Method and system for determining optimal decommissioning time of battery, electronic equipment and storage medium

Abstract

The invention provides a battery optimal decommissioning time determination method and system, electronic equipment and a storage medium. The method for determining the optimal decommissioning time of the battery comprises the following steps: establishing a battery full-life-cycle carbon emission evaluation model fused with dynamic round-trip efficiency degradation characteristics; constructing a dynamic round-trip efficiency degradation model based on three degradation scenes of high speed, medium speed and low speed, and establishing a quantitative association relationship between round-trip efficiency degradation and carbon emission, thereby determining single-cycle energy output and corresponding single-cycle carbon emission of each cycle of the battery; determining the total carbon emission amount of the system according to the total energy output amount of the single battery in the full life cycle; and constructing a target function, and determining an optimal decommissioning point of the minimized total carbon emission of the whole life cycle based on the target function. According to the method for determining the optimal decommissioning time of the battery, the carbon emission of the battery in the full life cycle can be accurately evaluated, the optimal decommissioning time is dynamically determined, differentiated carbon emission reduction decisions are realized, and the utilization efficiency of battery resources and the green management level are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of energy storage carbon emission, and particularly relates to a battery optimal retirement time determination method and system, an electronic device and a storage medium. BACKGROUND

[0002] With the rapid development of the new energy industry, lithium ion batteries, as the core power source of electric vehicles and other transportation tools, have become a key link affecting the sustainable development of the industry in terms of full life cycle carbon emission management.

[0003] However, the traditional carbon emission evaluation method has significant defects in carbon emission evaluation and retirement decision-making. First, in terms of carbon emission quantification, the traditional method adheres to the static round-trip efficiency (RTE) model and fails to reflect the true characteristics of the battery operation process, resulting in serious distortion of the carbon emission evaluation results. Secondly, in terms of retirement decision-making, the traditional technology adopts a rigid decision-making mechanism with a fixed state of health (SOH) threshold, which is difficult to adapt to the differentiated characteristics of the battery degradation rate in different operating environments. The combination of this static evaluation model and rigid decision-making mechanism not only exacerbates the error of carbon emission calculation, but also leads to a decrease in battery resource utilization and a failure to fully utilize potential carbon emission reduction space.

[0004] Therefore, how to solve the defects of insufficient carbon emission evaluation accuracy and poor adaptability of the retirement strategy in the traditional technology is a current problem to be solved.

[0005] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the application, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] Based on this, the embodiments of the application provide a battery optimal retirement time determination method and system, an electronic device and a storage medium, which can accurately evaluate the full life cycle carbon emission of the battery and dynamically determine the optimal retirement time, realize differentiated carbon emission reduction decision-making, and improve the battery resource utilization efficiency and green management level.

[0007] According to some embodiments of the application, a battery optimal retirement time determination method is provided, which comprises:

[0008] establishing a battery full life cycle carbon emission evaluation model that integrates the degradation characteristics of dynamic round-trip efficiency;

[0009] construct a dynamic round-trip efficiency degradation model, which is based on at least three degradation scenarios of fast, medium and slow, and couples the round-trip efficiency degradation rate with the battery state of health degradation path to establish a quantitative correlation between the round-trip efficiency degradation and carbon emissions;

[0010] Using the dynamic round-trip efficiency degradation model, the single-cycle energy output of the battery per cycle and the corresponding single-cycle carbon emissions are determined;

[0011] According to the total energy output of the single battery in the whole life cycle, the total carbon emissions of the system are determined;

[0012] A target function is constructed to minimize the total carbon emissions in the whole life cycle, and the optimal retirement point of minimizing the total carbon emissions in the whole life cycle is determined based on the target function.

[0013] In some embodiments, the battery life cycle carbon emission evaluation model includes: a raw material extraction stage carbon emission calculation model, a manufacturing stage carbon emission calculation model, a transportation stage carbon emission calculation model, a use stage carbon emission calculation model and a recycling stage carbon emission calculation model, wherein,

[0014] The raw material extraction stage carbon emission calculation model determines the carbon emissions in the raw material extraction stage according to the mass of the raw materials required for battery manufacturing and the corresponding carbon emission factor of the raw materials;

[0015] The manufacturing stage carbon emission calculation model determines the carbon emissions in the manufacturing stage according to the energy consumption in the battery manufacturing process and the corresponding carbon emission factor of the energy consumption;

[0016] The transportation stage carbon emission calculation model determines the carbon emissions in the transportation stage according to the transportation distance, transportation weight and corresponding carbon emission factor of the transportation process;

[0017] The use stage determines the energy consumption of each cycle according to the dynamic round-trip efficiency degradation characteristics of the battery in the use process, and determines the carbon emissions in the use stage by combining the corresponding carbon emission factor of the use process;

[0018] The recycling stage determines the net carbon emissions in the recycling process according to the emission reduction benefits of the battery in the recycling process.

[0019] In some embodiments, the dynamic round-trip efficiency degradation model is constructed by:

[0020] The round-trip efficiency is defined as the product of the charging efficiency and the discharging efficiency, which is affected by the number of cycles and environmental factors;

[0021] The degradation functions of the round-trip efficiency with the cycle number are respectively established for the fast degradation scenario, the medium degradation scenario and the slow degradation scenario, and the degradation functions reflect the dynamic change law of the round-trip efficiency under different degradation scenarios.

[0022] A coupling model of the degradation of the round-trip efficiency and the state of health of the battery is constructed to quantify the evolution characteristics of the carbon emissions under different degradation scenarios.

[0023] In some embodiments, the single-cycle energy output of the battery and the corresponding single-cycle carbon emission amount of each cycle are determined by using the dynamic round-trip efficiency degradation model, which includes:

[0024] The output energy of each cycle is determined according to the energy output model of each cycle, and the carbon emission amount of each cycle is obtained based on the output energy of the current cycle and the carbon emission factor of the power grid.

[0025] In some embodiments, the energy output model of each cycle is constructed according to the rated capacity of the battery, the state of health discharge depth of the battery at the current cycle and the round-trip efficiency of the current cycle.

[0026] In some embodiments, the total system carbon emission amount is determined according to the total energy output of the single battery throughout the life cycle, which includes:

[0027] An initial carbon emission amount is obtained;

[0028] The total energy output of the single battery throughout the life cycle is obtained, and the number of required batteries is determined based on the total energy output of the single battery throughout the life cycle and the specified total energy demand.

[0029] The total system carbon emission amount is determined according to the initial carbon emission amount, the carbon emission amount of the battery used completely and the carbon emission amount of the battery used partially.

[0030] In some embodiments, a target function is constructed with the goal of minimizing the total life cycle carbon emission amount, and the optimal retirement point of minimizing the total life cycle carbon emission amount is determined based on the target function, which includes:

[0031] A target function is established with the goal of minimizing the total life cycle carbon emission amount;

[0032] The optimal retirement point is determined by the cycle counting module, the carbon emission evaluation module and the retirement optimization module, wherein the cycle counting module is configured to determine the minimum cycle number that meets the energy demand based on the dynamic round-trip efficiency characteristics, the carbon emission evaluation module is used for life cycle analysis, and the retirement optimization module is configured to determine the optimal retirement point by the target function.

[0033] According to some embodiments, the application further provides a battery optimal retirement time determination system for implementing the battery optimal retirement time determination method in the foregoing embodiments, and the determination system comprises:

[0034] The evaluation model construction module is configured to establish a battery full-life cycle carbon emission evaluation model that integrates the dynamic round-trip efficiency degradation characteristics.

[0035] The degradation model construction module is configured to construct a dynamic round-trip efficiency degradation model that couples the round-trip efficiency degradation rate with the battery health state degradation path based on at least three degradation scenarios of fast, medium and slow, and establishes a quantitative correlation between the round-trip efficiency degradation and the carbon emission.

[0036] The single-cycle carbon emission amount acquisition module is configured to determine the single-cycle energy output and the corresponding single-cycle carbon emission amount of each cycle of the battery by using the dynamic round-trip efficiency degradation model.

[0037] The system carbon emission total amount acquisition module is configured to determine the system carbon emission total amount according to the total amount of the full-life cycle energy output of the single battery.

[0038] The optimal retirement point acquisition module is configured to construct a target function with the minimization of the full-life cycle carbon emission total amount as the target, and determine the optimal retirement point that minimizes the full-life cycle carbon emission total amount based on the target function.

[0039] According to some embodiments, the application further provides an electronic device comprising:

[0040] A processor;

[0041] A memory having executable instructions of the processor stored therein;

[0042] The processor is configured to execute the steps of the battery optimal retirement time determination method in the foregoing embodiments by executing the executable instructions.

[0043] According to some embodiments, the application further provides a computer readable storage medium for storing a program, and the program is executed by a processor to implement the steps of the battery optimal retirement time determination method in the foregoing embodiments.

[0044] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the application.

[0045] The embodiments of the application can / at least have the following advantages:

[0046] The application truly reflects the influence of the continuous degradation of battery performance on carbon emission with the increase of cycle number in the actual use process of the battery by establishing a full life cycle carbon emission evaluation model integrating dynamic round-trip efficiency degradation characteristics, overcomes the systematic deviation problem of carbon emission evaluation caused by the traditional static RTE assumption, and thus significantly improves the accuracy of carbon emission evaluation.

[0047] By constructing three typical degradation scenarios of fast, medium and slow, and coupling the round-trip efficiency degradation rate with the battery health state degradation path, the quantitative correlation between the RTE degradation process and carbon emission is established, so that the carbon emission evaluation result is closer to the actual operation condition of the battery, and the systematic deviation problem of the traditional static evaluation model in carbon emission calculation is effectively solved.

[0048] The application also obtains the single-cycle energy output and the corresponding carbon emission of each cycle, constructs an optimization objective function on the basis of considering the full life cycle energy output, and determines the optimal retirement point of minimizing the life cycle carbon emission, breaks through the defect that the fixed retirement threshold in the traditional technology cannot be applied to different degradation scenarios, realizes the differentiated retirement decision for different degradation scenarios, realizes the minimization of full life cycle carbon emission while ensuring energy output, significantly improves the battery resource utilization efficiency and carbon emission reduction effect, and provides an innovative solution with industry universality for green and low-carbon management of the full life cycle of the battery.

[0049] Other advantages, objects, and features of the application will be set forth in part in the following specification taken in conjunction with the accompanying drawings, and in part will become apparent to those skilled in the art from a consideration of the following specification and drawings, or can be learned from the practice of the application. The advantages and other objects of the application can be realized and attained by means of the instrumentalities and combinations pointed out in the following specification. BRIEF DESCRIPTION OF DRAWINGS

[0050] Other features, objects, and advantages of the application will become more apparent from a review of the following detailed description, with reference to the non-limiting drawings.

[0051] Figure 1 The flowchart of the battery optimal retirement time determination method in some embodiments of the application is shown;

[0052] Figure 2 The battery full life cycle carbon emission evaluation model framework in some embodiments of the application is shown;

[0053] Figure 3 The flowchart of the dynamic round-trip efficiency degradation model construction in some embodiments of the application is shown;

[0054] Figure 4A schematic diagram of a process for determining the total carbon emission of a system according to the total amount of energy output of a single battery throughout its life cycle in some embodiments of the present application;

[0055] Figure 5 A schematic diagram of a carbon optimization retirement strategy framework in some embodiments of the present application is shown.

[0056] Figure 6 A graph of an RTE degradation curve in a static RTE scenario and fast, medium, and slow RTE degradation scenarios in some embodiments of the present application;

[0057] Figure 7 A graph of an SOH degradation curve of an LFP battery under different C-rates in some embodiments of the present application;

[0058] Figure 8 A statistical graph of carbon emission evaluation of a 1kg LFP battery under different RTE scenarios in some embodiments of the present application;

[0059] Figure 9 A statistical graph of life cycle carbon emission of a 1kg LFP battery in different battery SOH ranges in some embodiments of the present application;

[0060] Figure 10 A heat map of optimal retirement point and carbon emission improvement rate under different RTE and capacity attenuation scenarios in some embodiments of the present application;

[0061] Figure 11 A box plot of optimal retirement point distribution under different RTE attenuation scenarios in some embodiments of the present application;

[0062] Figure 12 A box plot of carbon emission improvement rate distribution under different RTE attenuation scenarios in some embodiments of the present application;

[0063] Figure 13 A schematic diagram of the structure of a battery optimal retirement time determination system in some embodiments of the present application;

[0064] Figure 14 A schematic diagram of the structure of an electronic device in some embodiments of the present application;

[0065] Figure 15 A schematic diagram of the structure of a computer readable storage medium in some embodiments of the present application. DETAILED DESCRIPTION

[0066] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The embodiments of the present application are shown in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for describing specific embodiments only and is not intended to be limiting of the application.

[0068] As used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. It should also be understood that the term "comprising" or "having" etc., specifies the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but does not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof.

[0069] In addition, the accompanying drawings are only schematic and are not necessarily drawn to scale. Identical reference signs designate identical or similar parts throughout the drawings. Some of the blocks in the drawings are functional entities that do not necessarily have to be physically or logically independent entities. These functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0070] The traditional carbon emission evaluation method has significant defects in carbon emission evaluation and retirement decision. First, at the carbon emission quantification level, the traditional method adheres to the static round-trip efficiency (RTE) model, which fails to reflect the true characteristics in the battery operation process, resulting in serious distortion of the carbon emission evaluation results. Second, at the retirement decision level, the traditional technology adopts a rigid decision mechanism with a fixed state of health (SOH) threshold, which is difficult to adapt to the differentiated characteristics of battery degradation rates in different operating environments. The combination of this static evaluation model and rigid decision mechanism not only exacerbates the error of carbon emission calculation, but also leads to a decrease in battery resource utilization and a failure to fully utilize potential carbon emission reduction space.

[0071] Based on this, the present application hopes to provide a solution that can accurately evaluate the whole life cycle carbon emission of the battery and dynamically determine the optimal retirement time, realize differentiated carbon emission reduction decision, and improve the efficiency of battery resource utilization and green management level. The detailed content will be described in the subsequent embodiments.

[0072] According to some embodiments, the present application provides a battery optimal retirement time determination method.

[0073] It should be understood that the battery involved in the present application is not limited to a specific type of electrochemical energy storage device, and can be a lithium ion battery, a sodium ion battery, a solid-state battery, a nickel-hydrogen battery, etc. For the convenience of description, the lithium ion battery is taken as an example in the following embodiments, but this does not constitute a limitation on the protection scope of the present application.

[0074] Referring to Figure 1 , the optimal retirement time determination method of the battery can specifically include the following steps S100-S500:

[0075] S100: Establish a battery full life cycle carbon emission evaluation model integrating dynamic round-trip efficiency degradation characteristics.

[0076] By establishing a full life cycle carbon emission evaluation model integrating dynamic round-trip efficiency degradation characteristics, the influence of the continuous degradation of battery performance with the increase of cycle number on carbon emission in the actual use process of the battery is truly reflected, the systematic deviation problem of carbon emission evaluation caused by the traditional static RTE assumption is overcome, and the accuracy of carbon emission evaluation is significantly improved.

[0077] S200: Construct a dynamic round-trip efficiency degradation model, which at least based on three degradation scenarios of fast, medium and slow, couples the round-trip efficiency degradation rate with the state of health (SOH) degradation path, and establishes a quantitative correlation between the round-trip efficiency degradation and carbon emission.

[0078] By constructing three typical degradation scenarios of fast, medium and slow, and coupling the round-trip efficiency degradation rate with the state of health degradation path, a quantitative correlation between the RTE degradation process and carbon emission is established, which makes the carbon emission evaluation result more close to the actual operation condition of the battery, and effectively solves the systematic deviation problem of the traditional static evaluation model in carbon emission calculation.

[0079] S300: Determine the single-cycle energy output of the battery and the corresponding single-cycle carbon emission amount of each cycle by using the dynamic round-trip efficiency degradation model.

[0080] Here, the single-cycle carbon emission amount takes into account the efficiency loss caused by RTE degradation.

[0081] S400: Determine the total system carbon emission amount according to the total amount of single-cycle energy output of the battery.

[0082] S500: Construct a target function with the minimization of the total amount of full life cycle carbon emission as the goal, and determine the optimal retirement point of the minimization of the total amount of full life cycle carbon emission based on the target function.

[0083] By determining the single-cycle energy output and the corresponding carbon emissions of each cycle, an optimization objective function is constructed based on considering the whole life cycle energy output, and the optimal retirement point of minimizing the life cycle carbon emissions is determined, which breaks through the defect that the fixed retirement threshold in the traditional technology cannot be applied to different degradation scenarios.

[0084] Therefore, the application can provide differentiated retirement decisions for different degradation scenarios, minimize the whole life cycle carbon emissions while ensuring energy output, significantly improve battery resource utilization efficiency and carbon emission reduction effect, and provide an innovative solution with industry universality for green and low-carbon management of the whole life cycle of batteries.

[0085] The above is the core idea of the application, and the technical solutions in the embodiments of the application will be described clearly and completely below. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the application.

[0086] In step S100, a battery whole life cycle carbon emission evaluation model integrating dynamic RTE degradation characteristics is established. In the process of modeling carbon emissions of different life cycle stages of the battery, the dynamic change characteristics of RTE degradation with cycle number under actual operating conditions are introduced into the carbon emission calculation of each stage, so that the model can more accurately reflect the real carbon emission performance of the battery in the whole life cycle, and improve the accuracy of carbon emission evaluation.

[0087] Please refer to Figure 2 It is understood that in step S100, the battery whole life cycle carbon emission evaluation model involved can cover the raw material extraction, manufacturing, transportation, use and recycling stages, and quantify the carbon emissions of each stage.

[0088] In some embodiments, the battery whole life cycle carbon emission evaluation model can specifically include a raw material extraction stage carbon emission calculation model, a manufacturing stage carbon emission calculation model, a transportation stage carbon emission calculation model, a use stage carbon emission calculation model and a recycling stage carbon emission calculation model.

[0089] The raw material extraction stage carbon emission calculation model determines the carbon emission of the raw material extraction stage according to the mass of the raw materials (such as positive electrode materials, negative electrode materials and electrolytes, etc.) required for battery manufacturing and the carbon emission factor corresponding to the raw materials.

[0090] As an example, the carbon emission of the raw material extraction stage CE materials The following formula can be used for calculation:

[0091] CE materials =∑(M k ×EF k)

[0092] wherein M k represents the mass of the kth raw material, EF k represents its corresponding carbon emission factor. The carbon emission of the raw material extraction stage is obtained by multiplying the mass of each raw material by its corresponding carbon emission factor and then accumulating the results.

[0093] The manufacturing stage carbon emission calculation model determines the carbon emission of the manufacturing stage according to the energy consumption in the battery manufacturing process and the corresponding carbon emission factors of the energy consumption. The manufacturing process mainly involves electrode preparation and battery assembly processes, and its carbon emission mainly comes from power consumption and natural gas usage.

[0094] As an example, the carbon emission of the manufacturing stage CE manufacturing may be calculated using the following formula:

[0095] CE manufacturing = E grid × EF grid + E ng × EF ng

[0096] wherein E grid and E ng represent the grid and natural gas energy consumption, respectively, and EF grid and EF ng represent the corresponding carbon emission factors of the grid and natural gas, respectively. By considering the carbon emissions of grid electricity and natural gas separately in the calculation, the carbon footprint of the manufacturing process is accurately reflected.

[0097] The transportation stage carbon emission calculation model determines the carbon emission of the transportation stage according to the transportation distance, transportation weight, and the corresponding carbon emission factors of the transportation process.

[0098] As an example, the carbon emission of the transportation stage CE trans may be calculated using the following formula:

[0099] CE trans = D × W × EF sea

[0100] wherein D represents the transportation distance, W represents the weight of the goods, and EF sea represents the carbon emission factor under the transportation mode (e.g., sea transportation). By multiplying the transportation distance, the weight of the goods, and the corresponding carbon emission factor of the transportation mode, the carbon emission generated by the transportation process of the battery from the production site to the use site is quantified.

[0101] Based on the dynamic round-trip efficiency degradation characteristics of the battery during use, the energy consumption of each cycle is accumulated, and combined with the carbon emission factor corresponding to the use process, the carbon emission amount of the use phase is determined.

[0102] As an example, the carbon emissions during the usage phase (CE) use The following formula can be used for calculation:

[0103]

[0104] CE use =E use ×EF grid

[0105] Where DoD represents the depth of discharge, C nom This indicates the rated capacity of the battery, SOH(N). i ) indicates the Nth battery i Battery health status during the next cycle Indicates the Nth battery i The round-trip efficiency of the next cycle, η Coul This indicates the charge / discharge coulomb efficiency.

[0106] The calculations during the usage phase take into account the impact of RTE degradation and changes in battery health on energy efficiency and carbon emissions during use. This is achieved by summing up the energy consumption of each cycle and combining it with the corresponding carbon emission factor EF of the power grid. grid This yields the carbon emissions during the usage phase.

[0107] The recycling phase determines the net carbon emissions of the recycling process based on the emission reduction benefits of the battery in the recycling process (such as battery dismantling and material regeneration).

[0108] As an example, the carbon emissions during the recycling phase (CE) recycling The following formula can be used for calculation:

[0109] CE recycling =CE disassembly +CE reprocessing –CE offset

[0110] Among them, CE disassembly Indicates carbon emissions during the battery dismantling process, CE reprocessing This represents the carbon emissions during the material recycling process. By balancing the above factors, the net carbon emissions of the recycling process are obtained.

[0111] For example, the total carbon emissions (CE) over the entire battery lifecycle total The carbon emissions from the five stages described above can be obtained by summing them using the formula shown below:

[0112] CE total = CE materials + CE manufacturing + CE trans + CE use + CE recycling

[0113] By accumulating the carbon emissions of the above five stages, the total carbon emissions of the battery from raw material acquisition to final recycling process can be obtained. Thus, the complete carbon footprint of the battery in its entire life cycle can be comprehensively reflected, thereby improving the accuracy of carbon emission evaluation.

[0114] For step S200, please refer to Figure 3 In some embodiments, the process of constructing the dynamic round-trip efficiency degradation model can specifically include steps S210-S230 as follows:

[0115] In step S210, the round-trip efficiency η RTE is defined as the product of the charging efficiency (η charge ) and the discharging efficiency (η discharge ), which is affected by the cycle number and environmental factors, and its expression is as follows:

[0116] η RTE = η charge × η discharge

[0117] Round-Trip Efficiency (RTE) is a key parameter to measure the energy cycle efficiency of lithium-ion batteries. It should be understood that RTE is dynamic, and the degradation of RTE is affected by multiple factors, mainly including cycle number N i , environmental factors θ and degradation mode.

[0118] First, the cycle number N i is a key factor affecting the degradation of RTE. For example, the RTE of lithium iron phosphate (LiFePO4, LFP for short) battery decreases by about 3%-20% after every 1000 cycles, and the specific degradation rate varies with battery quality and usage conditions.

[0119] Second, environmental factors θ such as temperature, charging and discharging rate (C-rate), battery state of charge (SOC) and depth of discharge (DOD) also significantly affect the degradation of RTE.

[0120] In step S220, a degradation function of round-trip efficiency with respect to cycle number is established for fast degradation scenario, medium degradation scenario and slow degradation scenario respectively, and the degradation function reflects the dynamic change rule of round-trip efficiency under different degradation scenarios.

[0121] In step S230, a coupling model of round-trip efficiency degradation and battery state of health is constructed, and the evolution characteristics of carbon emission under different degradation scenarios are quantified.

[0122] For step S300, in some embodiments, the output energy of each cycle can be determined according to an energy output model of each cycle, and the carbon emission of each cycle can be obtained based on the output energy of the current cycle and the grid carbon emission factor.

[0123] As an example, the energy output model of each cycle can be constructed according to the rated capacity C of the battery nom , the state of health SOH(N i ) of the battery at the current cycle, the depth of discharge DOD and the round-trip efficiency η of the current cycle.

[0124] Exemplarily, the energy output Q i (N cycle ) of the N i th cycle can be obtained by the following formula:

[0125] Q cycle (N i )=C nom ·SOH(N i )·DOD·η RTE (N i )

[0126] Based on this, the corresponding carbon emission calculation formula is:

[0127]

[0128] According to the above formula, it can be seen that the lower η RTE , the more power needs to be consumed to maintain the same energy output, that is, higher carbon emission is generated.

[0129] For step S400, please refer to Figure 4 , in some embodiments, the process of determining the total system carbon emission according to the total energy output of the whole life cycle of a single battery can specifically include the following steps:

[0130] In step S410, the initial carbon emission is obtained.

[0131] As an example, the initial carbon emission amount can be obtained by calculating the net carbon emission of the battery production, transportation and recycling stages, and taken as a benchmark for evaluating the impact of the use stage. At this time, the initial carbon emission amount can also be referred to as a benchmark carbon emission amount.

[0132] As an example, the benchmark carbon emission amount CE init The calculation can be performed using the following formula:

[0133] CE init = CE materials + CE trans - CE recycling

[0134] In step S420, the single battery full life cycle output energy is obtained, and the required battery number is determined based on the single battery full life cycle output energy and the specified total energy demand.

[0135] As an example, the single battery full life cycle output energy can include the emission amount CE init of the initial manufacturing and recycling stages, and the dynamic carbon emission cumulative value of the entire use stage, which can be specifically represented as:

[0136]

[0137] wherein, represents the energy output cumulative value of the entire use stage, N retire is the optimal retirement point determined according to the carbon emission minimization objective.

[0138] Based on this, under the premise of meeting the specified total energy demand Q total , the required battery number Batt total can be calculated using the following formula:

[0139]

[0140] wherein, represents the single battery full life cycle output energy.

[0141] In step S430, the system carbon emission total amount is determined according to the initial carbon emission amount, the carbon emission amount of the fully used battery, and the carbon emission amount of the partially used battery.

[0142] It should be noted that in determining the system carbon emission total amount, the partially used battery can refer to, for example, the last block partially used battery.

[0143] Specifically, the fully used battery refers to a battery that completes its full life cycle energy output before reaching the optimal retirement point, and the partially used battery refers to a battery that is only partially used because the energy demand is insufficient to support complete retirement when meeting the specified total energy demand.

[0144] The above distinction ensures that the calculation of the total system carbon emissions can comprehensively reflect the entire carbon footprint of the battery from production to the end of use, in which the carbon emissions of the end-of-life battery are considered separately to improve the accuracy of carbon emission assessment.

[0145] As mentioned above, the total system carbon emissions CE total The total system carbon emissions CE total is composed of two parts, namely the carbon emissions of the fully used battery and the carbon emissions of the partially used battery, and its calculation formula can be expressed as:

[0146] CE total = (Batt cell -1)·CE last

[0147] Among them, the carbon emissions CE last of the partially used battery (such as the end-of-life battery) can be calculated using the following formula:

[0148]

[0149] At the same time, in order to make up for the total output demand, the remaining power Q last provided by the end-of-life battery can be calculated using the following formula:

[0150]

[0151] For step S500, in some embodiments, a target function aimed at minimizing the total life cycle carbon emissions can be established first, and then the optimal retirement point is determined through the cooperation of the cycle counting module, the carbon emission assessment module and the retirement optimization module.

[0152] The target function can be constructed as:

[0153]

[0154] Among them, can be calculated using the following formula:

[0155]

[0156] The cycle counting module, the carbon emission assessment module and the retirement optimization module can jointly constitute a carbon optimization retirement strategy framework, for example Figure 5 as shown. Among them, the cycle counting module is configured to determine the minimum cycle number that meets the energy demand based on the dynamic round-trip efficiency characteristics, the carbon emission assessment module is used for life cycle analysis, and the retirement optimization module is configured to determine the optimal retirement point through the target function.

[0157] It should be noted that the optimal retirement point involved in the present application refers to a battery state of health threshold (also referred to as SOH threshold) dynamically calculated based on the minimum whole life cycle carbon emission target, by constructing an objective function, comprehensively considering the dynamic degradation characteristics of battery round trip efficiency (RTE) and the decay path of battery state of health (SOH).

[0158] The objective function constructed in the above embodiment comprehensively considers the carbon emission factors in multiple stages of the whole life cycle of the battery, specifically including the initial carbon emission amount (including production, transportation and recovery) and the cumulative carbon emission amount based on the RTE degradation characteristics in the use stage, and can couple the dynamic degradation process of the battery state of health (SOH) and the round trip efficiency (RTE) to construct a more accurate carbon footprint evaluation framework.

[0159] Therefore, the above embodiment can accurately quantify the carbon emission trend under different degradation paths, thereby providing differentiated retirement decisions for different degradation scenarios, minimizing the whole life cycle carbon emission while ensuring energy output, and significantly improving the battery resource utilization efficiency and carbon emission reduction effect.

[0160] As an example, the carbon emission evaluation module can perform whole life cycle analysis by using a life cycle analysis software. The life cycle analysis software can be SimaPro software, but is not limited thereto, and can also be other tool platforms with life cycle evaluation function and supporting multi-stage carbon factor modeling.

[0161] The present application provides a battery optimal retirement time determination method, which quantitatively analyzes the carbon emission of the battery from raw material extraction, manufacturing, transportation, use to recovery by establishing a battery whole life cycle carbon emission evaluation model integrating dynamic round trip efficiency (RTE) degradation characteristics. Based on three typical RTE degradation scenarios of fast, medium and slow, combined with multiple battery state of health (SOH) degradation paths, a dynamic RTE degradation model is constructed, which first reveals the quantitative correlation mechanism between RTE degradation and carbon emission, thereby realizing accurate evaluation of carbon emission under different degradation modes. Using the model, the single cycle energy output of the battery each cycle and the corresponding carbon emission amount are determined, and the total system carbon emission amount is calculated according to the total energy output of the whole life cycle of the single battery. Further, an objective function is constructed with the target of minimizing the total whole life cycle carbon emission amount, and the optimal retirement point is dynamically determined through the collaborative work of the cycle counting module, the carbon emission evaluation module and the retirement optimization module, forming a scientific framework of "evaluation-decision" integration.

[0162] The battery optimal retirement time determination method provided in the application can provide differentiated retirement strategies for different degradation scenarios, effectively balance energy output demand and carbon emission control, significantly improve the accuracy of carbon emission evaluation and the pertinence of retirement decision, and provide a solution with theoretical innovation and practical guidance for low-carbon management of electric vehicle batteries.

[0163] To further illustrate the effectiveness of the embodiments of the application, the full life cycle evaluation and retirement strategy research of a certain LFP battery for a two-wheeled vehicle are taken as an example.

[0164] The rated parameters of the battery are shown in Table 1:

[0165] Table 1 LFP battery regular parameters

[0166] Parameter Value Unit Shape type Cylindrical -- Model 32140 -- Mass 298 g Nominal voltage 3.2 V Nominal capacity 15 Ah Specific energy 0.16 kWh / kg Cycle life 2500 Number of cycles Positive electrode material LFP -- Negative electrode material Graphite --

[0167] It should be noted that, since the round-trip efficiency (RTE) degradation process in actual application is affected by many environmental factors, it is difficult to construct a unified degradation function model, and the following simplifying assumptions are made in the embodiments:

[0168] First, assuming constant environmental conditions, only the effect of cycle number on RTE is considered, i.e. η RTE (N i ).

[0169] Second, three typical RTE degradation scenarios are set, which are:

[0170] (1) Fast degradation scenario (RTE1): RTE sharply decreases with cycle number, representing harsh usage conditions such as high-rate charging and discharging.

[0171] (2) Moderate degradation scenario (RTE2): RTE linearly and slowly decreases with cycle number, representing standard daily use conditions.

[0172] (3) Slow degradation scenario (RTE3): RTE decreases with a small amplitude, representing the running situation in a mild environment.

[0173] In addition to the above three typical RTE degradation scenarios, the Static RTE reference scenario (assuming RTE is constant at 90%) is further introduced as a comparison group.

[0174] Further, different SOH degradation curves are matched for the above degradation scenarios, and multiple groups of typical working condition combinations are constructed. The RTE degradation curve and SOH degradation path are shown in Figure 6 and Figure 7 .

[0175] In this embodiment, the multi-dimensional scenario design has at least the following objectives: First, it covers typical efficiency degradation modes that may occur in practical applications, enhancing the representativeness of the carbon assessment model to real scenarios; second, it verifies the good applicability and stability of the carbon optimization decommissioning strategy proposed in this application under different capacity degradation trends through cross-validation of multiple degradation scenarios.

[0176] The following is based on Figure 8 and Figure 9 The data shown analyzed the carbon emission performance of the LFP battery for two-wheeled vehicles under different SOH degradation curves and RTE degradation scenarios, in order to verify the effect of the above-mentioned method for determining the optimal retirement time of the battery on improving the accuracy of carbon emission estimation.

[0177] Figure 8 The results show the lifecycle carbon emissions of LFP batteries under different combinations of SOH degradation curves (SOH1 to SOH6) and different RTE degradation scenarios (Static, RTE1, RTE2 and RTE3).

[0178] like Figure 8 As shown in the upper part (SOH1~SOH3), the static RTE consistently underestimates actual carbon emissions. Taking the rapid degradation scenario of RTE1 as an example:

[0179] In the SOH1 scenario, the statically assumed emissions are 441.5 kg CO2 eq, while the corresponding emissions in RTE1 are 583.2 kg CO2 eq, a deviation of 32.1%.

[0180] In the SOH2 scenario, the statically assumed emissions are 389.7 kg CO2 eq, while RTE1 corresponds to 455.2 kg CO2 eq, a deviation of 16.8%.

[0181] In the SOH3 scenario, the statically assumed emissions are 249.9 kg CO2 eq, while RTE1 corresponds to 280.6 kg CO2 eq, a deviation of 12.3%.

[0182] Figure 8 The lower part (SOH4–SOH6) shows that as SOH degradation intensifies, the emission difference between the static assumption and RTE1 gradually converges. For example:

[0183] In the SOH4 scenario, the statically assumed emissions are 165.7 kg CO2 eq, while RTE1 corresponds to 185.5 kg CO2 eq, a deviation of 11.9%.

[0184] In SOH5 and SOH6 scenarios, the deviations are smaller (approximately 5-8%), while in some scenarios RTE2 or RTE3 are slightly lower than the static model, showing negative deviations.

[0185] From the above analysis, it can be seen that the optimal battery retirement time determination method has significant advantages in the early stage of good health state, especially in the SOH1-SOH3 interval. The static model systematically underestimates in all scenarios, with a maximum deviation of 32.1%. At this time, the precision improvement brought by dynamic RTE adjustment is most significant, which can reduce the system deviation by 4.6%-13.1% on average. In the SOH4-SOH6 deep degradation interval, although the difference is reduced, the optimal battery retirement time determination method can still capture the subtle changes that the static assumption cannot detect, providing a key basis for battery life cycle carbon management, especially in formulating differentiated retirement strategies considering the relative relationship between SOH degradation rate and RTE efficiency decline to achieve optimal emission reduction effect.

[0186] To further reveal the carbon emission error of the static model under different battery health states, Figure 9 Taking the SOH1 degradation curve as an example, the carbon emission comparison between the static model and the three types of RTE degradation models in the four SOH intervals (100%-80%) is shown.

[0187] From Figure 9 As can be seen from the upper left graph, in the whole life cycle stage (SOH 100%-80%), the carbon emission estimated by the static model is 441.5kg CO2 eq, while the RTE1 dynamic model is 583.2kg CO2 eq, with an error of 32.1%. RTE2 and RTE3 are 5.7% and 1.6% higher, respectively, indicating that the static assumption significantly underestimates carbon emissions.

[0188] From Figure 9 As can be seen from the upper right graph, in the new battery stage (100%-95% SOH), the difference between the three types of dynamic models and the static model is the smallest, with emission values of 43.7kg CO2 eq (Static), 44.1kg CO2 eq (RTE1), 43.0kg CO2 eq (RTE2) and 43.4kg CO2 eq (RTE3), respectively. The error range is between -1.7% and 0.7%, indicating that at this stage, the battery state is better, and the difference between the static assumption and the dynamic model is relatively small.

[0189] From Figure 9 As can be seen from the lower left graph, after entering the secondary use stage (95%-85% SOH), the carbon emission difference is significantly enlarged. The carbon emission estimated by the RTE1 model is 250.5kg CO2 eq, while the carbon emission estimated by the static model is 217.9kg CO2 eq, with an error of 15%. The errors of RTE2 and RTE3 are 2.1% and 0.5%, respectively, indicating that the dynamic degradation effect starts to accumulate, and the error increases significantly.

[0190] From Figure 9 It can be seen from the lower right graph that the difference reaches the maximum in the near-retirement stage (SOH 85% ~ 70%), the static model estimates the carbon emissions as 277.0 kg CO2 eq, while the RTE1 model estimates the carbon emissions as 567.0 kg CO2 eq, the deviation expands to 104.7%, and RTE2 and RTE3 also reach high error levels of 15.0% and 4.2%, respectively.

[0191] As shown by the data Figure 10 It can be seen from the data that as the battery SOH gradually decreases, the static model has a significant systematic deviation in evaluating the battery life cycle carbon emissions, and the degree of deviation is positively correlated with the battery degradation state. As the battery health state gradually deteriorates in different operating stages, the error amplitude gradually enlarges. Especially in the middle and late degradation stages (below 85%), the static model cannot effectively reflect the increase in energy consumption caused by efficiency decline, thereby systematically underestimating carbon emissions.

[0192] Therefore, the necessity and superiority of the battery optimal retirement time determination method provided in the application are further verified, especially in the second half of the battery life cycle or in the multi-cycle use scenario. Establishing a full life cycle carbon emission evaluation model that integrates dynamic round-trip efficiency degradation characteristics can significantly improve the accuracy of carbon emission evaluation, and provide more reliable decision basis for retirement strategy formulation and carbon emission reduction target optimization.

[0193] As shown in Figure 10 Under the combination of different battery capacity degradation curves (SOH1-SOH6) and three typical RTE degradation scenarios (RTE1 fast, RTE2 medium, RTE3 slow), the optimal retirement points (expressed by SOH) and the corresponding carbon emission improvement rates (emission reduction effect) results are calculated and optimized respectively and displayed in the form of a heat map.

[0194] From Figure 10 It can be seen that different degradation scenarios have a significant impact on the optimal retirement point position:

[0195] In the fast degradation scenario (RTE1), the optimal retirement point corresponding to SOH1 is 89.6% SOH, which can achieve a carbon emission reduction rate of 28.7%, while the optimal retirement point at SOH5 remains at 80% SOH.

[0196] In the medium degradation scenario (RTE2), the optimal retirement point corresponding to SOH1 is 86.7% SOH, which can achieve a carbon emission reduction rate of 5.7%, while in the SOH3 scenario, the improvement is only 0.4%.

[0197] In the slow degradation scenario (RTE3), the optimal retirement point is significantly delayed, and the retirement threshold is lower. For example, the carbon emission reduction effect of the SOH6 curve is most significant, while the SOH2 curve changes little.

[0198] Figure 11 It is also shown that in multiple degradation scenarios, the optimal retirement point eventually converges to around 70% SOH, which is consistent with the battery life termination standard preset by the application, indicating that the carbon optimization strategy provided by the application not only improves the evaluation accuracy, but also meets the standard of the actual service life of the battery.

[0199] As shown in Figure 12 and Figure 11 , the optimization results of the SOH degradation curves under different RTE conditions are further analyzed, and the distribution of the optimal retirement points ( Figure 12 ) and the corresponding carbon emission improvement rate distribution ( Figure 11 ) are shown in the box plot.

[0200] Figure 11 It is shown that in the RTE1 fast degradation scenario, the average optimal retirement point is 84% SOH, which can achieve a carbon emission reduction of 28.7% compared to the traditional 80% standard, with an average improvement of 10%.

[0201] In the RTE2 medium degradation scenario, the average optimal retirement point is 79% SOH, corresponding to an average emission reduction of 2.8%, indicating that there is still emission reduction potential in the RTE2 scenario.

[0202] In the RTE3 slow degradation scenario, the average optimal retirement point is 75.2% SOH, which can achieve an average emission reduction of 2.3%.

[0203] It is worth noting that in the static RTE scenario, the embodiments of the application are also applicable. Under the condition of fixed 90% efficiency, 72.4% SOH is determined as the optimal retirement point, which can achieve an average emission reduction of 3.6% compared to the traditional 80% SOH standard, proving the wide applicability of the embodiments of the application.

[0204] In summary Figure 12 and Figure 13 It can be understood that in the fast degradation scenario, early retirement is needed to control emissions; in the medium degradation scenario, it is suitable to seek a balance between carbon emission reduction and battery life, and to flexibly set the retirement threshold; in the slow degradation scenario, a lower retirement point is allowed to maximize the battery life. The optimization performance of the embodiments of the application depends on the relative speed of the SOH and RTE degradation curves. In addition, combined with real-time efficiency monitoring and flexible replacement strategy, the retirement point selection can be optimized to control emissions while reducing replacement costs.

[0205] From the above analysis, it can be seen that the battery optimal retirement time determination method proposed in the application exhibits good evaluation accuracy and optimization effect in different battery degradation scenarios, and can be applied to various electric vehicle application scenarios.

[0206] Based on the same inventive concept, the application also provides a battery optimal retirement time determination system for implementing the battery optimal retirement time determination method in the foregoing embodiments according to some embodiments. Therefore, the battery optimal retirement time determination system can achieve the technical effects of the battery optimal retirement time determination method, which will not be repeated here.

[0207] It should also be noted that the technical features between the method embodiments and the system embodiments can be mutually replaced and supplemented without causing conflicts, so as to enable those skilled in the art to understand the technical content of the application.

[0208] Please refer to Figure 13 The battery optimal retirement time determination system can specifically include an evaluation model construction module 100, a degradation model construction module 200, a single-cycle carbon emission amount acquisition module 300, a system carbon emission total amount acquisition module 400, and an optimal retirement point acquisition module 500.

[0209] The evaluation model construction module 100 is configured to establish a battery full-life cycle carbon emission evaluation model that integrates dynamic round-trip efficiency degradation characteristics.

[0210] The degradation model construction module 200 is configured to construct a dynamic round-trip efficiency degradation model that couples round-trip efficiency degradation rates with battery state of health degradation paths based on at least three degradation scenarios of fast, medium, and slow, and establishes a quantitative correlation between round-trip efficiency degradation and carbon emissions.

[0211] The single-cycle carbon emission amount acquisition module 300 is configured to determine the single-cycle energy output and the corresponding single-cycle carbon emission amount of each cycle of the battery by using the dynamic round-trip efficiency degradation model.

[0212] The system carbon emission total amount acquisition module 400 is configured to determine the system carbon emission total amount according to the total amount of full-life cycle energy output of a single battery.

[0213] The optimal retirement point acquisition module 500 is configured to construct an objective function with the minimization of the total amount of full-life cycle carbon emissions as the goal, and determine the optimal retirement point that minimizes the total amount of full-life cycle carbon emissions based on the objective function.

[0214] The various modules in the above battery optimal retirement time determination system can be all or partially implemented by software, hardware, and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above modules.

[0215] It can be understood that the battery optimal retirement time determination system of the present application can also include other existing functional modules supporting the operation of the battery optimal retirement time determination system. Figure 14 The displayed battery optimal retirement time determination system is merely an example and should not bring any limitation to the function and use range of the embodiments of the present application.

[0216] The embodiments of the present application also provide an electronic device, including a processor, a memory having executable instructions of the processor stored therein, wherein the processor is configured to execute the steps of the battery optimal retirement time determination method described in the foregoing embodiments via execution of the executable instructions.

[0217] Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method or a program product. Therefore, each aspect of the present application can be specifically implemented as follows: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "platform" here.

[0218] The electronic device 600 according to this embodiment of the present application will be described below with reference to Figure 14 Figure 14 The displayed electronic device 600 is merely an example and should not bring any limitation to the function and use range of the embodiments of the present application.

[0219] As shown in Figure 1 , the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 can include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0220] The storage unit stores program codes which can be executed by the processing unit 610, so that the processing unit 610 executes the steps of various exemplary embodiments according to the present application described in the battery optimal retirement time determination method part of the present specification. For example, the processing unit 610 can execute the steps as shown in Figure 15

[0221] The storage unit 620 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 6201 and / or a cache memory unit 6202, and can further include a read-only memory (ROM) 6203.

[0222] ​​The storage unit 620 can also include a program / utility 6204 having a set of programs / modules 6205, each of which performs one or more of the processes or functions as described herein. The programs / utility 6204 can include, but is not limited to, one or more of: an operating system, one or more applications, other program modules, and program data.

[0223] The bus 630 can represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration bus, a processor or local bus using any of a variety of bus architectures, and so forth.

[0224] The electronic device 600 can also communicate with one or more external devices 700 such as a keyboard or pointing device, a Bluetooth device, etc.; other devices such as printers, scanners, etc.; and / or various types of networks including a local area network (LAN), a wide area network (WAN), and / or the Internet. In many implementations, the electronic device 600 can communicate with other computing devices via the network adapter 660 using one or more communication protocols, such as TCP / IP (transmission control protocol / Internet protocol), UDP (user datagram protocol), and / or RDP (Rapid Datagram Protocol). Communication can occur via I / O interface 650. Still yet, the electronic device 600 can communicate to one or more devices 700 using other technologies, such as Bluetooth, Zigbee, and / or infrared (IR) technology. In some embodiments, the electronic device 600 can be a multi-functional device, such as a personal digital assistant, which includes multiple functions such as a cellular telephone, a PDA, a laptop computer, and a wireless web appliance.

[0225] The electronic device, when the program in the memory is executed by the processor, realizes the steps of the battery optimal retirement time determination method in the foregoing embodiments, and thus the electronic device can also obtain the technical effects of the battery optimal retirement time determination method.

[0226] The embodiments of the present application also provide a computer readable storage medium for storing a program, and when the program is executed by a processor, the steps of the battery optimal retirement time determination method in the foregoing embodiments are realized.

[0227] In some possible implementations, various aspects of the present application can also be implemented as a program product, including a program code, which when executed on a terminal device, causes the terminal device to perform the steps described in the battery optimal retirement time determination method part of the present specification according to various exemplary embodiments of the present application.

[0228] Reference ​As shown, a program product 800 for implementing the above-described method according to the embodiments of the present application is described, which can take the form of a portable compact disc read-only memory (CD-ROM) and includes program code, and can be executed on a terminal device, such as a personal computer. However, the program product of the present application is not limited to this, and in the present document, the readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device.

[0229] The program product can take any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium, for example, can be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0230] The computer readable storage medium can include a data signal carried by a carrier wave or propagated by a carrier wave, where the carrier wave is embodied in a baseband or propagated as part of a carrier wave. Such a propagated signal can take any of a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. A computer readable storage medium can also be any medium that can be read by a computer or a readable medium that can store or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical fiber, RF, or any suitable combination thereof.

[0231] The program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, C++, or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on a user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet service provider.

[0232] The program in the computer storage medium is executed by the processor to realize the steps of the battery optimal retirement time determination method, and therefore the computer storage medium can also obtain the technical effects of the battery optimal retirement time determination method.

[0233] In the description of the specification, the description of the terms "some embodiments", "as an example", "exemplarily" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiments or examples are contained in at least one embodiment or example of the present application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example.

[0234] The technical features of the above-described embodiments can be combined in any manner. In order to make the description simple, all possible combinations of the technical features of the above-described embodiments are not described, however, as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present application.

[0235] The above-described embodiments only express several implementation manners of the present application, the description is more specific and detailed, but it should not be understood as a limitation on the patent application scope. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the patent protection scope of the present application should be subject to the appended claims.

Claims

1. A method for determining the optimal retirement time of a battery, characterized in that, The application relates to a battery full-life cycle carbon emission evaluation model integrating dynamic round-trip efficiency degradation characteristics. A dynamic round-trip efficiency degradation model is constructed, which at least based on three degradation scenarios of fast, medium and slow, couples the round-trip efficiency degradation rate with the battery state of health degradation path, and establishes the quantitative correlation between round-trip efficiency degradation and carbon emission. The dynamic round-trip efficiency degradation model is used to determine the single-cycle energy output and the corresponding single-cycle carbon emission of each cycle of the battery. According to the total amount of single battery full-life cycle energy output, the total amount of system carbon emission is determined. A target function is constructed with the goal of minimizing the total amount of full-life cycle carbon emission, and the optimal retirement point of minimizing the total amount of full-life cycle carbon emission is determined based on the target function. The battery full-life cycle carbon emission evaluation model includes raw material extraction stage carbon emission calculation model, manufacturing stage carbon emission calculation model, transportation stage carbon emission calculation model, use stage carbon emission calculation model and recovery stage carbon emission calculation model, wherein, 2. The battery optimal retirement time determination method of claim 1, wherein, The raw material extraction stage carbon emission calculation model determines the carbon emission amount of the raw material extraction stage according to the mass of the raw material required for battery manufacturing and the corresponding carbon emission factor of the raw material; The manufacturing stage carbon emission calculation model determines the carbon emission amount of the manufacturing stage according to the energy consumption in the battery manufacturing process and the corresponding carbon emission factor of the energy consumption; The transportation stage carbon emission calculation model determines the carbon emission amount of the transportation stage according to the transportation distance, transportation weight and corresponding carbon emission factor of the transportation process; The use stage determines the energy consumption of each cycle according to the dynamic round-trip efficiency degradation characteristics of the battery in the use process, and determines the carbon emission amount of the use stage by combining the corresponding carbon emission factor of the use process; The recovery stage determines the net carbon emission amount of the recovery process according to the emission reduction benefit of the battery in the recovery process. The dynamic round-trip efficiency degradation model is constructed, which at least based on three degradation scenarios of fast, medium and slow, couples the round-trip efficiency degradation rate with the battery state of health degradation path, and establishes the quantitative correlation between round-trip efficiency degradation and carbon emission.

3. The battery optimal retirement time determination method of claim 1, wherein, The dynamic round-trip efficiency degradation model is used to determine the single-cycle energy output and the corresponding single-cycle carbon emission of each cycle of the battery. The total amount of system carbon emission is determined according to the total amount of single battery full-life cycle energy output. The initial carbon emission amount is obtained. ​ 4. The battery optimal retirement time determination method of claim 1, wherein, ​ ​ 5. The battery optimal retirement time determination method of claim 4, wherein, ​ 6. The battery optimal retirement time determination method of claim 1, wherein, ​ ​ acquiring a single battery full life cycle output energy, and determining a required number of batteries based on the single battery full life cycle output energy and a specified total energy requirement; determining the total system carbon emission amount based on the initial carbon emission amount, the carbon emission amount of full use of the battery, and the carbon emission amount of partial use of the battery.

7. The battery optimal retirement time determination method of claim 1, wherein, constructing an objective function with the objective of minimizing the total life cycle carbon emission amount, and determining an optimal retirement point of minimizing the total life cycle carbon emission amount based on the objective function. constructing an objective function with the objective of minimizing the total life cycle carbon emission amount; determining the optimal retirement point through a cycle counting module, a carbon emission evaluation module, and a retirement optimization module, wherein the cycle counting module is configured to determine a minimum cycle number satisfying the energy requirement based on dynamic round-trip efficiency characteristics, the carbon emission evaluation module is used for life cycle analysis, and the retirement optimization module is configured to determine the optimal retirement point through the objective function.

8. A battery optimal retirement time determination system, comprising: A determination system for implementing the battery optimal retirement time determination method of any one of claims 1 to 7, comprising: an evaluation model construction module configured to establish a battery full life cycle carbon emission evaluation model integrating dynamic round-trip efficiency degradation characteristics; a degradation model construction module configured to construct a dynamic round-trip efficiency degradation model coupling round-trip efficiency degradation rates with battery health state degradation paths based on at least three degradation scenarios of fast, medium, and slow, and establishing a quantitative correlation between round-trip efficiency degradation and carbon emission; a single cycle carbon emission amount acquisition module configured to determine single cycle energy output and corresponding single cycle carbon emission amount of the battery in each cycle by using the dynamic round-trip efficiency degradation model; a system carbon emission total amount acquisition module configured to determine the total system carbon emission amount based on the total amount of single battery full life cycle energy output; an optimal retirement point acquisition module configured to construct an objective function with the objective of minimizing the total life cycle carbon emission amount, and determine an optimal retirement point of minimizing the total life cycle carbon emission amount based on the objective function.

9. An electronic device, comprising: comprising: a processor; a memory having executable instructions of the processor stored therein; wherein the processor is configured to execute the steps of the battery optimal retirement time determination method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer readable storage medium for storing a program, characterized in that, The program is executed by the processor to implement the steps of the battery optimal retirement time determination method of any one of claims 1 to 7.

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