Lithium battery cycle mechanism simulation analysis method, device, equipment, medium and product

Abstract

The application discloses a lithium battery circulation mechanism simulation analysis method, device, equipment, medium and product. The method obtains corresponding multi-source basic data of the lithium battery and a first social accounting matrix table, wherein the multi-source basic data includes scenario parameters set under a preset scenario, and the first social accounting matrix table is obtained by expanding a benchmark social accounting matrix table in the circulation life cycle of the lithium battery; the multi-source basic data and the first social accounting matrix table are input into a target equilibrium model constructed in advance, data processing of the lithium battery under the preset scenario is provided through the target equilibrium model, output results corresponding to a target stage under the preset scenario are obtained, and the output results include a second social accounting matrix table; and effect analysis results corresponding to the lithium battery under the preset scenario are determined according to the output results. By using the method, the coupling relationship between the forward production flow and the reverse recovery flow of the lithium battery is completely described, and the comprehensiveness, accuracy and reliability of the evaluation are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to methods, apparatus, equipment, media and products for simulating and analyzing the cycling mechanism of lithium batteries. Background Technology

[0002] The rapid development of the global new energy vehicle and energy storage industries has driven a continuous increase in demand for lithium batteries, accompanied by a rapid growth in the scale of retired batteries. Lithium batteries contain strategic metal resources such as lithium, cobalt, and nickel, and their recycling is of great significance to resource security and environmental protection. In the practical promotion of lithium battery recycling, various application scenarios, such as the layout of cross-regional recycling networks and the optimization of resource allocation, increasingly require precise assessment of the recycling effects corresponding to each scenario.

[0003] However, there is currently a lack of evaluation methods specifically for lithium-ion battery cycling scenarios. Existing evaluation methods based on computable general equilibrium models have significant limitations when applied to lithium-ion battery cycling scenarios, making it difficult to meet practical needs. Current evaluation methods only construct unidirectional flow structures based on forward production flow, resulting in a lack of comprehensiveness in the evaluation results, which in turn affects the accuracy and reliability of the evaluation outcomes. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and product for simulating and analyzing the cycling mechanism of lithium batteries, in order to solve the problem that existing evaluation methods, which only construct a unidirectional flow structure based on forward production flow, lack comprehensiveness in the evaluation results, thereby reducing the accuracy and reliability of the evaluation results.

[0005] In a first aspect, embodiments of the present invention provide a method for simulating and analyzing the cycling mechanism of a lithium battery, the method comprising: Obtain multi-source basic data and a first social accounting matrix table corresponding to the lithium battery. The multi-source basic data includes scenario parameters set under a preset scenario. The first social accounting matrix table is obtained by expanding the benchmark social accounting matrix table during the cycle life of the lithium battery. The multi-source basic data and the first social accounting matrix table are input into the pre-constructed target equilibrium model. The target equilibrium model provides data processing for the lithium battery under the preset scenario, and obtains the output results corresponding to the target stage under the preset scenario. The output results include the second social accounting matrix table. The effect analysis result of the lithium battery under the preset scenario is determined based on the output result.

[0006] Secondly, embodiments of the present invention provide a lithium battery cycle mechanism simulation and analysis device, the device comprising: The acquisition module is used to acquire multi-source basic data corresponding to the lithium battery and a first social accounting matrix table. The multi-source basic data includes scenario parameters set under a preset scenario. The first social accounting matrix table is obtained by expanding the benchmark social accounting matrix table during the cycle life of the lithium battery. The first result determination module is used to input the multi-source basic data and the first social accounting matrix table into the pre-constructed target equilibrium model, and provide data processing of the lithium battery under the preset scenario through the target equilibrium model to obtain the output result corresponding to the target stage under the preset scenario. The output result includes the second social accounting matrix table. The second result determination module is used to determine the effect analysis result of the lithium battery under the preset scenario based on the output result.

[0007] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the lithium battery cycle mechanism simulation analysis method according to any embodiment of the present invention.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute the lithium battery cycle mechanism simulation analysis method described in any embodiment of the present invention.

[0009] Fifthly, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the lithium battery cycle mechanism simulation analysis method according to any embodiment of the present invention.

[0010] The technical solution of this invention involves acquiring multi-source basic data corresponding to the lithium battery and a first social accounting matrix table. The multi-source basic data includes scenario parameters set under a preset scenario. The first social accounting matrix table is obtained by expanding a benchmark social accounting matrix table during the cyclic life cycle of the lithium battery. The multi-source basic data and the first social accounting matrix table are input into a pre-constructed target equilibrium model. The target equilibrium model provides data processing for the lithium battery under the preset scenario, obtaining the output results corresponding to the target stage under the preset scenario. The output results include a second social accounting matrix table. Based on the output results, the effect analysis results of the lithium battery under the preset scenario are determined. Using this method, an extended first social accounting matrix table matching the cycling characteristics of lithium batteries is obtained, avoiding the data contradictions that may occur with traditional data and the problem that focusing only on unidirectional flow cannot support accurate cycle assessment. By using a target equilibrium model that supports data processing of lithium batteries under preset scenarios, the coupling relationship between forward production flow and reverse recycling flow is fully characterized, resulting in output results that intuitively reflect the overall impact of scenarios on the lithium battery cycle chain. This improves the scenario adaptability, accuracy, and effectiveness of the simulation, thereby enhancing the comprehensiveness, accuracy, and reliability of the assessment and providing strong support for subsequent decision-making.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating a method for simulating and analyzing the cycling mechanism of a lithium battery, provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a lithium battery cycle mechanism simulation and analysis device provided in an embodiment of the present invention; Figure 3 A schematic diagram of an electronic device that can be used to implement embodiments of the present invention is shown. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] It is understood that before using the technical methods disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0017] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as the electronic device, application, server, or storage medium performing the operations of this disclosed technology.

[0018] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0019] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0020] Based on this, embodiments of the present invention provide a method for simulating and analyzing the cycling mechanism of lithium batteries. Figure 1 This is a flowchart of a lithium battery cycling mechanism simulation analysis method provided by an embodiment of the present invention. The embodiment of the present invention can be applied to scenarios where information in lithium battery cycling-related scenarios is evaluated. The method can be executed by a lithium battery cycling mechanism simulation analysis device, which can be implemented in the form of software and / or hardware, and optionally, by an electronic device, preferably a mobile terminal, desktop computer, laptop computer, or server.

[0021] like Figure 1 As shown, the lithium battery cycle mechanism simulation and analysis method provided in this embodiment of the invention may specifically include: S101. Obtain multi-source basic data and the first social accounting matrix table corresponding to the lithium battery. The multi-source basic data includes scenario parameters set under the preset scenario. The first social accounting matrix table is obtained by expanding the benchmark social accounting matrix table during the cycle life of the lithium battery.

[0022] Multi-source basic data can be understood as various raw data supporting the assessment, characterized by its wide range of sources and coverage of the entire recycling chain. For example, multi-source basic data can come from industry surveys or literature. Multi-source basic data may include battery-related data, such as the metal content per unit battery (lithium, cobalt, and nickel, etc.), battery lifespan, the stock of batteries in use at the retirement site, and a pre-constructed reverse trade matrix, where matrix elements represent the amount of used batteries transported from the retirement site to the recycling site. Multi-source basic data may also include cost data, such as primary metal costs, recycled metal costs, recycling costs, and logistics-related costs, which may include costs and prices. Preset scenarios may include technological advancements and changes in resource supply. Correspondingly, multi-source basic data may also include scenario parameters set under the preset scenarios, such as recovery rate, percentage increase in recovery rate, and percentage increase in metal costs, used to simulate the recycling effects under corresponding possible scenarios.

[0023] In this embodiment, the baseline social accounting matrix (SAM) table can be understood as a traditional matrix-formatted information table recording the flow and allocation of resources in regular resource transfer activities. The first social accounting matrix table can be understood as a social accounting matrix table that matches the lithium battery cycle life cycle by specifically extending the baseline social accounting matrix table, matching the cyclical characteristics of lithium battery production, use, retirement, recycling, and regeneration. Optionally, the extension of the baseline social accounting matrix table may include splitting accounts, adding new accounts, and adding new production factors, etc.

[0024] S102. Input the multi-source basic data and the first social accounting matrix table into the pre-constructed target equilibrium model. The target equilibrium model provides data processing for lithium batteries under a preset scenario, and obtains the output results corresponding to the target stage under the preset scenario. The output results include the second social accounting matrix table.

[0025] The target equilibrium model can be considered a computable general equilibrium model used to simulate the entire lithium battery cycle under a preset scenario. The target phase can be understood as the simulation execution cycle. A target phase can contain only one cycle, such as one month or one year, or it can contain multiple cycles, such as from the first year to the tenth year. For example, the target phase can be input through an interactive object or obtained from system settings. The second social accounting matrix table can be understood as the social accounting matrix table obtained after data processing by the target equilibrium model. It is used to record the changes of each account based on the first social accounting matrix table under the preset scenario, to intuitively reflect the impact of the scenario on the cycle effect.

[0026] For example, the target equilibrium model can be used to characterize the forward flow in the cyclic life cycle of lithium batteries, such as raw materials → products → consumption, and it can also be used to characterize the reverse flow in the cyclic life cycle of lithium batteries, such as waste products → recycling → recycled resources. It can also be used to characterize the cross-regional spatial optimization characteristics in the cyclic life cycle, such as the logistics network characteristics of waste batteries from the decommissioning site to the recycling site. Furthermore, it can be used to characterize the dynamic impact of recycling rate on factors such as metal prices, external incentives and / or logistics costs. Finally, it can be used to characterize the impact of external factors on the ratio of primary metals to recycled metals used.

[0027] In this embodiment, multi-source basic data and the first social accounting matrix table are input into a pre-constructed target equilibrium model. The target equilibrium model provides data processing for lithium batteries under a preset scenario, thereby simulating the entire lithium battery cycle before reaching the target stage under the preset scenario. For example, the preset scenario is a metal cost adjustment, with scenario parameters being a metal cost increase of A% or a metal cost increase to B. The model simulates how the lithium battery recycling rate, the transportation route of waste lithium batteries, and the proportion of recycled metals used might be adjusted under the preset scenario. In this step, the metal cost can be considered as the metal price.

[0028] As described above, the target equilibrium model can ensure the validity of the simulation results based on equilibrium solution logic, through market clearing conditions, factor constraints, etc., and output the output results corresponding to the target stage under the preset scenario when the iteration termination condition is met. The output results can include data representing the cycle effect or situation, such as the second social accounting matrix table, and can also include data that has reached the equilibrium condition after adjusting the multi-source basic data through the simulation process, such as the recovery rate and cost of recycled metals under the equilibrium condition.

[0029] S103. Determine the effect analysis results of the lithium battery under the preset scenario based on the output results.

[0030] In this embodiment, the effect analysis results under the preset scenario can be determined based on the output results (such as the increase in recycling rate, the rate of replacement of recycled metals, the amount of resource savings and / or the changes in the flow of resources in the industrial chain), as well as the preset evaluation indicators and indicator thresholds, so as to clarify the regional recycling effect of lithium batteries, resource and environmental benefits, and / or the optimization effect of the recycling industry layout under the preset scenario, and provide a reference for subsequent decision-making or business promotion.

[0031] The lithium battery cycle mechanism simulation and analysis method provided in this invention obtains multi-source basic data and a first social accounting matrix table corresponding to the lithium battery. The multi-source basic data includes scenario parameters set under a preset scenario. The first social accounting matrix table is obtained by expanding the baseline social accounting matrix table during the cycle life of the lithium battery. The multi-source basic data and the first social accounting matrix table are input into a pre-constructed target equilibrium model. The target equilibrium model provides data processing for the lithium battery under the preset scenario to obtain the output results corresponding to the target stage under the preset scenario. The output results include a second social accounting matrix table. Based on the output results, the effect analysis results of the lithium battery under the preset scenario are determined. Using this method, an extended first social accounting matrix table matching the cycling characteristics of lithium batteries is obtained, avoiding the data contradictions that may occur with traditional data and the problem that focusing only on unidirectional flow cannot support accurate cycle assessment. By using a target equilibrium model that supports data processing of lithium batteries under preset scenarios, the coupling relationship between forward production flow and reverse recycling flow is fully characterized, resulting in output results that intuitively reflect the overall impact of scenarios on the lithium battery cycle chain. This improves the scenario adaptability, accuracy, and effectiveness of the simulation, thereby enhancing the comprehensiveness, accuracy, and reliability of the assessment and providing strong support for subsequent decision-making.

[0032] As a first optional embodiment of the present invention, based on the above embodiments, the first social accounting matrix table can be obtained by extending the benchmark social accounting matrix table, specifically as follows / specifically optimized as follows: a1) Split the first battery account in the baseline social accounting matrix into a second battery account and a third battery account, split the first metal account into a second metal account and a third metal account according to its source, and add a first recycling-related account and a first resource-related account to obtain the expanded initial social accounting matrix.

[0033] In this embodiment, the baseline social accounting matrix table can be split into accounts and new accounts can be added based on the cyclical characteristics of lithium battery production, use, retirement, recycling and regeneration. This results in the initial social accounting matrix table after the account splitting and account additions include both forward flow data such as new batteries and primary metals, and reverse flow data such as waste batteries, recycled metals and cross-regional flows.

[0034] For example, the first battery account can be understood as the unified account corresponding to batteries in the benchmark social accounting matrix. It can be split according to forward and reverse flows. The resulting second battery account can be considered the account corresponding to new batteries, used to record the forward flow of new batteries. The resulting third battery account can be considered the account corresponding to used batteries, used to record the reverse flow of used batteries discarded by consumers. The first metal account can be understood as the unified account corresponding to metals in the benchmark social accounting matrix. It can be split according to primary and recycled sources. The resulting second metal account can be used to record the primary mining, processing, and / or supply of metals such as lithium, cobalt, and nickel. The resulting third metal account can be used to record the supply of recycled metals recovered from used batteries. The first recycling-related account can be considered the recycling industry production activity account, used to record recycling production operations and factor inputs. The first resource-related account can be understood as the extended producer responsibility system fund institution account.

[0035] In an alternative embodiment, “existing stock of used batteries” can be added as a special production factor for the recycling industry.

[0036] In an alternative embodiment, a reverse transport matrix can also be constructed. ,in, For battery decommissioning sites, For battery recycling locations, the elements in the matrix represent the amount of used batteries transported from the battery retirement location to the battery recycling location, and the unit can be tons.

[0037] b1) Fill the initial social accounting matrix table according to the resource transfer relationship between accounts to generate the first social accounting matrix table. The resource transfer relationship between accounts includes the resource transfer relationship between the consumer account and the third battery account, the third battery account and the first recycling-related account, and the first recycling-related account and the recycled materials account.

[0038] In this embodiment, after constructing the initial social accounting matrix table based on the cycle life of lithium batteries, the resource transfer relationship between each account can be quantified according to the actual logic of lithium battery cycles, so as to achieve reasonable data filling for each account.

[0039] For example, the resource transfer relationship between the consumer account and the third-party battery account can be represented as follows: ; in, for Battery sales volume in areas where batteries are decommissioned at a certain stage; Service life The corresponding battery retirement rate; The residual value of used batteries.

[0040] For example, the resource transfer relationship between the third battery account and the first recycling-related account can be specifically represented as follows: ; in, The cost of collecting batteries at their decommissioning sites; This refers to the logistical costs incurred from the battery's decommissioning location to its recycling location. These costs can be considered as expenses.

[0041] For example, the resource transfer relationship between the first recycling-related account and the recycled materials account can be represented as follows: ; in, These are metals, including lithium, cobalt, nickel, and manganese; The metal content per unit battery is determined based on the type of lithium battery. Metal recovery rate; The cost of recycled materials. In this step, the cost of recycled materials can be considered as the price of recycled materials.

[0042] In this embodiment, the initial social accounting matrix table is filled according to the resource transfer relationship between accounts, and each row and column of the generated first social accounting matrix table is completely balanced.

[0043] The above-described technical solution in this embodiment obtains a first social accounting matrix table that can accurately depict the entire chain of forward production flow and reverse circulation flow by splitting and adding accounts and filling the data of accounts by quantifying the transfer relationship between accounts. This solves the problem that the benchmark social accounting matrix table cannot adapt to the cycle life of lithium batteries and provides accurate and effective input for the subsequent target equilibrium model.

[0044] As a second optional embodiment of the present invention, based on the above embodiments, the target equilibrium model includes a lithium battery full life cycle tracking module, a reverse logistics network optimization module, a recycling rate endogenous response module, a primary and recycled material substitution module, and an equilibrium main module.

[0045] a2) The lithium battery full life cycle tracking module is used to determine the amount of batteries in use at the target stage based on the battery consumption attribute information corresponding to the battery retirement location in the multi-source basic data.

[0046] The lithium battery lifecycle tracking module can be used to determine how many batteries are in use and how many will be retired at a target stage. Battery consumption attribute information can include historical battery inventory, newly added battery volume, and retired battery volume. For example, newly added battery volume can include lithium batteries produced at the retirement location and lithium batteries obtained from other regions.

[0047] Optionally, the method for determining the in-use battery inventory at the battery retirement location in the target stage based on the battery consumption attribute information corresponding to the battery retirement location can be as follows: determine the sum of the historical battery inventory and the newly added battery inventory, and determine the difference between the sum and the retired battery inventory as the in-use battery inventory at the battery retirement location in the target stage. For example, the method for determining the in-use battery inventory at the battery retirement location in the target stage can be expressed as: ; in, For battery decommissioning sites in the target phase The stock of batteries in use; This refers to the historical battery inventory at the battery decommissioning site, that is, in... The current stock of batteries in use at this stage; For battery decommissioning sites in the target phase The amount of new batteries; For battery decommissioning sites in the target phase The amount of retired batteries.

[0048] Optionally, the number of retired batteries is determined based on the retirement probability of the lithium batteries, which is determined based on the service life of the lithium batteries combined with the Weibull distribution.

[0049] For example, the probability of retiring a lithium battery can be determined by combining the battery's service life with a Weibull distribution. The method can be expressed as: ; in, The scale parameter can be the average retirement age or average lifespan of a lithium battery. This is a shape parameter used to reflect the degree of decommissioning concentration, such as 2.5.

[0050] Correspondingly, the battery decommissioning site is in the target phase retired battery volume It can be represented as: ; in, This refers to the maximum lifespan of a lithium battery (e.g., 15 years).

[0051] The technical solution described in this embodiment accurately fits the life cycle characteristics of lithium batteries, which are characterized by fewer early-stage retirements, more mid-stage retirements, and fewer late-stage retirements, using the Weibull distribution. This improves the accuracy of determining the retirement probability of lithium batteries and, consequently, the accuracy of tracking the dynamic process of lithium batteries from commissioning to retirement, providing basic data for subsequent recycling and logistics planning.

[0052] b2) The reverse logistics network optimization module is used to determine the unit logistics cost from the battery decommissioning location to the battery recycling location based on the logistics cost attribute information in the multi-source basic data, and to determine the first transportation volume matrix based on the unit logistics cost and recycling processing cost combined with the first constraint condition.

[0053] The reverse logistics network optimization module can be used to establish a logistics network model for used batteries from their retirement location to their recycling location, in order to help determine the most reasonable route for used batteries and where recycling capacity should be built.

[0054] In this embodiment, the logistics cost attribute information may include fixed loading and unloading costs from the battery decommissioning location to the battery recycling location, distance variation costs, transportation distance, cross-regional additional costs, and additional costs related to transportation risks. The costs involved in the logistics cost attribute information can be considered as expenses. The first constraint can be considered as a relative reverse transportation matrix. The constraints to be constructed.

[0055] For example, the method for determining the unit logistics cost from the battery decommissioning location to the battery recycling location based on logistics cost attribute information can be expressed as follows: ; in, To fix the loading and unloading costs, the unit can be yuan / ton; The cost of distance variation can be expressed in yuan per (ton-kilometer). The transportation distance between regions; For additional costs across regions, the unit can be yuan / ton, and may include cross-regional inspection costs, etc. For cross-regional transportation indication functions, when the battery is decommissioned... With battery recycling sites When they belong to the same area, ,otherwise, ; Additional costs for transportation risks can be expressed in yuan per ton. For example, additional costs for transportation risks may include costs such as dedicated vehicle leasing, insurance, and qualification certification. Let this be the transportation risk trigger indication function. When the lithium battery meets the risk trigger condition during transportation, then... ,otherwise, Risk triggering conditions can be predetermined by managers.

[0056] In this embodiment, the method for determining the first transportation volume matrix based on the unit logistics cost and recycling cost combined with the first constraint can be as follows: A model minimizing the total cost of recycling site selection is established based on the unit logistics cost, recycling cost, and reverse transportation matrix. The reverse transportation matrix that satisfies the first constraint when the total cost of recycling site selection is minimized is determined as the first transportation volume matrix. The total cost of recycling site selection can be considered as the sum of the costs incurred in completing the recycling of waste batteries at the recycling site.

[0057] For example, the total cost of choosing a recycling location The minimization model can be expressed as: ; in, Battery recycling location The cost of recycling and processing can be considered as the price of recycling and processing.

[0058] In this embodiment, the first constraint includes a recycling rate constraint, a scenario parameter feasibility constraint, and a recycling capacity constraint. The recycling rate constraint can be used to ensure that the amount of used batteries recycled at the battery retirement site meets the preset recycling rate requirement, avoiding waste of recyclable resources. For example, the recycling rate constraint can be expressed as... ,in, Battery decommissioning site The recovery rate at the target stage can be determined based on scenario parameters or by the recovery rate intrinsic response module. Scenario parameter feasibility constraints can be used to ensure that the transportation plan under the preset scenario complies with relevant regulations for cross-regional recycling. For example, scenario parameter feasibility constraints can be expressed as... ,in, The indicator function can be determined according to the relevant regulations on cross-regional recycling; For a sufficiently large number. Recycling capacity constraints can be used to ensure battery recycling... The amount received should not exceed its actual processing capacity to avoid overloading the battery recycling site. For example, the recycling capacity constraint can be expressed as... ,in, This refers to the processing capacity of the battery recycling site. It is understandable that the first constraint can also include a non-negativity constraint, specifically expressed as... .

[0059] In this embodiment, a linear programming solver can be used to solve the model that minimizes the total cost of reclaim site selection under the first constraint, find the reverse transportation matrix that minimizes the total cost of reclaim site selection, determine it as the first transportation volume matrix, and output it.

[0060] c2) The endogenous response module for recovery rate is used to determine the recovery rate of the target stage based on the recovery attribute information in the multi-source basic data.

[0061] The recycling attribute information may include the lower limit of natural recycling rate, the upper limit of technical feasibility, the flexibility coefficient, the residual value of waste batteries, and the constraint enforcement strength index.

[0062] It should be noted that existing technologies all set the recovery rate as a constant, fixed parameter, ignoring the dynamic impact of factors such as metal costs, external incentives, and logistical costs on the recovery rate. This leads to a decrease in the accuracy of simulating the lithium battery cycle life. Therefore, this embodiment constructs an endogenous recovery rate response module to internalize the recovery rate, improving the accuracy of dynamic simulation of the lithium battery cycle life under preset scenarios.

[0063] For example, the method for determining the recovery rate of the target stage based on the recovery attribute information in the multi-source basic data can be expressed as follows: ; in, This represents the natural lower limit of the recovery rate. The technically feasible upper limit for recovery rate; To constrain the implementation intensity index, a value between 0 and 10 can be used; The elasticity coefficient relative to the residual value of used batteries; The elastic coefficient for the relative constraint strength index can optionally be, and It can be obtained by regression estimation based on historical data.

[0064] d2) The primary and recycled material substitution module is used to determine the amount of primary material input, the amount of recycled material input, and the ratio of recycled to primary material usage in the target stage, and to determine the total supply of metal in the target stage based on the amount of primary material input, the amount of recycled material input, and the ratio of recycled to primary material usage.

[0065] In this embodiment, the substitution relationship between primary and recycled materials is characterized by the primary and recycled material substitution module, and the cost-minimizing condition for material selection is established. The proportion of primary and recycled materials used is intrinsically determined, thereby accurately tracking the material flow of metals in the battery.

[0066] Optionally, the amount of recycled material input is determined based on the second transport volume matrix, unit battery metal content, and recovery rate of the target stage in the multi-source basic data; the ratio of recycled to virgin material usage is determined based on the virgin material transfer cost, recycled material transfer cost, and technology preference coefficient of the target stage in the multi-source basic data; and the amount of virgin material input is determined based on the amount of recycled material input and the ratio of recycled to virgin material usage.

[0067] Among them, the second transport volume matrix The elements in the table represent the quantity of recycled material transported from the battery recycling site to the battery decommissioning site, and the unit can be tons; recycled material can be considered as the recycled metal obtained by the recycling site after the lithium battery recycling is completed.

[0068] For example, the method for determining the amount of recycled material input of metal in the target stage based on the second transport volume matrix of the target stage, the metal content per unit battery, and the recovery rate can be expressed as follows: ; in, Battery decommissioning site metal In the target phase The amount of recycled materials input; For the target phase The second transport volume matrix.

[0069] For example, the method for determining the ratio of recycled to virgin materials use based on the transfer cost of virgin materials, the transfer cost of recycled materials, and the technology preference coefficient at the target stage can be as follows: ; in, Battery decommissioning site metal In the target phase The amount of raw materials input; Metal In the target phase The cost of transferring the original materials, where cost can be considered as price; Metal In the target phase The cost of transferring recycled materials, where cost can be considered as price; This is the technology preference coefficient; Allocate shares for virgin materials. Allocate a share for recycled materials; Metal The substitution elasticity is 1.5 for lithium and 2.0 for cobalt, for example.

[0070] In this embodiment, the method for determining the total supply of metal in the target stage based on the input of virgin materials, the input of recycled materials, and the ratio of recycled to virgin materials used can be expressed as follows: ; ; in, Battery decommissioning site metal In the target phase Total supply.

[0071] e2) The balancing main module is used to determine the second social accounting matrix table according to the called processing function, combined with the in-use battery inventory, the first transportation volume matrix, the recycling rate, the input of virgin materials, the input of recycled materials and the set clearing conditions corresponding to the target stage, and update the multi-source basic data based on the update function, and generate output results based on the second social accounting matrix table and the updated multi-source basic data.

[0072] In this embodiment, the balancing module can use the called processing function to combine the output or intermediate results of the above modules to characterize the behavioral logic of each actor in the lithium battery cycle life cycle, and at the same time connect the linkage link between the recycling industry, battery manufacturing industry, factor market and regulation and management.

[0073] Optionally, the processing function includes a production function for the battery manufacturing industry, a production function for the battery recycling industry, a utility function, and a revenue and expenditure function; the production function for the battery manufacturing industry is based on a first value-added sub-function and a first substitution sub-function; the production function for the battery recycling industry is based on the first battery input, the amount of recycled resources, the amount of labor input, and technical efficiency parameters at the decommissioning site.

[0074] Among them, the production function of the battery manufacturing industry is used to characterize production decisions at a given level of technology, including how to combine the value-added part and the input of various metal materials to maximize battery output.

[0075] For example, the production function of the battery manufacturing industry can be expressed as: ; in, Battery decommissioning site The total output of the battery manufacturing industry, namely, the production of new batteries; This refers to the total factor productivity (technical efficiency parameter) of the battery manufacturing industry. For the first value-added sub-function, namely capital and labor The increase in value remains constant, replacing the elastic form function; Value added share (the proportion of capital and labor's contribution to total output); The first substitution subfunction can be calibrated using social accounting matrix table data from the basic year; This represents the percentage of contribution of various metallic materials to the total output.

[0076] In this embodiment, the utility function can be in the form of Stone-Geary or constant elasticity of substitution; the revenue and expenditure function can be used to characterize the regulatory logic of the Extended Producer Responsibility (EPR) system. The revenue and expenditure function can include a revenue function and an expenditure function. Since unrecycled batteries will generate environmental costs, by levying the EPR cost (i.e., EPR fee) on the batteries sold to battery producers and / or sellers, the environmental costs are internalized, and recycling subsidies are provided to the recycling industry for the batteries that have been recycled, reducing the costs incurred by recycling companies and incentivizing them to expand recycling capacity and increase recycling rates.

[0077] For example, the income function can be expressed as: ; in, For the target phase The costs of the extended producer responsibility system; For the target phase The extended producer responsibility rate can be expressed in yuan / ton.

[0078] For example, the expenditure function can be expressed as: ; in, For the target phase Recycling subsidies; For the target phase The recycling subsidy rate can be expressed in yuan / ton.

[0079] Optionally, the set clearing conditions may include clearing conditions for commodity markets, including new batteries, used batteries, and various metals; the set clearing conditions may also include clearing conditions for factor markets such as labor and capital; and the set clearing conditions may also include macroeconomic closing conditions, such as savings-investment balance and trade balance.

[0080] For example, the conditions for market clearing in goods can be expressed as: ; in, Battery decommissioning site Goods The supply; Battery decommissioning site Goods The demand.

[0081] In this embodiment, to improve the convergence and computational efficiency of the numerical solution, a nested iterative algorithm can be employed based on the target equilibrium model. For example, the process of obtaining the output result using a nested iterative algorithm based on the target equilibrium model can be described as follows: Initialize the multi-source basic data, the first social accounting matrix table, and the target stage (e.g., 1 to T (maximum target stage)) corresponding to the lithium battery. Input the multi-source basic data and the first social accounting matrix table into the target equilibrium model. Through the modules included in the target equilibrium model, based on the multi-source basic data and the first social accounting matrix table, solve for maximizing producer profits to obtain the stock of batteries in use as supply, solving for maximizing consumer utility to obtain demand, renewal recycling rate, and solving for minimizing the total cost of recycling location selection to obtain the first transportation volume matrix, etc.; determine the excess demand based on the stock of batteries in use and the demand; and determine the update function based on the multi-source basic data and the excess demand. The algorithm updates the multi-source basic data; it then returns to re-execute the steps related to maximizing producer profits based on the multi-source basic data and the first social accounting matrix to obtain the supply, until the inner iteration termination condition is met. The multi-source basic data and the second social accounting matrix obtained at the end of the iteration are then determined as the output results of the target stage. Based on the output results, the in-use battery inventory is updated through the lithium battery full life cycle tracking module to obtain the in-use battery inventory that can be used for the next target stage. The algorithm then returns to re-execute the steps related to initializing the multi-source basic data and the first social accounting matrix corresponding to the lithium battery, until the outer iteration termination condition is met, and the output results corresponding to each target stage are obtained.

[0082] For example, in the inner layer, the first In the next iteration, the stock of batteries in use, which serves as the supply, can be expressed as: The demand can be expressed as Excess demand can be expressed as The update function can be represented as: ;in, and The first The second iteration and the first The next iteration uses multi-source basic data. This is the adjustment coefficient.

[0083] Optionally, the termination condition for the inner iteration can be a convergence condition and / or reaching the maximum number of inner iterations. ), for example, the first The convergence condition for the nth iteration can be expressed as: , The preset convergence threshold (e.g.) The outer iteration termination condition can be reaching the maximum target stage.

[0084] The technical solution described in this embodiment tracks the evolution of battery inventory across different batches through a lithium battery lifecycle tracking module, constructs a consistency constraint equation between inventory and flow, and ensures a dynamic balance among battery inventory, new inventory, and retired inventory at each target stage. A reverse logistics network optimization module establishes a cross-regional flow decision model for waste batteries from their retirement location to their recycling location, determining the most spatially and costly first transport volume matrix. An endogenous response module for recycling rate internalizes the recycling rate, enabling dynamic responses to the impact of recycling attribute information. A module for primary and recycled material substitution characterizes the substitution relationship between primary and recycled materials, endogenously determining the usage ratio of primary and recycled materials. A balance module combines other modules in the target balance model and provides primary data processing, thereby achieving unified modeling of forward and reverse flows in the lithium battery cycle lifecycle, improving the accuracy, effectiveness, and reliability of the output results, and consequently improving the accuracy and reliability of the effect analysis results.

[0085] Figure 2 This is a schematic diagram of a lithium battery cycle mechanism simulation and analysis device provided in an embodiment of the present invention. Figure 2 As shown, the device includes: an acquisition module 21, a first result determination module 22, and a second result determination module 23.

[0086] The acquisition module 21 is used to acquire multi-source basic data and a first social accounting matrix table corresponding to the lithium battery. The multi-source basic data includes scenario parameters set under a preset scenario. The first social accounting matrix table is obtained by expanding the benchmark social accounting matrix table during the cycle life of the lithium battery. The first result determination module 22 is used to input the multi-source basic data and the first social accounting matrix table into the pre-constructed target equilibrium model, and provide data processing of the lithium battery under the preset scenario through the target equilibrium model to obtain the output result corresponding to the target stage under the preset scenario. The output result includes the second social accounting matrix table. The second result determination module 23 is used to determine the effect analysis result of the lithium battery under the preset scenario based on the output result.

[0087] The lithium battery cycle mechanism simulation and analysis device provided in this embodiment of the invention acquires multi-source basic data corresponding to the lithium battery and a first social accounting matrix table. The multi-source basic data includes scenario parameters set under a preset scenario. The first social accounting matrix table is obtained by expanding the benchmark social accounting matrix table during the cycle life of the lithium battery. The multi-source basic data and the first social accounting matrix table are input into a pre-constructed target equilibrium model. The target equilibrium model provides data processing for the lithium battery under the preset scenario to obtain the output results corresponding to the target stage under the preset scenario. The output results include a second social accounting matrix table. Based on the output results, the effect analysis results of the lithium battery under the preset scenario are determined. Using this device, an extended first social accounting matrix table matching the cycling characteristics of lithium batteries is obtained, avoiding the data contradictions that may occur with traditional data and the problem that focusing only on unidirectional flow cannot support accurate cycle assessment. By using a target equilibrium model that supports data processing of lithium batteries under preset scenarios, the coupling relationship between forward production flow and reverse recycling flow is fully characterized, resulting in output results that intuitively reflect the overall impact of scenarios on the lithium battery cycle chain. This improves the scenario adaptability, accuracy, and effectiveness of the simulation, thereby enhancing the comprehensiveness, accuracy, and reliability of the assessment and providing strong support for subsequent decision-making.

[0088] Furthermore, the lithium battery cycle mechanism simulation and analysis device also includes an expansion module, which can be specifically used for: The first battery account in the baseline social accounting matrix is ​​split into the second battery account and the third battery account. The first metal account is split into the second metal account and the third metal account according to the source. The first recycling-related account and the first resource-related account are added to obtain the expanded initial social accounting matrix. The initial social accounting matrix table is filled with the resource transfer relationships between accounts to generate the first social accounting matrix table. The resource transfer relationships between accounts include the resource transfer relationships between the consumer account and the third battery account, the third battery account and the first recycling-related account, and the first recycling-related account and the recycled materials account.

[0089] Furthermore, the target equilibrium model includes a lithium battery full life cycle tracking module, a reverse logistics network optimization module, a recycling rate endogenous response module, a primary and recycled material substitution module, and an equilibrium subject module; The lithium battery lifecycle tracking module is used to determine the amount of batteries in use at the target stage based on the battery consumption attribute information corresponding to the battery retirement location in the multi-source basic data. The reverse logistics network optimization module is used to determine the unit logistics cost from the battery decommissioning site to the battery recycling site based on the logistics cost attribute information in the multi-source basic data, and to determine the first transportation volume matrix based on the unit logistics cost and recycling processing cost combined with the first constraint condition. The first constraint condition includes recycling rate constraint, scenario parameter feasibility constraint and recycling capacity constraint. The endogenous response module for recovery rate is used to determine the recovery rate of the target stage based on the recovery attribute information in the multi-source basic data. The virgin and recycled material substitution module is used to determine the amount of virgin material input, the amount of recycled material input, and the ratio of recycled to virgin material usage for metal in the target stage, and to determine the total supply of metal in the target stage based on the amount of virgin material input, the amount of recycled material input, and the ratio of recycled to virgin material usage. The balancing module is used to determine the second social accounting matrix table based on the called processing function, combined with the in-use battery inventory, the first transportation volume matrix, the recycling rate, the input of virgin materials, the input of recycled materials, and the set clearing conditions corresponding to the target stage, and to update the multi-source basic data based on the update function, and to generate output results based on the second social accounting matrix table and the updated multi-source basic data.

[0090] Furthermore, the battery consumption attribute information includes historical battery inventory, newly added battery quantity, and retired battery quantity; The number of retired batteries is determined based on the retirement probability of lithium batteries, which is determined by combining the service life of lithium batteries with a Weibull distribution.

[0091] Furthermore, the amount of recycled material input is determined based on the first transport volume matrix of the target stage, the metal content per unit battery, and the recovery rate in the multi-source basic data; The ratio of recycled to virgin materials used is determined based on the virgin material transfer cost, recycled material transfer cost, and technology preference coefficient in the target stage of the multi-source basic data. The amount of virgin material input is determined based on the amount of recycled material input and the ratio of recycled to virgin material usage.

[0092] Furthermore, the processing function includes a production function for the battery manufacturing industry, a production function for the battery recycling industry, a utility function, and a revenue and expenditure function; The production function of the battery manufacturing industry is based on a first value-added sub-function and a first substitution sub-function; The production function of the battery recycling industry is based on the amount of first batteries input at the decommissioning site, the amount of recycled resources input, the amount of labor input, and technical efficiency parameters.

[0093] The lithium battery cycle mechanism simulation and analysis device provided in this embodiment of the invention can execute the lithium battery cycle mechanism simulation and analysis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0094] Figure 3 A schematic diagram of an electronic device 30 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0095] like Figure 3 As shown, the electronic device 30 includes at least one processor 31 and a memory, such as a read-only memory (ROM) 32 or a random access memory (RAM) 33, communicatively connected to the at least one processor 31. The memory stores computer programs executable by the at least one processor. The processor 31 can perform various appropriate actions and processes based on the computer program stored in the ROM 32 or loaded from storage unit 38 into the RAM 33. The RAM 33 can also store various programs and data required for the operation of the electronic device 30. The processor 31, ROM 32, and RAM 33 are interconnected via a bus 34. An input / output (I / O) interface 35 is also connected to the bus 34.

[0096] Multiple components in electronic device 30 are connected to I / O interface 35, including: input unit 36, such as keyboard, mouse, etc.; output unit 37, such as various types of monitors, speakers, etc.; storage unit 38, such as disk, optical disk, etc.; and communication unit 39, such as network card, modem, wireless transceiver, etc. Communication unit 39 allows electronic device 30 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0097] Processor 31 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 31 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 31 performs the various methods and processes described above, such as the lithium battery cycle mechanism simulation analysis method.

[0098] In some embodiments, the lithium battery cycling mechanism simulation analysis method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 38. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 30 via ROM 32 and / or communication unit 39. When the computer program is loaded into RAM 33 and executed by processor 31, one or more steps of the lithium battery cycling mechanism simulation analysis method described above can be performed. Alternatively, in other embodiments, processor 31 can be configured to perform the lithium battery cycling mechanism simulation analysis method by any other suitable means (e.g., by means of firmware).

[0099] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0101] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0103] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0104] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0105] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0106] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for simulating and analyzing the cycling mechanism of lithium batteries, characterized in that, include: Obtain multi-source basic data and a first social accounting matrix table corresponding to the lithium battery. The multi-source basic data includes scenario parameters set under a preset scenario. The first social accounting matrix table is obtained by expanding the benchmark social accounting matrix table during the cycle life of the lithium battery. The multi-source basic data and the first social accounting matrix table are input into the pre-constructed target equilibrium model. The target equilibrium model provides data processing for the lithium battery under the preset scenario, and obtains the output results corresponding to the target stage under the preset scenario. The output results include the second social accounting matrix table. The effect analysis result of the lithium battery under the preset scenario is determined based on the output result.

2. The method according to claim 1, characterized in that, The first social accounting matrix table is obtained by expanding the baseline social accounting matrix table, including: The first battery account in the baseline social accounting matrix is ​​split into the second battery account and the third battery account. The first metal account is split into the second metal account and the third metal account according to the source. The first recycling-related account and the first resource-related account are added to obtain the expanded initial social accounting matrix. The initial social accounting matrix table is filled with the resource transfer relationships between accounts to generate the first social accounting matrix table. The resource transfer relationships between accounts include the resource transfer relationships between the consumer account and the third battery account, the third battery account and the first recycling-related account, and the first recycling-related account and the recycled materials account.

3. The method according to claim 1, characterized in that, The target equilibrium model includes a lithium battery life cycle tracking module, a reverse logistics network optimization module, a recycling rate endogenous response module, a primary and recycled material substitution module, and an equilibrium subject module. The lithium battery lifecycle tracking module is used to determine the amount of batteries in use at the target stage based on the battery consumption attribute information corresponding to the battery retirement location in the multi-source basic data. The reverse logistics network optimization module is used to determine the unit logistics cost from the battery decommissioning site to the battery recycling site based on the logistics cost attribute information in the multi-source basic data, and to determine the first transportation volume matrix based on the unit logistics cost and recycling processing cost combined with the first constraint condition. The first constraint condition includes recycling rate constraint, scenario parameter feasibility constraint and recycling capacity constraint. The endogenous response module for recovery rate is used to determine the recovery rate of the target stage based on the recovery attribute information in the multi-source basic data. The virgin and recycled material substitution module is used to determine the amount of virgin material input, the amount of recycled material input, and the ratio of recycled to virgin material usage for metal in the target stage, and to determine the total supply of metal in the target stage based on the amount of virgin material input, the amount of recycled material input, and the ratio of recycled to virgin material usage. The balancing module is used to determine the second social accounting matrix table based on the called processing function, combined with the in-use battery inventory, the first transportation volume matrix, the recycling rate, the input of virgin materials, the input of recycled materials, and the set clearing conditions corresponding to the target stage, and to update the multi-source basic data based on the update function, and to generate output results based on the second social accounting matrix table and the updated multi-source basic data.

4. The method according to claim 3, characterized in that, The battery consumption attribute information includes historical battery inventory, newly added battery quantity, and retired battery quantity; The number of retired batteries is determined based on the retirement probability of lithium batteries, which is determined by combining the service life of lithium batteries with a Weibull distribution.

5. The method according to claim 3, characterized in that, The amount of recycled material input is determined based on the second transport volume matrix of the target stage, the metal content per unit battery, and the recovery rate in the multi-source basic data. The ratio of recycled to virgin materials used is determined based on the virgin material transfer cost, recycled material transfer cost, and technology preference coefficient in the target stage of the multi-source basic data. The amount of virgin material input is determined based on the amount of recycled material input and the ratio of recycled to virgin material usage.

6. The method according to claim 3, characterized in that, The processing functions include production functions for the battery manufacturing industry, production functions for the battery recycling industry, utility functions, and income and expenditure functions. The production function of the battery manufacturing industry is based on a first value-added sub-function and a first substitution sub-function; The production function of the battery recycling industry is based on the amount of first batteries input at the decommissioning site, the amount of recycled resources input, the amount of labor input, and technical efficiency parameters.

7. A lithium battery cycle mechanism simulation and analysis device, characterized in that, include: The acquisition module is used to acquire multi-source basic data corresponding to the lithium battery and a first social accounting matrix table. The multi-source basic data includes scenario parameters set under a preset scenario. The first social accounting matrix table is obtained by expanding the benchmark social accounting matrix table during the cycle life of the lithium battery. The first result determination module is used to input the multi-source basic data and the first social accounting matrix table into the pre-constructed target equilibrium model, and provide data processing of the lithium battery under the preset scenario through the target equilibrium model to obtain the output result corresponding to the target stage under the preset scenario. The output result includes the second social accounting matrix table. The second result determination module is used to determine the effect analysis result of the lithium battery under the preset scenario based on the output result.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the lithium battery cycle mechanism simulation analysis method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the lithium battery cycle mechanism simulation analysis method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the lithium battery cycle mechanism simulation and analysis method according to any one of claims 1-6.

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