Quantitative analysis method and system for carbon emission reduction path of lithium battery and medium

Abstract

The invention discloses a lithium battery carbon emission reduction path quantitative analysis method and system and a medium, and the method comprises the steps: determining a function unit, a system boundary and an accepting and rejecting principle of a lithium battery product in a full life cycle, and the system boundary comprises a raw material obtaining stage, a production stage, a product transportation stage and an end-of-life stage; according to the determined function unit, the system boundary and the accepting and rejecting principle, the carbon emission of each stage and the total carbon emission of the full-life-cycle lithium battery product are calculated; the correlation between the lithium battery product in each stage and the total carbon emission is obtained through prediction of the carbon emission in each stage and the total carbon emission through a linear regression model; and selecting influence factors according to the determination of the correlation, and determining a carbon emission reduction path according to the selected influence factors.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery carbon emission accounting, and in particular to a method, system and medium for quantitatively analyzing carbon emission reduction paths of lithium batteries. Background Art

[0002] New energy vehicles and power batteries are new highlights of my country's exports, reflecting my country's low-carbon competitiveness. Lithium batteries account for 30% to 40% of the total carbon emissions of new energy vehicles and are the key to ensuring low-carbon development. Carbon emission requirements for industrial enterprises, especially new energy enterprises, have been put on the agenda, but the calculation and evaluation of carbon emissions is a series of cumbersome calculation processes. It is difficult for enterprises to evaluate themselves according to actual conditions, so they cannot conduct self-supervision and timely adjustments. A large amount of non-compliant carbon emissions will continue to affect the environment for some time to come.

[0003] The main process of the accounting method for lithium-ion battery carbon emissions is as follows: analyze the five major stages of raw material acquisition, production, product transportation, product use and product end of life, and calculate the carbon emissions of each stage using the carbon emission factor method, so that the carbon footprint accounting model of lithium battery products throughout the life cycle is more complete, the accounting boundaries are clearer, and the accounting results are more accurate. However, for the current status of the accounting method, on the one hand, only through the emission factor accounting, it is not conducive to objectively evaluating the effectiveness of carbon emission reduction and scientifically promoting carbon emission reduction work; on the other hand, only accounting data results, not guiding emission reduction work, can not meet the accounting needs. Summary of the invention

[0004] In order to overcome the above shortcomings, the purpose of the present invention is to provide a method, system and medium for quantitative analysis of carbon emission reduction paths of lithium batteries, which can calculate the carbon emissions of lithium battery products and guide the progress of subsequent emission reduction work.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: a quantitative analysis method of carbon emission reduction path of lithium batteries, comprising: Determine the functional units, system boundaries, and trade-off principles of lithium battery products throughout their life cycle, including the raw material acquisition stage, production stage, product transportation stage, and end-of-life stage; Calculate the carbon emissions at each stage and total carbon emissions of lithium battery products throughout their life cycle based on the determined functional units, system boundaries and trade-off principles; The carbon emissions at each stage and the total carbon emissions are predicted by a linear regression model to obtain the correlation between the carbon emissions at each stage and the total carbon emissions of the lithium battery product; Influencing factors are selected based on the determination of the correlation, and a carbon emission reduction path is determined based on the selected influencing factors.

[0006] The beneficial effects of the present invention are: through the linear regression method, the important influencing factors of emission reduction are quantitatively analyzed, the emission reduction path is pointed out, and while calculating the carbon emissions of lithium batteries, the progress of subsequent emission reduction work is guided.

[0007] Specifically, the calculation formula for carbon emissions at each stage is: C=

[0008] Among them, C is the carbon emissions at the stage of raw material acquisition, production, product transportation or end-of-life stage, AD i is the greenhouse gas activity data of the ith emission source; EF i is the greenhouse gas emission factor corresponding to the i-th emission source; Gwp i is the global warming potential value of the greenhouse gas emitted by the i-th emission source, and n is the total number of emission sources; The total carbon emissions are the sum of carbon emissions in each stage, and the calculation formula is: C 总碳排 =C 原材料 +C 生产 +C 运输 +C 生命末期 Among them, C 原材料 , C 生产 , C 运输 and C 生命末期 They are the carbon emissions at the raw material acquisition stage, production stage, product transportation stage or end-of-life stage.

[0009] At this point, the carbon emissions of lithium battery products can be calculated.

[0010] Specifically, the linear regression model is: C 总碳排放 =β0+β1*C 原材料 +β2*C 生产 +β3*C 运输 +β4*C 生命末期 +ɛ Among them, β0, β1, β2, β3 and β4 are the parameters in the linear regression model, ɛ is the error variable, and the data of β0, β1, β2, β3, β4 and ɛ are obtained by the fitting process, which indicates the effect of each factor on C 总碳排放 In the fitting process, the square residual coefficient is used to quantitatively evaluate the correlation.

[0011] The linear regression equation can be solved by the least square method, and the linear regression model can be used to analyze the important influencing factors of emission reduction.

[0012] Specifically, the closer the square residual coefficient is to 1, the greater the correlation is.

[0013] Furthermore, the linear regression model is trained by collected training data, and the training data includes carbon emission data of the lithium battery products of the same type.

[0014] Furthermore, the collection of the training data is controlled and evaluated in terms of data source, time representativeness, geographical representativeness and technical representativeness to ensure the accuracy and effectiveness of the training data.

[0015] Specifically, the functional unit is a 1kWh lithium battery product, and the selection principle is that secondary data is used for emission processes whose carbon emissions account for less than 1% or for which primary data cannot be obtained due to practical conditions. The secondary data excludes processes whose greenhouse gas emissions contribute less than 0.1%, but the total of all excluded processes cannot exceed 1%.

[0016] Further, selecting influencing factors based on the correlation, and determining the carbon emission reduction path based on the selected influencing factors specifically include: comparing the correlation to a threshold value; Select the influencing factors whose correlation is greater than a threshold as selected influencing factors; The emission reduction path table is searched according to the selected influencing factors to determine the emission reduction path.

[0017] The present invention also discloses a lithium battery carbon emission reduction path quantitative analysis system, which adopts the above-mentioned lithium battery carbon emission reduction path quantitative analysis method. The lithium battery carbon emission reduction path quantitative analysis system comprises: A storage module, wherein the storage module stores the functional units, system boundaries, and trade-off principles of lithium battery products throughout their life cycle; A carbon emission module, which is used to calculate the carbon emissions at each stage and the total carbon emissions of lithium battery products throughout their life cycle according to the determined functional units, system boundaries and trade-off principles; A linear regression module, wherein the linear regression module is used to predict the correlation between the carbon emissions of the lithium battery product at each stage and the total carbon emissions according to the carbon emissions at each stage and the total carbon emissions; A path analysis module is used to select influencing factors according to the determination of the correlation, and determine a carbon emission reduction path according to the selected influencing factors.

[0018] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the quantitative analysis method of the carbon emission reduction path of lithium batteries as described above are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1This is a flow chart of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0020] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0021] Embodiment 1 A quantitative analysis method for the carbon emission reduction path of lithium batteries is used to determine the carbon emission reduction path of lithium batteries, calculate the carbon emissions of lithium batteries, and guide the progress of subsequent emission reduction work.

[0022] See attached Figure 1 As shown, the quantitative analysis method of lithium battery carbon emission reduction path includes the following steps: S100. Determine the functional units, system boundaries and trade-off principles of lithium battery products throughout their life cycle.

[0023] The functional unit is a 1kWh lithium battery product.

[0024] The trade-off principle is that the emission process with a carbon emission ratio of less than 1% (producing insubstantial contribution) or inability to obtain primary data due to practical conditions uses secondary data. The data exclusion threshold is the process with a GHG (greenhouse gas) emission contribution of less than 0.1%, but the total of all excluded processes cannot exceed 1%. The system boundary includes four stages: raw material acquisition stage, production stage, product transportation stage and end-of-life stage.

[0025] S200. Calculate the carbon emissions at each stage and total carbon emissions of lithium battery products throughout their life cycle based on the determined functional units, system boundaries and trade-off principles.

[0026] Available data have been screened out based on functional units, system boundaries and trade-off principles, and input into the accounting model to calculate the carbon emissions corresponding to the four stages: raw material acquisition, production, product transportation and end-of-life.

[0027] The calculation formula included in the accounting model is: C=

[0028] Among them, C is the carbon emissions at the stage of raw material acquisition, production, product transportation or end-of-life stage, AD i is the greenhouse gas activity data of the ith emission source; EF i is the greenhouse gas emission factor corresponding to the i-th emission source; Gwp iis the global warming potential value corresponding to the greenhouse gas emitted by the i-th emission source, i is a positive integer, and n is the total number of emission sources.

[0029] The unit of C is kgCO 2e (kg CO2 equivalent),AD i The unit is determined by the specific emission source, EF i Primary data shall be used preferentially according to actual conditions, that is, data issued by suppliers or national departments. If such data are not available, they can be obtained through the emission factor database. The units shall be consistent with those of greenhouse gas activity data.

[0030] The total carbon emissions are the sum of carbon emissions in each stage, and the calculation formula is: C 总碳排 =C 原材料 +C 生产 +C 运输 +C 生命末期 Among them, C 原材料 , C 生产 , C 运输 and C 生命末期 They are the carbon emissions at the raw material acquisition stage, production stage, product transportation stage or end-of-life stage.

[0031] Through steps 100 to S200, the carbon emissions of lithium battery products can be calculated.

[0032] S300, carbon emissions at each stage and total carbon emissions are predicted through a linear regression model to obtain the correlation between lithium battery products at each stage and the total carbon emissions.

[0033] The linear regression model is: C 总碳排放 =β0+β1*C 原材料 +β2*C 生产 +β3*C 运输 +β4*C 生命末期 +ɛ Among them, β0, β1, β2, β3 and β4 are the parameters in the linear regression model, ɛ is the error variable, and the data of β0, β1, β2, β3, β4 and ɛ are obtained by the fitting process, which indicates the effect of each factor on C 总碳排放 The square residual coefficient R is used in the fitting process. 2 To quantitatively evaluate the correlation.

[0034] Linear regression can be solved by the least squares method. 2 ∈[0,1], the square residual coefficient R 2 The closer to 1, the greater the correlation.

[0035] S400: selecting influencing factors according to the correlation, and determining a carbon emission reduction path according to the selected influencing factors.

[0036] The influencing factors with high correlation are selected as the selected influencing factors, and the carbon emission reduction path is driven by the selected influencing factors to provide guidance for subsequent emission reduction work.

[0037] Selecting influencing factors based on the correlation, and determining the carbon emission reduction path based on the selected influencing factors specifically include: comparing the correlation to a threshold value; Select the influencing factors whose correlation is greater than a threshold as selected influencing factors; The emission reduction path table is searched according to the selected influencing factors to determine the emission reduction path.

[0038] In one embodiment, the correlation may not be compared with a threshold, that is, no threshold is set, and the influencing factor with the greatest correlation is selected as the selected influencing factor, and the emission reduction path table is searched according to the selected influencing factor to determine the emission reduction path.

[0039] In one embodiment, the correlation may not be compared with the threshold, that is, no threshold is set, the influencing factors are sorted from large to small, and several influencing factors with large correlation are selected as selected influencing factors. The emission reduction path table is searched according to the selected influencing factors to determine the emission reduction path.

[0040] In this embodiment, the linear regression method is used to quantitatively analyze the important influencing factors of emission reduction, indicate the emission reduction path, and guide the progress of subsequent emission reduction work while calculating the carbon emissions of lithium batteries.

[0041] For example, based on the carbon emission data of the same type of LFP (LiFePO4, lithium iron phosphate) batteries in different countries, a linear regression is performed to obtain the linear regression model formula as follows: C 总碳排放 =1.1168*C 原材料 +13.026, R 2 =0.979 C 总碳排放 =3.0675*C 生产 +43.097, R 2 =0.841 Among them C 原材料 Represents the impact of the raw material stage, C 生产 Represents the impact of the production and manufacturing stage. It can be concluded that the above two factors have a high impact on total carbon emissions, and the factors in the raw materials stage have a greater impact.

[0042] At this time, the emission reduction path table is as follows: Table 1

[0043] When only R 2 =0.979 is greater than the threshold, indicating that the selected influencing factor is the raw material factor. The emission reduction paths determined according to Table 1 include: ① reducing the use of raw materials for products, ② designing more efficient production processes, and ③ optimizing product design.

[0044] In one embodiment, the linear regression model is trained by collected training data, and the training data includes carbon emission data of the lithium battery products of the same type. The collection of the training data is controlled and evaluated from the perspective of data source, time representativeness, geographical representativeness, and technical representativeness. The lithium battery products of the same type include lithium battery products of the same model produced by different manufacturers, or lithium battery products of the same model produced in different countries, or lithium battery products of different types and different types.

[0045] Control and evaluation from the perspective of data source means that primary data is used first, that is, data directly obtained from statistics during the production process. All data used must have relevant data sources and data processing algorithms; data estimated or cited from literature must be explained in the calculation process. Control and evaluation from the perspective of time representativeness means that the reference data time is based on the principle of proximity, and the closer the time, the higher the data quality. Control and evaluation from the perspective of technical representativeness means that the data reflects the actual production technology situation, that is, it reflects the influence of factors such as actual process flow, technology and equipment type, raw material and energy consumption type, and production scale. Control and evaluation from the perspective of geographical representativeness means that the selection of relevant parameters such as emission factors takes into account the geographical location at the time of production.

[0046] In one embodiment, a linear regression method can be used to quantitatively analyze and compare similar products under different influencing factors, so as to accurately find the carbon emission gap between products and enhance the competitiveness of one's own brand.

[0047] Embodiment 2 The present invention also discloses a lithium battery carbon emission reduction path quantitative analysis system, which adopts the lithium battery carbon emission reduction path quantitative analysis method in implementation one.

[0048] See attached Figure 2 As shown, the lithium battery carbon emission reduction path quantitative analysis system includes a storage module, a carbon emission module, a linear regression module and a path analysis module.

[0049] The storage module stores the functional units, system boundaries and trade-off principles of lithium battery products throughout their life cycle. The carbon emissions module is used to calculate the carbon emissions and total carbon emissions of lithium battery products at each stage of their life cycle based on the determined functional units, system boundaries and trade-off principles. The linear regression module is used to predict the correlation between the carbon emissions of the lithium battery products at each stage and the total carbon emissions based on the carbon emissions at each stage and the total carbon emissions. The path analysis module is used to select influencing factors based on the determination of the correlation, and determine the carbon emission reduction path based on the selected influencing factors.

[0050] The functional unit mentioned is a 1kWh lithium battery product.

[0051] The trade-off principle is that the emission process with a carbon emission ratio of less than 1% (producing insubstantial contribution) or inability to obtain primary data due to practical conditions uses secondary data. The data exclusion threshold is the process with a GHG (greenhouse gas) emission contribution of less than 0.1%, but the total of all excluded processes cannot exceed 1%. The system boundary includes four stages: raw material acquisition stage, production stage, product transportation stage and end-of-life stage.

[0052] The carbon emission module stores a calculation model, which includes the following calculation formula: C=

[0053] Among them, C is the carbon emissions in the raw material acquisition stage, production stage, product transportation stage or end-of-life stage, ADi is the greenhouse gas activity data of the ith emission source; EFi is the greenhouse gas emission factor corresponding to the ith emission source; Gwpi is the global warming potential value corresponding to the greenhouse gas emitted by the ith emission source, i is a positive integer, and n is the total number of emission sources.

[0054] The linear regression module stores a trained linear regression model, which is: C 总碳排放 =β0+β1*C 原材料 +β2*C 生产 +β3*C 运输 +β4*C 生命末期 +ɛ Among them, β0, β1, β2, β3 and β4 are the parameters in the linear regression model, ɛ is the error variable, and the data of β0, β1, β2, β3, β4 and ɛ are obtained by the fitting process, which indicates the effect of each factor on C 总碳排放 The square residual coefficient R is used in the fitting process. 2 The closer the square residual coefficient R2 is to 1, the greater the correlation is.

[0055] The path analysis module stores an emission reduction path table. The path analysis module selects the influencing factors with correlation greater than a threshold as selected influencing factors, searches the emission reduction path table according to the selected influencing factors, and determines the emission reduction path.

[0056] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the corresponding method embodiment, and it has the corresponding technical effects.

[0057] It should be understood that the disclosed system can be implemented in other ways. For example, the division of the modules described above is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The above modules may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.

[0058] Embodiment 3 The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for quantitatively analyzing the carbon emission reduction path of a lithium battery in the first embodiment are implemented.

[0059] In this embodiment, if the quantitative analysis method of lithium battery carbon emission reduction path is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0060] The above implementation modes are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with this technology to understand the content of the present invention and implement it, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quantitative analysis of carbon emission reduction pathways of lithium batteries, characterized by: include: Determine the functional units, system boundaries, and trade-off principles of lithium battery products throughout their life cycle, including the raw material acquisition stage, production stage, product transportation stage, and end-of-life stage; Calculate the carbon emissions at each stage and total carbon emissions of lithium battery products throughout their life cycle based on the determined functional units, system boundaries and trade-off principles; The carbon emissions at each stage and the total carbon emissions are predicted by a linear regression model to obtain the correlation between the carbon emissions at each stage and the total carbon emissions of the lithium battery product; Influencing factors are selected based on the determination of the correlation, and a carbon emission reduction path is determined based on the selected influencing factors.

2. The method for quantitative analysis of carbon emission reduction pathways of lithium batteries according to claim 1, characterized in that: The calculation formula for carbon emissions at each stage is: C= Among them, C is the carbon emissions at the stage of raw material acquisition, production, product transportation or end-of-life stage, AD i is the greenhouse gas activity data of the ith emission source; EF i is the greenhouse gas emission factor corresponding to the i-th emission source; Gwp i is the global warming potential value of the greenhouse gas emitted by the i-th emission source, and n is the total number of emission sources; The total carbon emissions are the sum of carbon emissions in each stage, and the calculation formula is: C 总碳排 =C 原材料 +C 生产 +C 运输 +C 生命末期 Among them, C 原材料 , C 生产 , C 运输 and C 生命末期 They are the carbon emissions at the raw material acquisition stage, production stage, product transportation stage or end-of-life stage.

3. The method for quantitative analysis of carbon emission reduction pathways of lithium batteries according to claim 2, characterized in that: The linear regression model is: C 总碳排放 =β0+β1*C 原材料 +β2*C 生产 +β3*C 运输 +β4*C 生命末期 +ɛ Among them, β0, β1, β2, β3 and β4 are the parameters in the linear regression model, ɛ is the error variable, and the data of β0, β1, β2, β3, β4 and ɛ are obtained by the fitting process, which indicates the effect of each factor on C 总碳排放 In the fitting process, the square residual coefficient is used to quantitatively evaluate the correlation.

4. The method for quantitative analysis of carbon emission reduction pathways of lithium batteries according to claim 3, characterized in that: The closer the square residual coefficient is to 1, the greater the correlation is.

5. The method for quantitative analysis of carbon emission reduction pathways of lithium batteries according to claim 3, characterized in that: The linear regression model is trained by collected training data, and the training data includes carbon emission data of the lithium battery products of the same type.

6. The method for quantitative analysis of carbon emission reduction pathways of lithium batteries according to claim 5, characterized in that: The collection of the training data is controlled and evaluated in terms of data source, time representativeness, geographical representativeness and technical representativeness.

7. The method for quantitative analysis of carbon emission reduction pathways of lithium batteries according to claim 1, characterized in that: The functional unit is a 1kWh lithium battery product. The selection principle is that secondary data is used for emission processes whose carbon emissions account for less than 1% or for which primary data cannot be obtained due to practical conditions. The secondary data excludes processes whose greenhouse gas emissions contribute less than 0.1%, but the total of all excluded processes cannot exceed 1%.

8. The method for quantitative analysis of carbon emission reduction pathways of lithium batteries according to claim 1, characterized in that: Selecting influencing factors based on the correlation, and determining the carbon emission reduction path based on the selected influencing factors specifically include: comparing the correlation to a threshold value; Select the influencing factors whose correlation is greater than a threshold as selected influencing factors; The emission reduction path table is searched according to the selected influencing factors to determine the emission reduction path.

9. A lithium battery carbon emission reduction path quantitative analysis system, characterized by: The method for quantitatively analyzing the carbon emission reduction path of a lithium battery according to any one of claims 1 to 8 is adopted, and the system for quantitatively analyzing the carbon emission reduction path of a lithium battery comprises: A storage module, wherein the storage module stores the functional units, system boundaries, and trade-off principles of lithium battery products throughout their life cycle; A carbon emission module, which is used to calculate the carbon emissions at each stage and the total carbon emissions of lithium battery products throughout their life cycle according to the determined functional units, system boundaries and trade-off principles; A linear regression module, wherein the linear regression module is used to predict the correlation between the total carbon emissions of the lithium battery product at each stage and the total carbon emissions according to the carbon emissions at each stage; A path analysis module is used to select influencing factors according to the determination of the correlation, and determine a carbon emission reduction path according to the selected influencing factors.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for quantitatively analyzing the carbon emission reduction path of a lithium battery as described in any one of claims 1 to 8 are implemented.

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