0016] A data acquisition unit for obtaining real-time data collected by an Internet of Things sensor network deployed on a lithium battery production line. The real-time data includes raw material composition, sintering temperature, energy consumption, pollutant concentration in the tail gas, and key process parameters;

[0017] A life cycle data generation unit for fusing the real-time data with the data stored in a preset life cycle assessment database. Among them, the data fusion process is: using preset data normalization processing, data smoothing filtering, and outlier removal algorithms, synchronously fuse the collected real-time data with the historical data stored in the life cycle assessment database to generate life cycle data in a standard format. The life cycle data in the standard format is used to reflect the actual situation of carbon emissions and pollution emissions during the production process;

[0018] The combined environmental assessment index generation unit is used to calculate the carbon emission value and pollution emission value during the production process based on the standardized life cycle data, and form a combined environmental assessment index;

[0019] The optimization process parameter generation unit is used to establish an objective function according to the combined environmental assessment index, and solve the objective function by using a multi-objective optimization algorithm to obtain optimized process parameters that meet the preset performance constraints. Among them, the objective function is used to specify the collaborative optimization requirements for carbon emissions and pollution emissions;

[0020] The feedback adjustment unit is used to transmit the optimized process parameters to the production control system to adjust the real-time key process parameters on the production line, monitor the production control system, collect the actual data of the production process after adjustment, and feedback the actual data to the data collection unit to form a closed-loop control.

[0021] Preferably, the multi-objective optimization algorithm is an improved NSGA-III algorithm, which processes the physical constraints and performance constraints of the production process by introducing a penalty function during the search process, and outputs a set of candidate process parameter sets containing Pareto optimal solutions;

[0022] Among them, the optimization objectives of the objective function are to minimize the carbon emission value and minimize the pollution emission value. The weight values of the carbon emission value and the pollution emission value are dynamically adjusted based on the fuzzy multi-criteria decision-making algorithm, and the constraint conditions are the parameters of the production process.

[0023] Preferably, the closed-loop control is specifically:

[0024] Using continuously collected real-time data, comparing it with the preset optimization objectives, and realizing error correction and re-optimization regulation by setting dynamic thresholds, so that the entire production process is always in the best state of reducing pollution and carbon emissions.

[0025] The third embodiment of the present invention provides a lithium battery pollution reduction and carbon emission reduction collaborative optimization device based on LCA, including a memory and a processor. The memory stores a computer program, and the computer program can be executed by the processor to implement a lithium battery pollution reduction and carbon emission reduction collaborative optimization method as described in any one of the above.

[0026] The fourth embodiment of the present invention provides a computer-readable storage medium, which is characterized in that it stores a computer program, and the computer program can be executed by the processor of the device where the computer-readable storage medium is located to implement a lithium battery pollution reduction and carbon emission reduction collaborative optimization method as described in any one of the above.

[0027] A method, device, equipment and storage medium for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA according to the present invention. By obtaining real-time data collected by an Internet of Things sensor network deployed on a lithium battery production line, the real-time data includes raw material composition, sintering temperature, energy consumption, pollutant concentration in tail gas, and key process parameters. Then, the real-time data is fused with the data stored in a preset life cycle assessment database to generate standardized life cycle data, which is used to reflect the actual situation of carbon emission and pollution emission during the production process. Then, according to the standardized life cycle data, the carbon emission value and pollution emission value during the production process are calculated to form a joint environmental evaluation index. Then, according to the joint environmental evaluation index, an objective function is established, and a multi-objective optimization algorithm is used to solve the objective function to obtain optimized process parameters that meet preset performance constraints, where the objective function is used to specify the collaborative optimization requirements of carbon emission and pollution emission. Finally, the optimized process parameters are transmitted to the production control system to adjust the key process parameters on the production line in real time, monitor the production control system, collect the actual data of the production process after adjustment, and feedback the actual data to form a closed-loop control, solving the problem of difficulty in achieving overall optimization of pollution reduction and carbon emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 FIG. 6 is a schematic flowchart of a method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA provided in the first embodiment of the present invention;

[0029] Figure 2 FIG. 10 is a schematic diagram of modules of a device for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA provided in the second embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] For a better understanding of the technical solutions of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0032] It should be clear that the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise.

[0034] It should be understood that the term "and / or" used herein is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0035] Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0036] The "first / second" mentioned in the embodiments is only to distinguish similar objects and does not represent a specific order for the objects. It can be understood that the "first / second" can be interchanged in the allowed cases for the specific order or sequence. It should be understood that the objects distinguished by the "first / second" can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0037] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings.

[0038] The present invention discloses a method, device, equipment and storage medium for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA, aiming to solve the problem of difficult overall optimization of pollution reduction and carbon emission reduction.

[0039] Please refer to Figure 1 , the first embodiment of the present invention provides a method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA, which can be executed by a device for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA (hereinafter referred to as the optimization device), and particularly, executed by one or more processors in the optimization device to at least achieve the following steps:

[0040] S1: Obtain the real-time data collected by the Internet of Things sensor network deployed on the lithium battery production line, where the real-time data includes raw material composition, sintering temperature, energy consumption, pollutant concentration in the tail gas, and key process parameters;

[0041] In this embodiment, the optimization device can be a terminal with data processing capabilities such as a server or a workstation, which can establish communication with the Internet of Things sensor network deployed on the lithium battery production line. An appropriate operating system and application software can be installed in the optimization device, and the functions required in this embodiment can be realized through the combination of the operating system and the application software.

[0042] It should be noted that by reasonably arranging the Internet of Things sensor network on the lithium battery production line, real-time data collection of the entire production process is realized. Specifically, the sensor network covers all links of the production line. At the place where raw materials enter the process link, the on-line detection equipment can collect the proportion and characteristic information of each component in the raw materials in real time; in the sintering process, the high-precision temperature sensor monitors the temperature change in the furnace in real time to ensure that the temperature data can accurately reflect the actual working conditions; at the same time, the energy consumption metering device records the electric energy value consumed by the equipment during the production process; the concentration of pollutants in the tail gas is measured by the gas component sensor to obtain the concentration data of carbon dioxide, carbon monoxide and other harmful gases in the tail gas in real time; in addition, the optimization device also monitors key parameters related to the production process, such as the operating state of the equipment and the process duration. These real-time data are transmitted to the central data processing unit through the industrial field bus or wireless communication network.

[0043] S2: Perform data fusion on the real-time data and the data stored in the preset life cycle assessment database to generate standardized life cycle data, which is used to reflect the actual situation of carbon emissions and pollution emissions during the production process;

[0044] Among them, the data fusion process is as follows: adopt the preset data normalization processing, data smoothing filtering and outlier rejection algorithms to synchronously fuse the collected real-time data with the historical data stored in the life cycle assessment database, and output the life cycle data in a standard format;

[0045] It should be noted that, in this embodiment, the optimization device first fuses the real-time data collected by the IoT sensors deployed in the lithium battery production line with the historical data in the pre-established LCA database (such as the openLCA database). Specifically, the collected real-time data is normalized, smoothed and filtered, and outliers are eliminated, and then matched with the basic data stored in the database according to the time series and process links, and synchronized docking is achieved using the data fusion algorithm. The fusion process not only generates standardized life cycle data in accordance with the ILCD format, but also further uses the real-time corrected data to calculate the carbon footprint (kgCO2 / kWh) and pollution footprint (expressed in pollution equivalent or pollution index) in the current production process. On this basis, the dynamic LCA model module converts the original sensor data into life cycle data that meets the ILCD standard, and dynamically corrects the basic data in the openLCA database through the data fusion algorithm.

[0046] The following is a practical example, in which the data fusion rule includes real-time LCI generation pseudo code as follows:

[0047] def dynamic_LCI(sensor_data,ecodb):

[0048] lci=ecodb.get_base_data("NCM622")#Call openLCA basic data

[0049] lci.material_flow*=sensor_data['Co_ratio']#Dynamically corrected according to the cobalt content of the sensor

[0050] lci.energy_flow+=sensor_data['sinter_energy']#Superimpose the real-time energy consumption of sintering process

[0051] return lci.to_ILCD()#Output standardized life cycle data format.

[0052] S3: Calculate the carbon emission value and pollution emission value in the production process based on the standardized life cycle data to form a joint environmental evaluation index;

[0053] In this embodiment, after generating the standardized life cycle data, the optimization device uses this data to further calculate the carbon emission value and pollution emission value during the production process, thereby forming a combined environmental evaluation index. First, the standardized life cycle data includes detailed energy flows, material flows, and emission data for each production link. After preprocessing and normalization, these data are output in a format that conforms to the ILCD standard. The system calculates the carbon footprint in units of kgCO2 / kWh by applying pre-determined conversion factors to the energy consumption data and the carbon dioxide concentration in the tail gas. At the same time, for various pollutants in the tail gas and other emission sources, weighted summation is performed according to their respective concentrations and corresponding weight factors to obtain a pollution footprint reflecting the pollution intensity, which can be expressed in the form of pollution equivalents or pollution indices. Subsequently, the optimization device integrates the carbon footprint and the pollution footprint to form a combined environmental evaluation index, which comprehensively reflects the overall performance of the production process in terms of energy consumption and environmental emissions.

[0054] S4: Establish an objective function based on the combined environmental evaluation index, and use a multi-objective optimization algorithm to solve the objective function to obtain optimized process parameters that meet the preset performance constraints, where the objective function is used to specify the collaborative optimization requirements for carbon emissions and pollution emissions;

[0055] The multi-objective optimization algorithm is an improved NSGA-III algorithm, which processes the physical constraints and performance constraints of the production process by introducing a penalty function during the search process and outputs a set of candidate process parameter sets containing Pareto optimal solutions; among them, the optimization objectives of the objective function are to minimize the carbon emission value and minimize the pollution emission value, and the weight values of the carbon emission value and the pollution emission value are dynamically adjusted based on the fuzzy multi-criteria decision-making algorithm, and the constraint conditions are the parameters of the production process.

[0056] Specifically, in this embodiment, after obtaining the combined environmental evaluation index, the optimization device establishes an objective function based on this index. The objective function clearly stipulates the collaborative optimization requirements for carbon emissions and pollution emissions, and its optimization goal is to simultaneously minimize the carbon emission value and the pollution emission value. To achieve this goal, the objective function adopts the form of weighted summation, where the weights of the carbon emission value and the pollution emission value are dynamically adjusted based on the fuzzy multi-criteria decision-making algorithm to adapt to the environmental assessment requirements under different production conditions. Subsequently, the optimization device uses the improved NSGA-III multi-objective optimization algorithm to solve the objective function. During this optimization process, penalty functions are introduced to penalize the physical constraints (such as the safe operating range of equipment and the upper limit of energy consumption) and performance constraints (such as the defined intervals of process parameters and product quality requirements) during the production process, ensuring that the candidate solutions are practically feasible on the premise of meeting all preset conditions. After global search, the optimization algorithm outputs a set of candidate process parameter sets containing Pareto optimal solutions. Each candidate solution represents an optimal adjustment plan that meets the preset performance constraints and can be directly used to guide the production control system to adjust the key process parameters in real time, thereby realizing the collaborative optimization control of reducing pollution and carbon emissions in the lithium battery production process.

[0057] The battery types include LFP and NCM, and the core indicators include the scarcity of phosphorus-iron resources (S) and the ecological toxicity of heavy metals (T);

[0058] where s = 0.7 * Recyc_p + 0.3 * Land_use,

[0059] T = ∑(Co * EF_Co + Ni * EF_Ni).

[0060] Further preferably, the collaborative optimization algorithm is as follows:

[0061] (Minimize carbon footprint)

[0062] (Minimize pollution footprint)

[0063] (Minimize pollution footprint)

[0064] s.t. (Battery energy density ≥ E min )

[0065] (Process parameters ∈ [X min , X max )

[0066] where, ω PE = 1 - ω CF

[0067] α and β are fuzzy decision coefficients.

[0068] Among them, CF i is the carbon footprint of the i-th link (kgCO2 / kWh), and PE i is the j-th pollution footprint (such as HF equivalent = actual emissions × toxicity factor), RR k is the recycling rate of the k-th resource (such as metal recovery rate), w is the dynamic weight coefficient (calculated by the fuzzy analytic hierarchy process), E min is the battery performance constraint (energy density threshold), and X is the process parameter range (such as sintering temperature, solvent ratio).

[0069] S5: Transmit the optimized process parameters to the production control system to adjust the key process parameters on the production line in real time, monitor the production control system, collect the actual data of the production process after adjustment, and feedback the actual data to step S1 to form a closed-loop control.

[0070] The specific closed-loop control is as follows: Use the continuously collected real-time data to compare with the preset optimization goal, and achieve error correction and re-optimization control by setting dynamic thresholds, so that the entire production process is always in the best state of reducing pollution and carbon emissions.

[0071] It should be noted that in this embodiment, after obtaining the optimized process parameters, the optimization device transmits them to the production control system to adjust the key process parameters on the production line in real time. This transmission process is completed through an industrial communication network. After receiving the optimization parameters, the production control system adjusts the key process variables that affect carbon emissions and pollution emissions, such as sintering temperature, solvent ratio, and inert gas injection rate, to ensure that the production process is executed according to the optimization plan. At the same time, the optimization device continuously monitors the production control system, uses the sensor network to collect the production data after adjustment in real time, including parameters such as temperature, energy consumption, tail gas emission components, and process stability, and transmits the collected actual data back to the data processing unit, so as to realize the dynamic evaluation of the execution effect of the optimization plan. The data is then fed back to step S1 to form a closed-loop control, enabling the optimization process to have an adaptive adjustment ability. The closed-loop control mechanism compares the continuously collected real-time data with the preset optimization goal, calculates the deviation between the current production state and the target value, and sets a dynamic threshold based on this deviation to determine whether to trigger error correction. When the deviation exceeds the threshold, the system automatically re-executes the optimization calculation, adjusts the process parameters, and issues the optimization result to the production control system again, so as to achieve re-optimization control.

[0072] Please refer to Figure 2 , the second embodiment of the present invention provides a device for collaborative optimization of reducing pollution and carbon emissions of lithium batteries based on LCA, including:

[0073] The data acquisition unit 201 is used to obtain the real-time data collected by the Internet of Things sensor network deployed on the lithium battery production line. The real-time data includes raw material composition, sintering temperature, energy consumption, pollutant concentration in the tail gas, and key process parameters.

[0074] The life cycle data generation unit 202 is used to perform data fusion on the real-time data and the data stored in the preset life cycle assessment database. Among them, the data fusion process is as follows: using the preset data normalization processing, data smoothing filtering, and outlier removal algorithms, synchronously fuse the collected real-time data with the historical data stored in the life cycle assessment database to generate life cycle data in a standard format. The life cycle data in the standard format is used to reflect the actual situation of carbon emissions and pollution emissions during the production process.

[0075] The joint environmental assessment index generation unit 203 is used to calculate the carbon emission value and pollution emission value during the production process based on the standardized life cycle data to form a joint environmental assessment index.

[0076] The optimized process parameter generation unit 204 is used to establish an objective function based on the joint environmental assessment index, and use a multi-objective optimization algorithm to solve the objective function to obtain optimized process parameters that meet the preset performance constraints. Among them, the objective function is used to specify the collaborative optimization requirements for carbon emissions and pollution emissions.

[0077] The feedback adjustment unit 205 is used to transmit the optimized process parameters to the production control system to adjust the real-time key process parameters on the production line, monitor the production control system, collect the actual data of the production process after adjustment, and feed back the actual data to the data acquisition unit to form a closed-loop control.

[0078] Preferably, the multi-objective optimization algorithm is an improved NSGA-III algorithm, which processes the physical constraints and performance constraints of the production process by introducing a penalty function during the search process and outputs a set of candidate process parameter sets containing Pareto optimal solutions.

[0079] Among them, the optimization objectives of the objective function are to minimize the carbon emission value and minimize the pollution emission value. The weight values of the carbon emission value and the pollution emission value are dynamically adjusted based on the fuzzy multi-criteria decision-making algorithm, and the constraint conditions are the parameters of the production process.

[0080] Preferably, the closed-loop control is specifically as follows:

[0081] Using the continuously collected real-time data, compare it with the preset optimization target, and achieve error correction and re-optimization control by setting dynamic thresholds, so that the entire production process is always in the best state of reducing pollution and carbon emissions.

[0082] The third embodiment of the present invention provides a device for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA, including a memory and a processor. A computer program is stored in the memory, and the computer program can be executed by the processor to implement a method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA as described in any one of the above.

[0083] The fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that it stores a computer program, and the computer program can be executed by the processor of the device where the computer-readable storage medium is located to implement a method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA as described in any one of the above.

[0084] Based on a method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA provided by the present invention, by obtaining real-time data collected by an Internet of Things sensor network deployed on a lithium battery production line, the real-time data includes raw material composition, sintering temperature, energy consumption, pollutant concentration in the tail gas, and key process parameters. Then, the real-time data is fused with the data stored in a preset life cycle assessment database to generate standardized life cycle data, and the standardized life cycle data is used to reflect the actual situation of carbon emissions and pollution emissions during the production process. Then, based on the standardized life cycle data, the carbon emission value and pollution emission value during the production process are calculated to form a joint environmental evaluation index. Then, a target function is established according to the joint environmental evaluation index, and a multi-objective optimization algorithm is used to solve the target function to obtain optimized process parameters that meet preset performance constraints, where the target function is used to specify the collaborative optimization requirements for carbon emissions and pollution emissions. Finally, the optimized process parameters are transmitted to the production control system to adjust the key process parameters on the production line in real time, monitor the production control system, collect the actual data of the production process after adjustment, and feed back the actual data to form a closed-loop control, solving the problem of difficulty in achieving overall optimization of pollution reduction and carbon emission reduction.

[0085] Exemplarily, the computer program described in the third and fourth embodiments of the present invention can be divided into one or more modules. The one or more modules are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in implementing a device for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA. For example, the device described in the second embodiment of the present invention.

[0086] The so-called processor may be a Central Processing Unit (CPU), or may also be 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. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA, and uses various interfaces and circuits to connect the whole to realize various parts of the method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA.

[0087] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, a text conversion function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0088] Among them, if the implemented module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0089] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0090] As described above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A collaborative optimization method for reducing pollution and carbon emissions of lithium batteries based on LCA, characterized in that, Including: S1: Obtain the real-time data collected by the Internet of Things sensor network deployed on the lithium battery production line. The real-time data includes raw material composition, sintering temperature, energy consumption, pollutant concentration in the tail gas, and key process parameters; S2: Perform data fusion on the real-time data and the data stored in the preset life cycle assessment database. The data fusion process is as follows: Using the preset data normalization processing, data smoothing filtering, and outlier rejection algorithms, synchronously fuse the collected real-time data with the historical data stored in the life cycle assessment database to generate life cycle data in a standard format. The standardized life cycle data is used to reflect the actual situation of carbon emissions and pollution emissions during the production process; S3: Calculate the carbon emission value and pollution emission value during the production process based on the standardized life cycle data to form a combined environmental assessment index; S4: Establish an objective function according to the combined environmental assessment index, and use a multi-objective optimization algorithm to solve the objective function to obtain optimized process parameters that meet the preset performance constraints. The objective function is used to specify the collaborative optimization requirements for carbon emissions and pollution emissions; S5: Transmit the optimized process parameters to the production control system to adjust the key process parameters on the production line in real time, monitor the production control system, collect the actual data of the production process after adjustment, and feedback the actual data to step S1 to form a closed-loop control.

2. The method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA according to claim 1, wherein, The multi-objective optimization algorithm is an improved NSGA-II algorithm. During the search process, it processes the physical constraints and performance constraints of the production process by introducing a penalty function and outputs a set of candidate process parameter sets containing Pareto optimal solutions; Among them, the optimization objectives of the objective function are to minimize the carbon emission value and minimize the pollution emission value. The weight values of the carbon emission value and the pollution emission value are dynamically adjusted based on the fuzzy multi-criteria decision-making algorithm, and the constraint conditions are the parameters of the production process.

3. A collaborative optimization method for reducing pollution and carbon emissions of lithium batteries based on LCA according to claim 1, characterized in that The closed-loop control is specifically: Using the continuously collected real-time data, compare it with the preset optimization goal, and achieve error correction and re-optimization regulation by setting dynamic thresholds, so that the entire production process is always in the best state of reducing pollution and carbon emissions.

4. A collaborative optimization device for reducing pollution and carbon emissions of lithium batteries based on LCA, characterized in that, Including: A data acquisition unit for obtaining the real-time data collected by the Internet of Things sensor network deployed on the lithium battery production line. The real-time data includes raw material composition, sintering temperature, energy consumption, pollutant concentration in the tail gas, and key process parameters; A life cycle data generation unit for performing data fusion on the real-time data and the data stored in the preset life cycle assessment database. The data fusion process is as follows: Using the preset data normalization processing, data smoothing filtering, and outlier rejection algorithms, synchronously fuse the collected real-time data with the historical data stored in the life cycle assessment database to generate life cycle data in a standard format. The life cycle data in the standard format is used to reflect the actual situation of carbon emissions and pollution emissions during the production process; The combined environmental assessment index generation unit is used to calculate the carbon emission value and pollution emission value in the production process based on the standardized life cycle data, and form a combined environmental assessment index; The optimized process parameter generation unit is used to establish an objective function according to the combined environmental assessment index, and solve the objective function by using a multi-objective optimization algorithm to obtain optimized process parameters that meet the preset performance constraints. Among them, the objective function is used to specify the collaborative optimization requirements for carbon emissions and pollution emissions; The feedback adjustment unit is used to transmit the optimized process parameters to the production control system to adjust the real-time key process parameters on the production line, monitor the production control system, collect the actual data of the production process after adjustment, and feedback the actual data to the data collection unit to form a closed-loop control.

5. The collaborative optimization device for reducing pollution and carbon emissions of lithium batteries based on LCA according to claim 4, wherein, The multi-objective optimization algorithm is an improved NSGA-II algorithm, which processes the physical constraints and performance constraints of the production process by introducing a penalty function during the search process, and outputs a set of candidate process parameter sets containing Pareto optimal solutions; Among them, the optimization objectives of the objective function are to minimize the carbon emission value and minimize the pollution emission value. The weight values of the carbon emission value and the pollution emission value are dynamically adjusted based on the fuzzy multi-criteria decision-making algorithm, and the constraint conditions are the parameters of the production process.

6. The collaborative optimization device for reducing pollution and carbon emissions of lithium batteries based on LCA according to claim 4, wherein, The specific closed-loop control is as follows: Using continuously collected real-time data, compare it with the preset optimization objectives, and achieve error correction and re-optimization control by setting dynamic thresholds, so that the entire production process is always in the best state of reducing pollution and carbon emissions.

7. A lithium battery pollution reduction and carbon emission reduction collaborative optimization device based on LCA, characterized in that, It includes a memory and a processor. The memory stores a computer program, and the computer program can be executed by the processor to implement a method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, A computer program is stored, and the computer program can be executed by the processor of the device where the computer-readable storage medium is located to implement a method for collaborative optimization of pollution reduction and carbon emission reduction of lithium batteries based on LCA as described in any one of claims 1 to 3.

Images