A method for accounting for carbon emissions of energy storage lithium batteries

Abstract

The application provides a carbon emission accounting method for energy storage lithium batteries, relates to the technical field of carbon emission accounting, and can improve the accuracy of carbon emission accounting in the production and manufacturing stage of energy storage lithium battery products. The specific technical scheme is as follows: according to the overlapping relationship between the running time interval of the production equipment and the processing time interval of the battery product batch at the corresponding process node, and the preset process route, the corresponding relationship between each production equipment, each process node and each battery product batch is determined; according to the corresponding relationship, the energy consumption data of the first energy consumption object is distributed to the corresponding process node and the battery product batch; according to the corresponding relationship and the preset distribution rule, the energy consumption data of the second energy consumption object is distributed to the corresponding process node and the battery product batch; the energy consumption data of each process node and the energy consumption data of the battery product batch are matched with the emission factor library, and the carbon emission of each process node and the carbon emission of each battery product batch are calculated according to the matching result.

Description

Technical Field

[0001] This application relates to the field of carbon emission accounting technology, and in particular to a method for carbon emission accounting of energy storage lithium batteries. Background Technology

[0002] With the increasing emphasis on product carbon footprint certification and low-carbon compliance requirements for battery products, energy storage lithium battery products need to provide more detailed, reliable, and traceable product carbon footprint data in the processes of certification, green supply chain management, and low-carbon manufacturing.

[0003] The production process of energy storage lithium batteries is characterized by a long process chain, diverse equipment types, complex energy sources, a high proportion of auxiliary systems, and frequent batch changes. The manufacturing stage includes not only direct production processes such as batching, mixing, coating, drying, rolling, slitting, winding, assembly, liquid injection, formation, capacity testing, and inspection, but also auxiliary systems such as air compression, cooling, dehumidification, and cleanroom air conditioning; thermal systems such as steam supply and natural gas heating; and environmental protection systems such as waste gas treatment and wastewater treatment, making carbon emission accounting quite complex.

[0004] For the production and manufacturing stage of energy storage lithium batteries, the carbon emission calculation results obtained by the current carbon emission accounting method are inaccurate. Summary of the Invention

[0005] This application provides a method for calculating carbon emissions from energy storage lithium batteries, which can improve the accuracy of carbon emission calculation during the production and manufacturing stage of energy storage lithium batteries.

[0006] To achieve the above objectives, the embodiments of this application adopt the following technical solutions: Firstly, a method for carbon emission accounting of energy storage lithium batteries is provided. This method includes: acquiring the operating time intervals of each production equipment during the production process of energy storage lithium batteries, and the processing time intervals of each batch of battery products at each process node; determining the correspondence between each production equipment, each process node, and each batch of battery products based on the overlap between the operating time intervals of the production equipment and the processing time intervals of the battery product batches at the corresponding process nodes, and a preset process route; the preset process route is used to characterize the process nodes that the battery product batches sequentially pass through during the manufacturing stage; acquiring energy consumption data of energy-consuming objects, including production equipment; and determining the correspondence based on the... The energy consumption data of the first energy-consuming object is allocated to the corresponding process node and battery product batch; the first energy-consuming object is an energy-consuming object equipped with an independent metering device; the independent metering device is used to measure the actual energy consumption value of the energy-consuming object; according to the correspondence and preset allocation rules, the energy consumption data of the second energy-consuming object is allocated to the corresponding process node and battery product batch; the second energy-consuming object is an energy-consuming object without an independent metering device; the energy consumption data of each process node is matched with the emission factor library, and the carbon emissions of each process node are calculated based on the matching results; and the energy consumption data of each battery product batch is matched with the emission factor library, and the carbon emissions of each battery product batch are calculated based on the matching results.

[0007] In one possible implementation of the first aspect, the carbon emissions of a battery product batch are converted into the carbon emissions of a single battery product based on the output quantity or the quantity of good products corresponding to the batch; the carbon emissions of a single battery product are used to indicate the carbon emissions of a single energy storage lithium battery product during the manufacturing stage.

[0008] In another possible implementation of the first aspect, the energy consumption objects also include auxiliary systems, thermal systems, and environmental protection systems; the method also includes: establishing the association between auxiliary systems, thermal systems, environmental protection systems and process nodes; and allocating the energy consumption data of auxiliary systems, thermal systems, and environmental protection systems to the corresponding process nodes and battery product batches according to the association.

[0009] In another possible implementation of the first aspect, the energy consumption data of the auxiliary system is allocated to the corresponding process nodes and battery product batches according to the correlation relationship, including: determining the first process-level allocation weight of each process node according to the correlation relationship between the auxiliary system and each process node, and the first allocation parameter corresponding to each process node; the first allocation parameter includes at least one of actual compressed air consumption, rated air consumption of equipment, cooling water flow rate, processing time or processing load; allocating the first total energy consumption to each process node according to the first process-level allocation weight; the first total energy consumption includes the total system energy consumption data of the auxiliary system; when the same process node corresponds to multiple battery product batches, the first batch secondary allocation weight is determined according to the processing time interval, process occupancy time, product output or number of good products of each battery product batch, and the energy consumption of the auxiliary system allocated to the process node is allocated to each battery product batch based on the first batch secondary allocation weight.

[0010] In another possible implementation of the first aspect, the energy consumption data of the thermal system is allocated to the corresponding process nodes and battery product batches according to the correlation relationship. This includes: determining the second process-level allocation weight of each process node based on the correlation relationship between the thermal system and each process node, and the second allocation parameter corresponding to each process node; the second allocation parameter includes at least one of steam usage, heat load, heating duration, or equipment operating duration; allocating the second total energy consumption to each process node according to the second process-level allocation weight; the second total energy consumption includes the total system energy consumption data of the thermal system; when the same process node corresponds to multiple battery product batches, determining the second batch-level allocation weight based on the processing time interval, process occupancy time, product output, or number of good products for each battery product batch, and allocating the thermal system energy consumption allocated to the process node to each battery product batch based on the second batch-level allocation weight.

[0011] In another possible implementation of the first aspect, the energy consumption data of the environmental protection treatment system is allocated to the corresponding process nodes and battery product batches according to the correlation relationship. This includes: determining the third process-level allocation weight of each process node based on the correlation relationship between the environmental protection treatment system and each process node, and the third allocation parameter corresponding to each process node; the third allocation parameter includes at least one of waste gas generation, wastewater generation, pollutant generation, processing time, or processing load; allocating the third total energy consumption to each process node according to the third process-level allocation weight; the third total energy consumption includes the total system energy consumption data of the environmental protection treatment system; when the same process node corresponds to multiple battery product batches, the third batch-level allocation weight is determined based on the processing time interval, process occupancy time, product output, or number of good products for each battery product batch, and the energy consumption of the environmental protection treatment system allocated to the process node is allocated to each battery product batch based on the third batch-level allocation weight.

[0012] In another possible implementation of the first aspect, the energy consumption data of the second energy-consuming object is allocated to the corresponding process nodes and battery product batches according to the correspondence and preset allocation rules, including: determining the fourth batch-level allocation weight of each battery product batch according to the correspondence and the fourth allocation parameter; the fourth allocation parameter includes at least one of the effective occupancy time of the battery product batch, product output, or number of good products; allocating the energy consumption data of the second energy-consuming object to each battery product batch according to the fourth batch-level allocation weight; and aggregating the energy consumption data of the second energy-consuming object allocated to each battery product batch to the process node corresponding to the second energy-consuming object according to the correspondence.

[0013] In another possible implementation of the first aspect, a multi-level total closure verification is performed on the energy consumption data allocation results. The verification levels include at least one of the following: factory level, workshop level, production line level, equipment level, and product batch level. The multi-level total closure verification includes: for each level, comparing the sum of energy consumption data of each lower level within the level with the energy consumption data of the upper level of the level to obtain the closure deviation; when the closure deviation is less than or equal to a preset threshold, the total closure verification of the current level is determined to be passed; when the closure deviation is greater than the preset threshold, an anomaly mark is generated and anomaly identification is triggered, and the abnormal data is corrected and / or marked based on the anomaly identification results. Anomaly identification includes identifying at least one of the following anomaly types: unit anomaly, data missing, duplicate measurement, batch time overlap, equipment shutdown not removed, production output anomaly, yield rate anomaly, allocation weight anomaly, and allocation result anomaly.

[0014] In another possible implementation of the first aspect, carbon emission accounting results and corresponding traceability records are output; the carbon emission accounting results include at least the carbon emission amount of each process node, the carbon emission amount of each batch of battery products, and the carbon emission amount of a unit battery product; the traceability records include at least one of the following: energy consumption data source, preset allocation rules, emission factor, carbon emission calculation process, or total closure verification results.

[0015] In another possible implementation of the first aspect, the process nodes include one or more of the following: batching, mixing, coating, drying, rolling, slitting, winding, stacking, assembly, liquid injection, formation, volume separation, aging, testing, and packaging; the auxiliary system includes at least one of the following: compressed air system, cooling water system, dehumidification system, or clean air conditioning system; the thermal system includes at least one of the following: steam system or natural gas system; and the environmental protection system includes at least one of the following: waste gas treatment system, wastewater treatment system, or solid waste temporary storage and treatment system.

[0016] In a second aspect, an electronic device is provided, comprising: a memory and one or more processors; the memory and the processors are coupled; wherein the memory stores computer program code, the computer program code including computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the carbon emission accounting method for energy storage lithium batteries as described in any of the first aspects above.

[0017] Thirdly, a computer-readable storage medium is provided, including computer instructions that, when executed on an electronic device, cause the electronic device to perform the carbon emission accounting method for energy storage lithium batteries as described in any of the first aspects above.

[0018] Fourthly, a computer program product is provided, which, when run on a computer, causes the computer to execute the carbon emission accounting method for energy storage lithium batteries described in any of the first aspects above.

[0019] The method provided in this application establishes a correspondence between each production equipment, each process node, and each battery product batch based on the overlap between the equipment operating time interval and the battery product batch processing time interval. This improves the accuracy of the correspondence and provides an accurate data foundation for subsequent energy consumption allocation. Simultaneously, by distinguishing between energy-consuming objects with and without independent metering devices and employing different energy consumption allocation methods, energy consumption data can be accurately allocated to the corresponding process nodes and battery product batches according to actual production conditions. Finally, by matching the energy consumption data of each process node and each battery product batch with an emission factor database, the calculated carbon emissions of each process node and each battery product batch exhibit higher accuracy.

[0020] Furthermore, the system can allocate energy consumption data from auxiliary systems, thermal systems, and environmental protection systems to corresponding process nodes and battery product batches based on actual production conditions, further improving the accuracy of carbon emission calculations at each level. The system can also check for anomalies in energy consumption data through total quantity closure verification and handle anomalies promptly, thus making the calculated carbon emission accounting results more accurate. The system can also generate carbon emission accounting results and traceability records to facilitate the traceability and verification of carbon emission accounting results. Attached Figure Description

[0021] Figure 1 A schematic diagram of a carbon emission accounting system for energy storage lithium batteries provided in this application embodiment; Figure 2 A flowchart illustrating a method for calculating carbon emissions from energy storage lithium batteries, provided as an embodiment of this application; Figure 3 A schematic diagram illustrating a correspondence provided in an embodiment of this application; Figure 4 This is a schematic diagram of a total closure check provided in an embodiment of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. In the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in this application 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 alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" are not necessarily different. Meanwhile, in the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of terms such as "exemplary" or "for example" is intended to present related concepts in a concrete manner for ease of understanding.

[0023] With the increasing emphasis on product carbon footprint certification and low-carbon compliance requirements for battery products, energy storage lithium battery products need to provide more detailed, reliable, and traceable product carbon footprint data in the processes of certification, green supply chain management, and low-carbon manufacturing.

[0024] The carbon footprint of energy storage lithium battery products typically needs to cover all stages of their lifecycle, including raw material acquisition, manufacturing, use, operation and maintenance, and recycling. Among these, the manufacturing stage is a key link in the acquisition and quality control of multi-source energy consumption data for carbon emission accounting.

[0025] The manufacturing process of energy storage lithium batteries is characterized by a long process chain, diverse equipment types, complex energy sources, a high proportion of auxiliary systems, and frequent batch changes. The manufacturing stage includes not only direct production processes such as batching, mixing, coating, drying, rolling, slitting, winding, assembly, electrolyte injection, formation, capacity testing, and inspection, but also auxiliary systems such as air compressors, cooling systems, dehumidifiers, and cleanroom air conditioning; thermal systems such as steam supply and natural gas heating; and environmental protection systems such as waste gas treatment and wastewater treatment. Currently, total energy consumption data at the factory, workshop, or production line level is usually available, but this total energy consumption data cannot directly correspond to specific energy storage cells, modules, or energy storage system products.

[0026] For the production and manufacturing stage of energy storage lithium batteries, current carbon emission accounting methods yield inaccurate results. Therefore, a carbon emission accounting method and system for energy storage lithium battery products is needed to improve the accuracy of carbon emission accounting during the production and manufacturing stage.

[0027] Therefore, this application provides a method for carbon emission accounting of energy storage lithium batteries, which can improve the accuracy of carbon emission accounting during the production and manufacturing stage of energy storage lithium battery products. The method includes: obtaining the operating time intervals of each production equipment during the production process of energy storage lithium batteries, and the processing time intervals of each batch of energy storage lithium batteries at each process node; determining the correspondence between each production equipment, each process node, and each batch of battery products based on the overlap between the operating time intervals of the production equipment and the processing time intervals of the battery product batches at the corresponding process nodes, and a preset process route; the preset process route is used to characterize the process nodes that the battery product batches sequentially pass through during the production and manufacturing stage; obtaining energy consumption data of energy consumption objects, including production equipment; and, based on the correspondence, calculating the energy consumption of the first energy consumption object... Data is allocated to the corresponding process nodes and battery product batches; the first energy consumption object is an energy consumption object equipped with an independent metering device; the independent metering device is used to measure the actual energy consumption value of the energy consumption object; according to the correspondence and preset allocation rules, the energy consumption data of the second energy consumption object is allocated to the corresponding process nodes and battery product batches; the second energy consumption object is an energy consumption object without an independent metering device; the energy consumption data of each process node is matched with the emission factor library, and the carbon emissions of each process node are calculated based on the matching results; and the energy consumption data of each battery product batch is matched with the emission factor library, and the carbon emissions of each battery product batch are calculated based on the matching results.

[0028] The method provided in this application establishes a correspondence between each production equipment, each process node, and each battery product batch based on the overlap between the equipment operating time interval and the battery product batch processing time interval. This improves the accuracy of the correspondence and provides an accurate data foundation for subsequent energy consumption allocation. Simultaneously, by distinguishing between energy-consuming objects with and without independent metering devices and employing different energy consumption allocation methods, energy consumption data can be accurately allocated to the corresponding process nodes and battery product batches according to actual production conditions. Finally, by matching the energy consumption data of each process node and each battery product batch with an emission factor database, the calculated carbon emissions of each process node and each battery product batch exhibit higher accuracy.

[0029] This application provides a carbon emission accounting method for energy storage lithium batteries, which can be applied to a carbon emission accounting system for energy storage lithium batteries. Specifically, this system can be implemented by a single server, a server cluster consisting of multiple servers, a computer, or computing devices such as processors or processing chips within a server or computer. This application does not limit the specific form of the computing device.

[0030] The following is a schematic diagram of the structure of a carbon emission accounting system for energy storage lithium batteries provided in an embodiment of this application. For example... Figure 1 As shown, the system may include an accounting node configuration module, a data acquisition module, a correspondence establishment module, an energy consumption processing module, an emission factor matching module, a carbon emission calculation module, a closure verification module, and a result output and traceability module.

[0031] The accounting node configuration module is used to build an energy storage lithium battery accounting node library. For example, it configures information such as process identifier, process name, process type, workshop, production line, corresponding equipment, energy type, applicable product model, and allocation rules.

[0032] The data acquisition module is used to collect energy consumption data from the factory area, workshops, production lines, production equipment, auxiliary systems, heating systems, and environmental protection systems. This energy consumption data can come from energy management systems (EMS), manufacturing execution systems (MES), enterprise resource planning (ERP) systems, equipment data acquisition systems, smart meters, flow meters, and manual reporting systems, etc.

[0033] The correspondence establishment module is used to establish correspondences between equipment, processes, batches, and products based on production batch data, equipment operation data, process route data, and product output data; and to establish service relationships, heating relationships, or pollution treatment relationships between auxiliary systems, heating systems, and environmental protection systems and direct production process nodes.

[0034] The energy consumption processing module is used to determine energy consumption allocation rules based on the category of energy consumption objects and allocate energy consumption data to the corresponding process nodes and product batches. This module may further include a direct metering equipment allocation unit, a non-direct metering equipment allocation unit, a public auxiliary system allocation unit, a thermal system allocation unit, and an environmental protection treatment system allocation unit. These multiple units are used to allocate the energy consumption data they process to the corresponding process nodes and product batches.

[0035] The emission factor matching module is used to match the allocated energy consumption data with emission factors in the emission factor library. For example, it matches the emission factors corresponding to the allocated energy consumption data from the emission factor library based on energy type, region, time, factor source, and applicable scope.

[0036] The carbon emission calculation module is used to calculate carbon emissions at the process level, batch level, and product level, and obtain carbon emission calculation results. For example, based on energy consumption data and matched emission factors, it calculates the carbon emissions of each process node, the carbon emissions of each battery product batch, and the carbon emissions of a unit battery product.

[0037] The closure verification module is used to perform total closure verification on the energy consumption allocation results. Its input data includes equipment logs from EMS, MES, ERP and other equipment, as well as environmental protection ledgers. It performs multi-level total closure verification on the energy consumption allocation results.

[0038] The results output and traceability module is used to output the carbon emission accounting results and corresponding traceability records during the manufacturing stage of energy storage lithium battery products. The processing involves rules such as weight calculation, factor matching, total quantity verification, and anomaly identification. It can output carbon emission amounts and traceability records at the process level, batch level, and product level. For example, it can generate traceability records that include data sources, allocation rules, emission factor versions, and calculation processes. The data output by this module specifically includes data source identifiers, equipment identifiers, process identifiers, batch identifiers, product models, allocation rule identifiers, emission factor identifiers, emission factor versions, calculation results, verification results, and anomaly markers.

[0039] The following, with reference to the accompanying drawings, details a method for calculating carbon emissions from energy storage lithium batteries provided in this application. Figure 2 This is a flowchart illustrating a method for calculating carbon emissions from energy storage lithium batteries, provided as an embodiment of this application. Figure 2 As shown, the method includes steps S1 to S6.

[0040] S1. Obtain the operating time intervals of each production equipment during the production process of energy storage lithium batteries, as well as the processing time intervals of each batch of energy storage lithium battery products at each process node.

[0041] Specifically, during the production of energy storage lithium batteries, the system can acquire the operating time range of each production equipment, as well as the processing time range of each batch of energy storage lithium batteries at each process node. These process nodes may include, but are not limited to: batching, mixing, coating, drying, rolling, slitting, winding, stacking, assembly, electrolyte injection, formation, capacity testing, aging, testing, and packaging.

[0042] In one possible implementation, during the production of energy storage lithium batteries, the system can acquire the operating data of the production equipment within a target statistical period. For example, it can obtain information such as the production equipment number, start time, end time, operating status, running time, operating load, process route corresponding to the production equipment, downtime, and maintenance time from the production equipment's operating log. The start time and end time are used to determine the operating time interval of the production equipment.

[0043] Meanwhile, the system can obtain the battery product model, battery product batch number, corresponding process route, input quantity, output quantity, number of good products, production start time and production end time from the battery product batch data. This allows the system to obtain the processing start time and processing end time of each battery product batch at the corresponding process node. The processing start time and processing end time are used to determine the processing time interval of the battery product batch at the corresponding process node.

[0044] For example, on date A, the operating time range of coating machine B is 08:00-12:00. The processing time range of battery product batch C on coating machine B is 09:00-11:00, and the processing time range of battery product batch D on coating machine B is 13:00-15:00. Therefore, there is a time overlap between coating machine B and battery product batch C at the corresponding process nodes on the coating machine, but no time overlap between coating machine B and battery product batch D at the corresponding process nodes on the coating machine. This time overlap relationship will be used to subsequently determine the correspondence between production equipment (such as coating machine B), process nodes (such as the process corresponding to coating machine B), and battery product batches (such as battery product batch C and battery product batch D).

[0045] In one possible implementation, the system may store a database of accounting nodes corresponding to the production and manufacturing stages of energy storage lithium batteries. This database of accounting nodes may be pre-built. The database of accounting nodes is used to describe and represent the accounting relationships between various energy consumption objects and the product manufacturing process from the entry of raw materials into the production line to the delivery of finished products for energy storage lithium batteries.

[0046] In some examples, the accounting node library may include direct production process nodes (or simply process nodes).

[0047] In other examples, the accounting node library may also include auxiliary accounting nodes. These auxiliary accounting nodes include public utility system nodes, thermal system nodes, and environmental protection system nodes. Public utility system nodes may include, but are not limited to, compressed air system nodes, cooling water system nodes, dehumidification system nodes, and cleanroom air conditioning system nodes. Thermal system nodes may include, but are not limited to, steam supply system nodes and natural gas heating system nodes. Environmental protection system nodes may include, but are not limited to, waste gas treatment system nodes, wastewater treatment system nodes, and solid waste temporary storage and treatment system nodes.

[0048] Each accounting node must be configured with at least the following: node identifier, node name, node type, workshop to which it belongs, production line to which it belongs, associated production equipment or system, energy type, applicable product model, whether it is directly metered, allocation rule identifier, and life cycle stage attribute.

[0049] The direct production process nodes characterize the actual processing, assembly, formation, and testing of products (such as energy storage lithium batteries). Auxiliary system nodes characterize energy-consuming entities that provide auxiliary conditions such as compressed air, cooling, dehumidification, or clean environments to one or more direct production processes. Thermal system nodes characterize energy-consuming entities that provide steam, gas heat sources, or other thermal media to one or more direct production processes. Environmental treatment system nodes characterize energy-consuming entities that centrally treat waste gas, wastewater, or solid waste generated during the production process. Auxiliary system nodes, thermal system nodes, and environmental treatment system nodes are associated with direct production process nodes as auxiliary accounting nodes and are not considered actual product processing steps.

[0050] In this way, the accounting node library maintained by the system can quickly and accurately help with subsequent energy consumption allocation and carbon emission accounting result processing.

[0051] S2. Based on the overlap between the operating time interval of the production equipment and the processing time interval of the battery product batch at the corresponding process node, and the preset process route, determine the correspondence between each production equipment, each process node and each battery product batch; the preset process route is used to characterize the process nodes that the battery product batch passes through in sequence during the manufacturing stage.

[0052] Specifically, after obtaining the operating time intervals of each production equipment and the processing time intervals of each batch of energy storage lithium batteries at each process node, the system can determine the correspondence between each production equipment, each process node, and each batch of battery products based on the overlap between the operating time intervals of the production equipment and the processing time intervals of the battery product batches at each process node, as well as the preset process route.

[0053] For example, the preset process routes for battery product batches C and D both include the process node (such as the coating process node) corresponding to coating machine B. Based on the time overlap relationship determined in S1: coating machine B and battery product batch C have time overlap at this coating process node, while coating machine B and battery product batch D do not have time overlap at this coating process node. Combining the above preset process routes, it can be determined that: there is a correspondence between coating machine B, the coating process node corresponding to coating machine B, and battery product batch C; while there is no correspondence between coating machine B and battery product batch D at this coating process node. Therefore, based on the overlap relationship between the operating time interval of the production equipment and the processing time interval of the battery product batch at the corresponding process node, combined with the preset process routes, the correspondence between each production equipment, each process node, and each battery product batch can be determined.

[0054] It is understandable that energy storage lithium batteries within the same batch of battery products should have the same model number.

[0055] In one possible implementation, such as Figure 3 As shown, after acquiring the operating time intervals of each production equipment and the processing time intervals of each batch of energy storage lithium batteries at each process node, the system determines the time matching relationship between production equipment and battery product batches based on the overlap of their operating time intervals on the time axis. Then, combined with the preset process route, the configuration relationship between production equipment and process nodes, and the product model, the system establishes a correspondence between production equipment, process nodes, battery product batches, and battery products. This ensures that each energy consumption can be traced back to a specific process node and battery product batch. Furthermore, for auxiliary systems, heating systems, and environmental protection systems, the carbon emission accounting system can also establish service relationships, heating relationships, or pollution treatment relationships between each auxiliary system, heating system, and environmental protection system and the direct production process nodes, based on their service targets, heating targets, or pollutant sources.

[0056] The battery batch data refers to data recording the production process information of each batch of energy storage lithium batteries, including at least the start and end times of processing at the corresponding process nodes. The operational data within the production equipment refers to data recording the operating status of the production equipment, including at least the start and end times of operation. The preset process route refers to data recording the sequence of process nodes traversed by the energy storage lithium battery products and the correspondence between each process node and the production equipment.

[0057] For example, the system can determine the equipment-process correspondence between each production equipment and each direct production process node based on process identifiers, preset process routes, and equipment-process configuration relationships. Then, based on the overlap between the operating time intervals of the production equipment and the processing time intervals of the battery product batches at the corresponding direct production process nodes, the system determines the time matching relationship between the production equipment and the battery product batches. Next, the system combines the above equipment-process correspondence and preset process routes to associate the production equipment, process nodes, and battery product batches. Finally, the system determines the product model corresponding to the battery product batch based on product output data, thereby establishing the correspondence between production equipment, process nodes, battery product batches, and battery products. Simultaneously, the system can also establish service relationships, heating relationships, or pollution treatment relationships between each auxiliary system, heating system, environmental treatment system, and process node based on the service objects, heating objects, or pollutant sources of each auxiliary system, heating system, and environmental treatment system.

[0058] S3. Obtain the energy consumption data of the energy consumption object, which includes production equipment.

[0059] In one possible implementation, the system can acquire energy consumption data of the energy-consuming object within a target statistical period. This target statistical period can be set to an hour, shift, day, week, month, or production batch cycle of battery products, depending on actual accounting needs.

[0060] This energy consumption data can be collected at various levels, including factory area, workshop, and production line, encompassing energy consumption data from production equipment, auxiliary systems, heating systems, and environmental protection systems (hereinafter referred to as energy consumption data). Specifically, this energy consumption data can include, but is not limited to, various types of data such as: electricity consumption data (e.g., total electricity consumption in the factory area, total electricity consumption in the workshop, total electricity consumption in the production line, and electricity consumption of specific generating equipment), steam consumption data, natural gas consumption data, compressed air consumption data, cooling system energy consumption data, dehumidification system energy consumption data, cleanroom air conditioning system energy consumption data, waste gas treatment system energy consumption data, and wastewater treatment system energy consumption data. Simultaneously, the system can also obtain information such as timestamps, metering point identifiers, and data source identifiers corresponding to the aforementioned energy consumption data.

[0061] Energy consumption data refers to the energy consumption data itself collected from the factory area, workshops, production lines, production equipment, auxiliary systems, heating systems, and environmental protection systems. Data such as equipment operating time, operating load rate, product output, number of good products, gas consumption, cooling load, service area area, and pollutant generation, used to determine the distribution ratio of energy consumption among different processes or batches, are allocation parameters and are not considered energy consumption data itself. Energy consumption data and allocation parameters are distinguished from each other.

[0062] S4. According to the correspondence, the energy consumption data of the first energy-consuming object is allocated to the corresponding process node and battery product batch; the first energy-consuming object is an energy-consuming object equipped with an independent metering device; the independent metering device is used to measure the actual energy consumption value of the energy-consuming object.

[0063] For example, the energy-consuming object can be production equipment, and the first energy-consuming object can be production equipment equipped with an independent metering device. Specifically, for production equipment equipped with an independent metering device, the system can obtain the actual energy consumption data of the equipment within the processing time interval of the battery product batch, and allocate the actual energy consumption data to the corresponding process node and battery product batch based on the correspondence between the production equipment, each process node, and each battery product batch. For example, coating machine B is equipped with an independent metering device, and during the processing time interval of battery product batch C at 09:00... Within 11:00, the independent metering device recorded an actual electricity consumption of 80 kWh. The system can directly allocate this 80 kWh to the coating process node and battery product batch C.

[0064] S5. Based on the correspondence and preset allocation rules, allocate the energy consumption data of the second energy consumption object to the corresponding process node and battery product batch; the second energy consumption object is an energy consumption object that does not have an independent metering device.

[0065] In one possible implementation, the system can determine energy consumption allocation rules according to the category of energy-consuming objects. The categories of energy-consuming objects include at least: production equipment with independent metering devices (or directly metered production equipment), production equipment without independent metering devices (or indirectly metered production equipment), auxiliary systems, thermal systems, and environmental protection systems. The system can allocate energy consumption data to each process node and each battery product batch based on the energy consumption allocation rules and corresponding relationships. The specific allocation method varies depending on the category of energy-consuming object. For production equipment with independent metering devices, the energy consumption allocation method can be found in step S4 and will not be repeated here.

[0066] In one possible implementation, for production equipment without independent metering devices, the system can determine the fourth batch-level allocation weight for each battery product batch based on the production equipment without independent metering devices, the correspondence between each process node and each battery product batch, and a fourth allocation parameter. This fourth allocation parameter includes at least one of the effective occupancy time of the battery product batch, product output, or the number of good products. Subsequently, based on the fourth batch-level allocation weight, the energy consumption data of the second energy-consuming object is allocated to each battery product batch. Finally, based on the correspondence, the energy consumption data of the second energy-consuming object allocated to each battery product batch is aggregated to the corresponding process node. In this way, the system can allocate the energy consumption data of production equipment without independent metering devices to the corresponding process nodes and battery product batches according to the actual production situation, thereby improving the accuracy of calculating carbon emissions at each level.

[0067] For example, for production equipment without an independent metering device, the system acquires the rated power, actual operating time, and operating load rate of the production equipment to determine the estimated total energy consumption of the equipment within a target statistical period. In one implementation, the estimated total energy consumption can be determined by multiplying the rated power, actual operating time, and average operating load rate of the equipment.

[0068] When the same equipment corresponds to multiple battery product batches, the effective occupancy time of each battery product batch is determined based on the overlap between the equipment's operating time interval and the processing time interval of each batch. One or more of the following—effective occupancy time, product output, and number of good products—are used as the fourth allocation parameter. Subsequently, the fourth allocation parameter for each battery product batch is normalized to obtain the fourth batch-level allocation weight. For example, for the first batch... i For each batch of battery products, the first allocation parameter X is determined by factors such as effective usage time, product output, or number of good products. i and in accordance with W i =X i / ΣX i Normalize the first allocation parameter to obtain the second... i The fourth allocation weight W for each battery product batch i Subsequently, the estimated total energy consumption of the equipment is allocated to each battery product batch according to the fourth allocation weight, and then aggregated to the direct production process node corresponding to the equipment. If the equipment serves only one battery product batch, no allocation is required, and it is directly aggregated to that battery product batch.

[0069] For example, a roller press F without an independent metering device serves battery product batches C and D. During the statistical period, roller press F has a rated power of 50kW, operates for 4 hours, and has an average load rate of 80%. Therefore, the estimated total energy consumption is 50 × 4 × 0.8 = 160kWh. Battery product batch C has an effective operating time of 2 hours, and battery product batch D has an effective operating time of 2 hours. Therefore, their first allocation weight is 0.5, and each is allocated 80kWh. This allocated 80kWh is then aggregated to the roller pressing process node and the corresponding battery product batch.

[0070] In another possible implementation, for the auxiliary system, the heating system, and the environmental protection system, the system can obtain the corresponding total energy consumption data of the system, and based on the established service relationship, heating relationship, or pollution treatment relationship between the auxiliary system, the heating system, the environmental protection system and the process node, the system can allocate the total energy consumption data of the auxiliary system, the heating system, and the environmental protection system to the corresponding process node and battery product batch.

[0071] In some examples, the system can determine the first process-level allocation weight of each process node based on the relationship between the auxiliary system and each process node, and the first allocation parameters corresponding to each process node. The first allocation parameters include at least one of the following: actual compressed air consumption, rated air consumption of the equipment, cooling water flow rate, processing time, or processing load. Subsequently, the first total energy consumption is allocated to each process node according to the first process-level allocation weight. The first total energy consumption includes the total energy consumption data of the auxiliary system. When the same process node corresponds to multiple battery product batches, the first batch of secondary allocation weights is determined based on the processing time interval, process occupancy time, product output, or number of good products for each battery product batch. Based on the first batch of secondary allocation weights, the energy consumption of the auxiliary system allocated to the process node is then allocated to each battery product batch. In this way, the system can allocate the energy consumption data of the auxiliary system to the corresponding process nodes and battery product batches according to the actual production situation, thereby improving the accuracy of calculating carbon emissions at each level.

[0072] For example, for auxiliary systems (including compressed air systems, cooling water systems, dehumidification systems, cleanroom air conditioning systems, etc.), the carbon emission accounting system can determine the process nodes it serves based on pipeline connections, service areas, or equipment connections, and obtain the total energy consumption data of the auxiliary systems within the target statistical period. For instance, for compressed air systems, one or more of the following parameters are used as process-level allocation parameters: actual compressed air consumption for each direct production process, the product of the equipment's rated air consumption and operating time, or the production line's air consumption ratio; for cooling water systems, one or more parameters are used as process-level allocation parameters: equipment cooling load, cooling water flow rate, equipment operating time, or process heat load; for dehumidification and cleanroom air conditioning systems, one or more parameters are used as process-level allocation parameters: service area area, area usage time, process operating time, or air handling load. The system normalizes the process-level allocation parameters corresponding to each process node to obtain the corresponding process-level allocation weight. Subsequently, based on the corresponding process-level allocation weight, the system allocates the total energy consumption of the auxiliary systems to the corresponding process nodes. When the same direct production process node corresponds to multiple product batches, the batch-level allocation weight is determined based on the processing time interval, process duration, product output or number of good products of each product batch, and the total system energy consumption of the auxiliary system already allocated to the process node is further allocated to each battery product batch.

[0073] For example, the total energy consumption of the compressed air system is 120 kWh, serving the coating and slitting processes. The coating process accounts for 60% of the air consumption, and the slitting process accounts for 40%. Therefore, the compressed air system allocates 72 kWh to the coating process and 48 kWh to the slitting process. If the coating process only involves battery product batch C within the statistical period, then all 72 kWh is attributed to battery product batch C; if multiple batches are involved, further batch-level allocation is performed based on batch processing time or output.

[0074] In other examples, the system can determine the second process-level allocation weight of each process node based on the correlation between the thermal system and each process node, and the second allocation parameters corresponding to each process node. The second allocation parameters include at least one of steam usage, heat load, heating duration, or equipment operating time. Then, based on the second process-level allocation weight, the second total energy consumption is allocated to each process node. The second total energy consumption includes the total energy consumption data of the thermal system. When the same process node corresponds to multiple battery product batches, the second batch-level allocation weight is determined based on the processing time interval, process occupancy time, product output, or number of good products for each battery product batch. Based on the second batch-level allocation weight, the thermal system energy consumption allocated to the process node is then allocated to each battery product batch. In this way, the system can allocate the energy consumption data of the thermal system to the corresponding process nodes and battery product batches according to the actual production situation, thereby improving the accuracy of calculating carbon emissions at each level.

[0075] For example, for a thermal system (including a steam supply system, a natural gas heating system, etc.), the system determines the heating relationship between the thermal system and the process nodes based on the pipeline connection relationship, the heating area, and the heat-using equipment. For instance, in the case where multiple direct production process nodes share a thermal system and do not have independent metering devices, the system determines and normalizes process-level allocation parameters based on one or more of the steam consumption, heat load, heating duration, or equipment operating time of each process to obtain the corresponding process-level allocation weight. Subsequently, the system allocates the energy consumption of the thermal system to the corresponding direct production process node based on the corresponding process-level allocation weight. When the same direct production process node involves multiple product batches, the corresponding thermal energy consumption is further allocated to the corresponding battery product batch based on the processing time interval, process occupancy time, or product output of each product batch. As another example, for process nodes equipped with independent steam meters, gas meters, or calorific values, the actual measured steam consumption, natural gas consumption, or calorific value is directly aggregated to the corresponding process node.

[0076] In other examples, the system determines the third-level allocation weight of each process node based on the relationship between the environmental protection system and each process node, as well as the third allocation parameters corresponding to each process node. The third allocation parameters include at least one of the following: waste gas generation, wastewater generation, pollutant generation, processing time, or processing load. Subsequently, based on the third-level allocation weight, the third total energy consumption is allocated to each process node. The third total energy consumption includes the total energy consumption data of the environmental protection system. When the same process node corresponds to multiple battery product batches, the third batch-level allocation weight is determined based on the processing time interval, process duration, product output, or number of good products for each battery product batch. Based on the third batch-level allocation weight, the energy consumption of the environmental protection system allocated to the process node is then allocated to each battery product batch. In this way, the system can allocate the energy consumption data of the environmental protection system to the corresponding process nodes and battery product batches according to the actual production situation, thereby improving the accuracy of the calculated carbon emissions at each level.

[0077] For example, for environmental protection systems (including waste gas treatment systems, wastewater treatment systems, etc.), the system establishes the pollution treatment relationship between the environmental protection system and the direct production process nodes that generate the corresponding waste gas, wastewater, or other pollutants, and obtains the total energy consumption of the environmental protection system within the target statistical period. For example, for the waste gas treatment system, one or more of the following parameters are used as process-level allocation parameters: waste gas generation, pollutant generation, treatment air volume, treatment time, or treatment load corresponding to each direct production process; for the wastewater treatment system, one or more of the following parameters are used as process-level allocation parameters: wastewater generation, pollutant generation, treatment water volume, treatment time, or treatment load corresponding to each direct production process. The system normalizes the process-level allocation parameters of each pollution source process to obtain the corresponding process-level allocation weight. Subsequently, based on the corresponding process-level allocation weight, the system first allocates the total energy consumption of the environmental protection system to the corresponding direct production process nodes; for cases where the same pollution source process involves multiple product batches, the batch-level allocation weight is determined according to the product batch processing time, product output, number of good products, or the pollutant generation corresponding to the batch, and the corresponding environmental protection system energy consumption is further aggregated to the corresponding product batch.

[0078] S6. Match the energy consumption data of each process node with the emission factor library, and calculate the carbon emissions of each process node based on the matching results; and match the energy consumption data of each battery product batch with the emission factor library, and calculate the carbon emissions of each battery product batch based on the matching results.

[0079] After allocating energy consumption data for energy-consuming objects to various process nodes and battery product batches, the system can match the energy consumption data of each process node with an emission factor database and calculate the carbon emissions of each process node based on the matching results. Similarly, the system can match the energy consumption data of each battery product batch with the emission factor database and calculate the carbon emissions of each battery product batch based on the matching results. Furthermore, the system converts the carbon emissions of the battery product batch into the carbon emissions per unit battery product based on the output quantity or the number of good products corresponding to each battery product batch. This carbon emissions per unit battery product is used to indicate the carbon emissions of a unit energy storage lithium battery product during the manufacturing stage.

[0080] In one possible implementation, the system matches the allocated energy consumption data with an emission factor database. Carbon emissions are calculated by multiplying the energy consumption data by the corresponding emission factor. For a single process node, the carbon emissions from different energy-consuming activities at that process node are summed to obtain the carbon emissions for that process node. For a single battery product batch, the carbon emissions belonging to that battery product batch from all process nodes it passes through are summed to obtain the carbon emissions for that battery product batch. For a single battery, the carbon emissions for that battery product batch are converted into the carbon emissions per unit battery product based on the batch's output quantity, number of good products, or functional units.

[0081] For example, battery product batch C has a total energy consumption of 80 kWh and a steam consumption of 0.5 t at the coating process node. The electricity emission factor is 0.5703 tCO2 / MWh (tons of carbon dioxide per megawatt-hour), and the steam emission factor is 2.6 tCO2 / t (tons of carbon dioxide per ton). Therefore, the carbon emissions from the electricity process at the coating process node = 80 kWh × 0.5703 kgCO2 / kWh = 45.624 kgCO2; the carbon emissions from the steam process = 0.5 t × 2.6 tCO2 / t = 1.3 tCO2; the total carbon emissions from the coating process node = 1.345624 tCO2. Summing up the carbon emissions from all process nodes in battery product batch C yields the batch-level carbon emissions; dividing this by the number of good products gives the carbon emissions per battery.

[0082] In addition to the carbon emission accounting methods described in steps S1 to S6 above, step S7 may also be included optionally.

[0083] S7. Perform multi-level total closure verification on the energy consumption allocation results, correct and / or mark the abnormal data detected, and output the carbon emission accounting results and corresponding source traceability records.

[0084] The verification level can include at least one of the following: factory level, workshop level, production line / equipment level, and product batch / process level.

[0085] In one possible implementation, the multi-level total closure verification includes: for each level, comparing the sum of energy consumption data of each lower level within the level with the energy consumption data of the upper level of the level to obtain the closure deviation; when the closure deviation is less than or equal to a preset threshold, the total closure verification of the current level is determined to be passed; when the closure deviation is greater than the preset threshold, an anomaly mark is generated and anomaly identification is triggered, and the abnormal data is corrected and / or marked based on the result of the anomaly identification.

[0086] The anomaly identification includes identifying at least one of the following anomaly types: unit anomaly, missing data, duplicate measurement, batch time overlap, equipment shutdown not removed, production anomaly, yield rate anomaly, energy meter anomaly, allocation weight anomaly, emission factor missing, and allocation result anomaly.

[0087] For anomalous data that can be corrected, the system corrects the data based on the anomaly identification results and re-executes the corresponding energy consumption collection or allocation and the closure check of that level and related downstream levels. For anomalous data that cannot be automatically corrected, the system retains the anomaly marker and records the cause of the anomaly. In this way, the total closure check can check whether energy consumption data has become abnormal and handle the anomalies in a timely manner, thereby making the calculated carbon emission accounting results more accurate.

[0088] For example, such as Figure 4 As shown, the system first performs a total closure check at the plant level. At the plant level, the system compares the aggregated energy consumption data of each workshop within the plant with the total energy consumption data of the plant, and calculates the closure deviation. If the closure deviation is less than or equal to a preset threshold, the plant level check is considered passed, and the system proceeds to the workshop level check; if the closure deviation is greater than the preset threshold, an anomaly flag is generated and anomaly identification is triggered. The identified abnormal data is corrected and / or marked, and the plant level check can be re-executed.

[0089] In the total energy consumption closure verification at the workshop level, the system compares the aggregated energy consumption data of each production line / equipment within the workshop with the total energy consumption data of the workshop and calculates the closure deviation. If the closure deviation is less than or equal to a preset threshold, the workshop level verification is deemed to have passed, and the process proceeds to the production line / equipment level verification. If the closure deviation is greater than the preset threshold, an anomaly flag is generated and anomaly identification is triggered. The identified abnormal data is corrected and / or flagged, and the workshop level verification can be re-executed.

[0090] In the total closure verification at the production line / equipment layer, the system compares the aggregated energy consumption data of each production process associated with the production line / equipment layer with the total energy consumption data of the production line / equipment, and calculates the closure deviation. If the closure deviation is less than or equal to a preset threshold, the production line / equipment layer verification is deemed to have passed, and the system proceeds to the production process and product batch layer verification (or can be considered as product batch layer verification). If the closure deviation is greater than the preset threshold, an anomaly flag is generated and anomaly identification is triggered. The identified abnormal data is corrected and / or flagged, and the production line / equipment layer verification can be re-executed.

[0091] In the total closure verification of the production process and product batch layers, the system compares the aggregated energy consumption data of each product batch within the production process with the total energy consumption data of the process to calculate the closure deviation. If the closure deviation is less than or equal to a preset threshold, the verification at that level is considered to have passed; if the closure deviation is greater than the preset threshold, an anomaly flag is generated and anomaly identification is triggered. The identified abnormal data is corrected and / or flagged, and the verification at that level is re-executed.

[0092] In another possible implementation, the system can also perform multi-level total closure verification on the carbon emission calculation results. Specifically, the system compares the sum of carbon emissions from each lower level within a certain level with the total carbon emissions from the upper level of that level to obtain the closure deviation; when the closure deviation is less than or equal to a preset threshold, the total closure verification of the current level is deemed to have passed; when the closure deviation is greater than the preset threshold, an anomaly marker is generated and anomaly identification is triggered, and the abnormal data is corrected and / or marked based on the results of the anomaly identification.

[0093] For example, the system compares the total carbon emissions of each workshop with the total carbon emissions of the factory area, and compares the total carbon emissions of each production line with the total carbon emissions of the workshop. If the difference between the total carbon emissions of a certain level and the total carbon emissions of the corresponding higher level is greater than a preset threshold, the system generates an anomaly marker and triggers anomaly identification, and corrects and / or marks the abnormal data based on the results of the anomaly identification.

[0094] In another possible implementation, after completing the total closure verification, the system can output the carbon emission accounting results and corresponding traceability records for the production and manufacturing stages of energy storage lithium battery products. The carbon emission accounting results should include at least the carbon emission amounts at the process level, batch level, product level, and unit functional unit level, such as the carbon emission amounts at each process node, each batch of battery products, and the carbon emission amount per unit of battery product.

[0095] The traceability record includes at least the energy consumption data source, preset allocation rules, emission factors, the calculation process of carbon emissions or the total emission closure verification result, and version information to support the traceability and verification of the results. Furthermore, the system can store the calculation results and traceability records in the carbon footprint assessment database for subsequent report preparation, verification, and compliance disclosure.

[0096] In some examples, taking a certain type of energy storage lithium battery cell as an example, the production process of this cell includes direct production steps such as material preparation, coating, rolling, slitting, assembly, electrolyte injection, formation, capacity testing, inspection, and packaging. The system first obtains the production batch data of this type of cell, including batch number, processing time of each step, input quantity, output quantity, and number of good products, and establishes the correspondence between production equipment, direct production steps, product batches, and products. Subsequently, the system collects energy consumption data for electricity, steam, natural gas, compressed air, cooling, dehumidification, and waste gas treatment in the corresponding workshops and production lines, and establishes service relationships, heating relationships, or pollution treatment relationships between auxiliary systems, heating systems, and environmental protection systems and the relevant direct production steps.

[0097] For the coating process, the system directly aggregates the actual electricity consumption of the coating equipment to the corresponding battery cell batch. For steam used in the coating process, if an independent steam metering device is installed, the system aggregates the actual steam consumption. If multiple processes share a steam supply system, the process-level allocation weight is determined based on the steam usage, heating duration, or heat load of each process. For organic waste gas generated during coating, the energy consumption of the waste gas treatment system is first allocated to the coating process based on the waste gas generation or treatment load, and then aggregated to the corresponding battery cell batch based on the batch processing time or output. For the formation process, the system determines the corresponding batch energy consumption based on the overlap between the formation equipment operating time interval and the battery cell batch processing time interval, as well as the equipment load rate. For the dehumidification system and cleanroom air conditioning system, the system performs process-level allocation based on the service area area, process occupancy time, or air handling load, and then further aggregates the relevant energy consumption to the corresponding product batch based on the batch processing time.

[0098] The system matches the process-level and batch-level multi-source energy consumption data, which have been directly collected, estimated, or allocated, with corresponding emission factors to calculate the carbon emissions during the production and manufacturing stage of that batch of battery cells. It then further converts this carbon emissions into the carbon emissions per battery cell during the production and manufacturing stage based on the number of good products. The system then performs closure checks at the factory area, workshop, production line / equipment, direct production process, and product batch levels. If any level's closure deviation exceeds a preset threshold, anomaly identification and correction are triggered, and the corresponding allocation and verification are re-executed. Finally, the system outputs the carbon footprint results and traceability records for the production and manufacturing stage of that model of battery cell. These traceability records display the corresponding direct production process, equipment, product batch, energy data, mapping relationship, allocation rules and weights, emission factor version, verification results, and anomaly handling process.

[0099] It is understood that the method provided in the embodiments of this application can also be applied to carbon emission accounting in the production and manufacturing stage of products such as energy storage modules and energy storage systems.

[0100] In summary, the method provided in this application can drill down energy consumption data at the factory or workshop level to the process, batch and product levels, improving the accuracy, traceability and reproducibility of carbon emission accounting in the production and manufacturing stage of energy storage lithium battery products. It is applicable to carbon emission accounting, compliance disclosure and low-carbon production management of energy storage cells, modules and energy storage system products.

[0101] This application establishes a correspondence between equipment, process, and batch based on the overlap between equipment operating time intervals and battery product batch processing time intervals, avoiding the arbitrariness of manual energy consumption allocation. At the same time, it uses actual measurement values ​​for directly metered production equipment and estimates and allocates energy consumption data based on parameters such as rated power, operating time, and load rate for non-directly metered production equipment, making the energy consumption data allocation closer to the actual production situation, thereby improving the accuracy of carbon emission accounting at the process and batch levels.

[0102] The production of energy storage lithium batteries involves multiple process nodes, including material preparation, coating, rolling, slitting, assembly, electrolyte injection, formation, capacity testing, and packaging. Furthermore, the same production equipment may serve multiple product batches. This application constructs an accounting node library and preset process routes, and automatically establishes the correspondence between production equipment, process nodes, and battery product batches by utilizing time overlap relationships. This enables it to adapt to the complex production process of energy storage lithium batteries and achieve refined carbon emission accounting.

[0103] This application targets indirect energy-consuming objects such as auxiliary systems, thermal systems, and environmental protection systems. It determines the allocation weight based on their service objects and operating loads (such as equipment gas consumption, cooling load, workshop area, process running time, and waste gas generation). This rationally allocates the energy consumption of auxiliary systems and environmental protection systems to the corresponding process nodes and battery product batches, avoiding the aggregation bias caused by simply allocating based on output or area. This achieves the rational aggregation of energy-consuming objects in auxiliary systems, thermal systems, and environmental protection systems.

[0104] This application performs multi-level total closure verification and anomaly identification on energy consumption allocation results and carbon emission calculation results, and corrects or marks abnormal data based on the verification results to ensure data quality. At the same time, the output traceability record includes at least the data source, allocation rules, emission factors, calculation process, verification results and version information, so that each energy consumption and carbon emission can be traced back to the specific process node, production equipment, energy type and product batch, thereby improving the traceability and reproducibility of the accounting results.

[0105] The carbon emission accounting results output by this application cover carbon emissions at the process level, batch level, product level, and unit functional unit level, while providing complete traceability records. These results can be directly used for product carbon footprint report preparation, third-party verification, and compliance disclosure, thereby supporting product carbon footprint compliance disclosure and low-carbon production management.

[0106] It is understood that the various methods, embodiments, or logical steps described above can be combined or referenced to construct more complex implementation methods, provided that they do not conflict with each other. This application does not impose any restrictions on this and aims to provide flexible technical solutions to support different actual needs.

[0107] This application provides a carbon emission accounting device for energy storage lithium batteries, which can be understood as a collection of functional modules that implement the above-mentioned data processing method.

[0108] This application provides an electronic device that may include a memory and one or more processors. The memory and processors are coupled. The memory stores computer program code, including computer instructions. When the processor executes the computer instructions, the electronic device can perform various functions or steps performed by the energy storage lithium battery carbon emission accounting system in the above method embodiments.

[0109] This application also provides a computer-readable storage medium including computer instructions that, when executed on an electronic device, cause the electronic device to perform various functions or steps of the energy storage lithium battery carbon emission accounting system described in the above method embodiments.

[0110] This application also provides a computer program product that, when run on a computer, causes the computer to execute various functions or steps performed by the energy storage lithium battery carbon emission accounting system in the above method embodiments.

[0111] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0112] The electronic devices, computer-readable storage media, or computer program products provided in this application are all used to perform the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0114] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0116] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially or in other words, the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for calculating carbon emissions from energy storage lithium batteries, characterized in that, include: The operating time intervals of each production equipment during the production process of energy storage lithium batteries are obtained, as well as the processing time intervals of each batch of energy storage lithium battery products at each process node. Based on the overlap between the operating time interval of the production equipment and the processing time interval of the battery product batch at the corresponding process node, and the preset process route, the correspondence between each production equipment, each process node and each battery product batch is determined. The preset process route is used to characterize the process nodes that a batch of battery products goes through sequentially during the manufacturing stage. Obtain energy consumption data of an energy-consuming object, wherein the energy-consuming object includes the production equipment; According to the correspondence, the energy consumption data of the first energy-consuming object is allocated to the corresponding process node and battery product batch; the first energy-consuming object is an energy-consuming object equipped with an independent metering device; the independent metering device is used to measure the actual energy consumption value of the energy-consuming object; According to the correspondence and preset allocation rules, the energy consumption data of the second energy consumption object is allocated to the corresponding process node and battery product batch; the second energy consumption object is an energy consumption object that does not have an independent metering device. The energy consumption data of each process node is matched with the emission factor database, and the carbon emissions of each process node are calculated based on the matching results; and the energy consumption data of each battery product batch is matched with the emission factor database, and the carbon emissions of each battery product batch are calculated based on the matching results.

2. The method according to claim 1, characterized in that, The method further includes: The carbon emissions of a battery product batch are converted into the carbon emissions of a single battery product based on the output quantity or the quantity of good products corresponding to that batch. The carbon emissions of a single battery product are used to indicate the carbon emissions of a single energy storage lithium battery product during the manufacturing stage.

3. The method according to claim 1, characterized in that, The energy-consuming objects also include auxiliary systems, heating systems, and environmental protection systems; The method further includes: Establish the relationship between the auxiliary system, the heating system, the environmental protection system and the process nodes; Based on the aforementioned relationships, the energy consumption data of the auxiliary systems, thermal systems, and environmental protection systems are allocated to the corresponding process nodes and battery product batches.

4. The method according to claim 3, characterized in that, The step of allocating the energy consumption data of the auxiliary system to the corresponding process nodes and battery product batches according to the association relationship includes: Based on the relationship between the auxiliary system and each process node, and the first allocation parameter corresponding to each process node, the first process-level allocation weight of each process node is determined; the first allocation parameter includes at least one of the following: actual compressed air consumption, rated air consumption of equipment, cooling water flow rate, processing time, or processing load. Based on the first process-level allocation weight, the first total energy consumption is allocated to each process node; the first total energy consumption includes the total system energy consumption data of the auxiliary system; When the same process node corresponds to multiple battery product batches, the first batch secondary allocation weight is determined according to the processing time interval, process duration, product output or number of good products of each battery product batch, and the auxiliary system energy consumption allocated to the process node is allocated to each battery product batch based on the first batch secondary allocation weight.

5. The method according to claim 3, characterized in that, The step of allocating the energy consumption data of the thermal system to the corresponding process nodes and battery product batches according to the association relationship includes: Based on the relationship between the thermal system and each process node, and the second allocation parameter corresponding to each process node, the second process-level allocation weight of each process node is determined; the second allocation parameter includes at least one of steam consumption, heat load, heating duration or equipment operating duration. Based on the second process-level allocation weight, the second total energy consumption is allocated to each process node; the second total energy consumption includes the total system energy consumption data of the thermal system; When the same process node corresponds to multiple battery product batches, the second batch-level allocation weight is determined based on the processing time interval, process duration, product output or number of good products of each battery product batch, and the thermal system energy consumption allocated to the process node is allocated to each battery product batch based on the second batch-level allocation weight.

6. The method according to claim 3, characterized in that, The step of allocating the energy consumption data of the environmental protection treatment system to the corresponding process nodes and battery product batches according to the aforementioned correlation includes: Based on the relationship between the environmental protection treatment system and each process node, and the third allocation parameter corresponding to each process node, the third process-level allocation weight of each process node is determined; the third allocation parameter includes at least one of the following: waste gas generation, wastewater generation, pollutant generation, treatment time, or treatment load. Based on the third process-level allocation weight, the third total energy consumption is allocated to each process node; the third total energy consumption includes the total system energy consumption data of the environmental protection treatment system; When the same process node corresponds to multiple battery product batches, the third batch-level allocation weight is determined based on the processing time interval, process duration, product output or number of good products of each battery product batch, and the energy consumption of the environmental protection treatment system allocated to the process node is allocated to each battery product batch based on the third batch-level allocation weight.

7. The method according to claim 1, characterized in that, The step of allocating the energy consumption data of the second energy-consuming object to the corresponding process node and battery product batch according to the correspondence and preset allocation rules includes: Based on the aforementioned correspondence and the fourth allocation parameter, the fourth batch-level allocation weight of each battery product batch is determined; the fourth allocation parameter includes at least one of the effective occupancy time of the battery product batch, product output, or number of good products. According to the fourth batch-level allocation weight, the energy consumption data of the second energy consumption object is allocated to each battery product batch; Based on the aforementioned correspondence, the energy consumption data of the second energy consumption object allocated to each batch of battery products is collected to the process node corresponding to the second energy consumption object.

8. The method according to claim 1, characterized in that, The method further includes: The energy consumption data allocation results are subjected to multi-level total closure verification; the verification level includes at least one of the following: plant level, workshop level, production line level, and product batch level. The multi-level total closure check includes: For each level, the summation value of the energy consumption data of each lower level within the level is compared with the energy consumption data of the upper level of the level to obtain the closure deviation. When the closure deviation is less than or equal to a preset threshold, the total closure check of the current level is deemed to have passed. When the closure deviation is greater than the preset threshold, an anomaly marker is generated and anomaly identification is triggered. Based on the result of the anomaly identification, the abnormal data is corrected and / or marked. The anomaly identification includes identifying at least one of the following anomaly types: unit anomaly, missing data, duplicate measurement, batch time overlap, equipment shutdown without removal, production abnormality, yield abnormality, weight allocation abnormality, and allocation result abnormality.

9. The method according to claim 1, characterized in that, The method further includes: Output carbon emission accounting results and corresponding traceability records; the carbon emission accounting results shall include at least the carbon emission amount of each process node, the carbon emission amount of each batch of battery products, and the carbon emission amount of a unit battery product; The traceability record includes at least one of the following: energy consumption data source, preset allocation rules, emission factors, carbon emission calculation process, or total closure verification result.

10. The method according to claim 1, characterized in that, The process nodes include one or more of the following: batching, mixing, coating, drying, rolling, slitting, winding, stacking, assembly, liquid injection, formation, volume separation, aging, testing, and packaging; the auxiliary systems include at least one of the following: compressed air system, cooling water system, dehumidification system, or clean air conditioning system; the thermal system includes at least one of the following: steam system or natural gas system; and the environmental protection system includes at least one of the following: waste gas treatment system, wastewater treatment system, or solid waste temporary storage and treatment system.

Images