Power battery echelon utilization optimization method and system and storage medium

Abstract

The invention discloses a power battery echelon utilization optimization method and system and a storage medium. The method comprises the steps that a carbon emission reduction accounting model of power battery echelon utilization is established through processing carbon emission data and production carbon emission data; according to the cost data and the income data of the power battery echelon utilization, establishing a net income accounting model of the power battery echelon utilization; constructing a comprehensive benefit evaluation model through the carbon emission reduction accounting model and the net income accounting model; solving the comprehensive benefit evaluation model by using a multi-target particle swarm algorithm to obtain a Pareto solution set by taking an evaluation value output by maximizing the comprehensive benefit evaluation model as a target; determining an optimization strategy of battery echelon utilization according to the Pareto solution set; and the recovery main body is assisted to plan a specific path of power battery echelon utilization, so that the balance of direct benefits of power battery echelon utilization and greenhouse gas emission reduction is realized.

Description

Technical Field

[0001] The present invention belongs to the field of battery recycling, and specifically relates to a method, system and storage medium for optimizing the recycling of power batteries. Background Art

[0002] With the development of the global new energy vehicle industry, the production capacity of power batteries has increased rapidly. Currently, some power batteries in commercial operation have entered the retirement stage. Depending on their performance, these retired power batteries can be recycled or reused. Although retired power batteries may not meet the performance requirements of battery vehicles, after testing, screening, and reassembly, they can be downgraded to other uses such as energy storage, low-speed power, and backup power. The reuse of power batteries extends their service life and indirectly protects the environment by reducing greenhouse gas emissions by reducing the production of new batteries.

[0003] The relationship between battery recycling costs and greenhouse gas emissions reductions is complex. Selecting higher-grade retired power batteries increases battery recycling costs, but displaces more greenhouse gas emissions during the reuse process. Conversely, selecting lower-grade retired power batteries reduces recycling costs, but also reduces the total greenhouse gas emissions displaced during the battery reuse process. Existing technologies cannot achieve a balance between the direct benefits of power battery recycling and greenhouse gas emissions reductions. Summary of the Invention

[0004] The present invention provides a method, system and storage medium for optimizing the cascade utilization of power batteries, which assist recycling entities in planning specific paths for the cascade utilization of power batteries, so as to achieve a balance between the direct benefits of the cascade utilization of power batteries and the reduction of greenhouse gas emissions.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A first aspect of the present invention provides a method for optimizing the cascade utilization of a power battery, comprising:

[0007] Obtaining processing carbon emission data from the power battery recycling process and production carbon emission data from the replacement battery production process, where the replacement battery has the same performance parameters as the power battery;

[0008] Establish a carbon emission reduction accounting model for the cascade utilization of power batteries by processing carbon emission data and production carbon emission data;

[0009] Based on the cost and benefit data of power battery recycling, a net benefit calculation model for power battery recycling is established;

[0010] A comprehensive benefit evaluation model is constructed through a carbon emission reduction accounting model and a net income accounting model; with the goal of maximizing the evaluation value output by the comprehensive benefit evaluation model, a multi-objective particle swarm algorithm is used to solve the comprehensive benefit evaluation model to obtain a Pareto solution set; and an optimization strategy for battery recycling is determined based on the Pareto solution set.

[0011] Furthermore, the carbon emission data of the power battery recycling process is obtained. The specific process includes:

[0012] Obtain processing energy consumption data and greenhouse gas release data during the cascade utilization of power batteries;

[0013] The processing energy consumption data during the power battery recycling process includes battery recycling detection energy consumption data and battery reassembly energy consumption data. The processing carbon emission data is calculated based on the battery recycling detection energy consumption data, the battery reassembly energy consumption data and the greenhouse gas processing release data. The expression formula is:

[0014]

[0015] In the formula, To process carbon emission data; is the carbon emission factor of fossil fuels; is the carbon emission factor of electricity consumption; Detecting fossil fuel depletion in energy consumption data for battery recycling; Detecting power loss in energy consumption data for battery recycling; The amount of fossil fuel lost to reorganize energy consumption data for batteries; The amount of power loss to reconstruct energy consumption data for the battery; For battery recycling testing process; For the battery reassembly process; is the amount of greenhouse gas released during the recycling of power batteries, where k is the type number of greenhouse gas released during the recycling of power batteries; is the conversion factor of the kth greenhouse gas relative to carbon dioxide; is the global warming potential of the kth greenhouse gas.

[0016] Processing carbon emissions data is calculated based on battery recycling and testing energy consumption data, battery reorganization energy consumption data and greenhouse gas processing release data. The battery recycling and testing energy consumption data and battery reorganization energy consumption data both cover fossil fuel loss and electricity loss, which can more accurately calculate processing carbon emissions and enhance the scientific nature and reliability of the entire decision-making process.

[0017] Furthermore, the carbon emission data of the production process of alternative batteries is obtained. The specific process includes:

[0018] Obtaining production energy consumption data and greenhouse gas emission data during the production of alternative batteries, including production energy consumption data, battery assembly energy consumption data, and battery factory inspection energy consumption data;

[0019] The production carbon emission data is calculated based on the production energy consumption data, battery assembly energy consumption data, battery factory inspection energy consumption data and greenhouse gas production release data. The expression formula is:

[0020]

[0021] In the formula, To produce carbon emission data; The amount of fossil fuel consumed in the production energy consumption data; The power loss in the production energy consumption data; is the carbon emission factor of fossil fuels; is the carbon emission factor of electricity consumption; It is the battery production process; Assemble the fossil fuel consumption data for batteries; is the power loss in the battery assembly energy consumption data; r is the battery assembly process; Detect the amount of fossil fuel loss in the energy consumption data of batteries before they leave the factory; Detect the power loss in the energy consumption data of the battery before leaving the factory; This is the battery factory inspection process; For the Conversion factors of greenhouse gases relative to carbon dioxide, The serial number of the type of greenhouse gas released during the production process of the replacement battery; To replace the first Amount of greenhouse gas emissions from processing; is the global warming potential of the hth greenhouse gas.

[0022] The production energy consumption data, battery assembly energy consumption data, and battery factory inspection energy consumption data all cover fossil fuel loss and electricity loss, and are then superimposed with greenhouse gas production and release data, enabling the present invention to accurately calculate the production carbon emission data of the alternative battery production process, which helps to compare the carbon emission differences between cascade utilization batteries and alternative batteries.

[0023] Furthermore, by processing carbon emission data and production carbon emission data, a carbon emission reduction accounting model for the cascade utilization of power batteries is established, and the expression formula is:

[0024]

[0025] In the formula, To produce carbon emission data, To process carbon emission data, Carbon emission reduction from the secondary utilization of power batteries;

[0026] The carbon emission reduction accounting model can accurately quantify the amount of carbon emissions reduced by the recycling of power batteries compared to traditional new battery production, thereby more accurately evaluating the environmental benefits of recycling.

[0027] Furthermore, based on the cost and benefit data of power battery recycling, a net benefit accounting model for power battery recycling is established, specifically including:

[0028] The cost data of power battery recycling includes energy cost, material cost, labor cost and equipment cost. The power battery recycling cost is calculated based on energy cost, material cost, labor cost and equipment cost. The expression formula is:

[0029] ;

[0030]

[0031] In the formula, Recover the cost of power batteries; Recover the purchase cost of power batteries; Power battery capacitors for cascade utilization; The remaining life of the power battery for cascade utilization; is the cost discount weight of the power battery capacitor; The cost discount weight for the remaining life of the power battery; is the energy cost of the battery recycling and testing process in step i; is the material cost in the battery recycling and testing process in step i; is the labor cost of the battery recycling and testing process in step i; is the equipment cost in the battery recycling and testing process in step i; For battery recycling testing process; is the energy cost in the j-th battery reassembly process; is the material cost in the j-th battery reassembly process; is the labor cost in the j-th battery reassembly process; is the equipment cost in the j-th battery reassembly process; For the battery reassembly process;

[0032] The revenue data of the power battery cascade utilization includes the revenue of the power battery cascade utilization project; a net revenue accounting model for the power battery cascade utilization is established based on the revenue of the power battery cascade utilization project and the power battery recycling cost, and the expression formula is:

[0033]

[0034] In the formula, The net income from the recycling of power batteries; is the income of the d-th power battery cascade utilization project; d is the serial number of the power battery cascade utilization project.

[0035] The net benefit accounting model for power battery recycling established in this paper comprehensively considers energy, material, labor, and equipment costs to calculate recycling costs, and incorporates the benefits of recycling projects. This net benefit accounting model enables companies to accurately assess economic benefits, optimize cost management, and improve resource utilization efficiency. It also helps companies formulate reasonable pricing strategies by quantifying recycling costs and project benefits.

[0036] Furthermore, a comprehensive benefit evaluation model is constructed through the carbon emission reduction accounting model and the net benefit accounting model, specifically including:

[0037]

[0038]

[0039]

[0040] In the formula, is the comprehensive benefit evaluation value, The net income from the recycling of power batteries; Carbon emission reduction from the secondary utilization of power batteries; and To set weights; Power battery capacitors for cascade utilization; The remaining life of the power battery for cascade utilization; and The maximum and minimum capacitance thresholds for cascade utilization of power batteries; and It is the maximum remaining life threshold and minimum remaining life threshold of the cascade utilization power battery.

[0041] This paper establishes a comprehensive benefit evaluation model aimed at maximizing the comprehensive benefit assessment value and incorporates power battery capacitance and lifespan constraints to ensure the feasibility and sustainability of the optimization solution. This model effectively balances environmental and economic benefits, helping decision makers develop optimal secondary utilization strategies. By using capacitance and lifespan constraints, the model also ensures the performance and safety of batteries during secondary utilization, extending their service life and improving resource utilization.

[0042] Furthermore, with the goal of maximizing the evaluation value output by the comprehensive benefit evaluation model, a multi-objective particle swarm algorithm is used to solve the comprehensive benefit evaluation model to obtain a Pareto solution set, specifically including:

[0043] Random Generation Initialize the position, velocity, inertia weight and maximum number of iterations of each particle to obtain the particle population. ; The particle individual As the comprehensive benefit evaluation value corresponding to the battery cascade utilization optimization strategy;

[0044] During the particle population update iteration process, the inertia weight of the individual particle is updated according to the number of particle population iterations. The expression formula is:

[0045]

[0046] In the formula, is the maximum threshold of inertia weight; is the minimum threshold of inertia weight; is the maximum number of iterations; For the The inertia weight of the individual particle in the iteration;

[0047] The goal is to maximize the evaluation value output by the comprehensive benefit evaluation model, according to the comprehensive benefit evaluation value and the inertia weight of the individual particles Update the position and velocity of the individual particle;

[0048] Determine whether the number of iterations has reached the maximum number of iterations. If not, continue to update the position, velocity, and inertia weight of the individual particles. If the maximum number of iterations has been reached, output the updated particle population as the Pareto solution.

[0049] This paper uses a multi-objective particle swarm algorithm to solve a comprehensive benefit evaluation model, dynamically updating particle inertia weights and flexibly adjusting particle search behavior to improve algorithm search efficiency and solution quality. After reaching the maximum number of iterations, it outputs a Pareto solution set, providing decision makers with multiple high-quality options and comprehensively presenting the benefit balance under different strategies.

[0050] Furthermore, according to the comprehensive benefit evaluation value and the inertia weight of the individual particles Update the position and velocity of the individual particle, expressed as:

[0051]

[0052]

[0053] In the formula, represents the number of iterations of the particle population, and is the learning factor, and is a random number between [0,1]; For the The update step vector of the sth particle during the iteration; For the The update step vector of the sth particle during the iteration; For the The individual particle position corresponding to the optimal comprehensive benefit evaluation value found in the history of the sth particle before the iteration process; For the The individual particle position corresponding to the optimal comprehensive benefit evaluation value found in the history of the entire particle population before the iteration process; For the The position of the sth particle during the iteration; For the The position of the sth particle during the iteration.

[0054] During the iterative update of the particle population, the inertia weight is dynamically adjusted to ensure that particles can achieve optimal performance in both the exploration and development phases, avoiding local optimality and improving global optimization capabilities. The comprehensive benefit evaluation value is combined with the particle position and velocity update to ensure that the optimization process always advances in the direction of improving comprehensive benefits.

[0055] A second aspect of the present invention provides a power battery recycling optimization system, comprising:

[0056] A data acquisition unit, configured to acquire processing carbon emission data from a power battery recycling process and production carbon emission data from a replacement battery production process, wherein the replacement battery has the same performance parameters as the power battery;

[0057] The model building unit establishes a carbon emission reduction accounting model for the cascade utilization of power batteries by processing carbon emission data and production carbon emission data; and establishes a net benefit accounting model for the cascade utilization of power batteries based on the cost and benefit data of the cascade utilization of power batteries;

[0058] The optimization solution unit constructs a comprehensive benefit evaluation model through the carbon emission reduction accounting model and the net benefit accounting model; with the goal of maximizing the evaluation value output by the comprehensive benefit evaluation model, the multi-objective particle swarm algorithm is used to solve the comprehensive benefit evaluation model to obtain a Pareto solution set;

[0059] The output unit determines the optimization strategy of battery recycling based on the Pareto solution set.

[0060] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for optimizing the cascade utilization of power batteries as described in the first aspect is implemented.

[0061] A fourth aspect of the present invention provides an electronic device comprising a storage medium and a processor; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the power battery cascade utilization optimization method described in the first aspect.

[0062] A fifth aspect of the present invention provides a computer program product, comprising instructions, which, when executed by a processor, enable the processor to execute the power battery cascade utilization optimization method described in the first aspect.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention constructs a comprehensive benefit evaluation model through a carbon emission reduction accounting model and a net income accounting model; solving the comprehensive benefit evaluation model with the goal of maximizing the evaluation value output by the comprehensive benefit evaluation model not only promotes the efficient recycling of power batteries, reduces the demand for new battery production and resource consumption, but also provides a sustainable environmental protection solution for the development of the new energy vehicle industry. It has significant economic and environmental dual benefits and is of great significance to promoting the green and low-carbon transformation of the entire industry.

[0065] The present invention uses a multi-objective particle swarm algorithm to solve the comprehensive benefit evaluation model to obtain the Pareto solution set, which comprehensively presents the comprehensive benefit trade-offs under different optimization strategies; determines the optimization strategy for battery cascade utilization based on the Pareto solution set; and can balance the dual goals of carbon emission reduction and net income while maximizing the comprehensive benefits, avoiding the benefit imbalance that may be caused by single-objective optimization, and helping to formulate more targeted and effective cascade utilization plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of a method for optimizing the cascade utilization of power batteries provided in Example 1 of the present invention;

[0067] Figure 2 A flowchart for solving the comprehensive benefit evaluation model provided in Example 1 of the present invention;

[0068] Figure 3 This is a flowchart of an electronic device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0069] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0070] Example 1

[0071] like Figure 1 As shown, this embodiment provides a method for optimizing the cascade utilization of power batteries, including:

[0072] Obtain processing energy consumption data and greenhouse gas release data during the cascade utilization of power batteries;

[0073] The processing energy consumption data during the power battery recycling process includes battery recycling detection energy consumption data and battery reassembly energy consumption data. The processing carbon emission data is calculated based on the battery recycling detection energy consumption data, the battery reassembly energy consumption data and the greenhouse gas processing release data. The expression formula is:

[0074]

[0075] In the formula, To process carbon emission data; is the carbon emission factor of fossil fuels; is the carbon emission factor of electricity consumption; Detecting fossil fuel depletion in energy consumption data for battery recycling; Detecting power loss in energy consumption data for battery recycling; The amount of fossil fuel lost to reorganize energy consumption data for batteries; The amount of power loss to reconstruct energy consumption data for the battery; For battery recycling testing process; For the battery reassembly process; is the amount of greenhouse gas released during the recycling of power batteries, where k is the type number of greenhouse gas released during the recycling of power batteries; is the conversion factor of the kth greenhouse gas relative to carbon dioxide; is the global warming potential of the kth greenhouse gas.

[0076] The performance parameters of replacement batteries and second-life power batteries are the same; obtain production carbon emission data of the replacement battery production process, the specific process includes:

[0077] Obtaining production energy consumption data and greenhouse gas emission data during the production of alternative batteries, including production energy consumption data, battery assembly energy consumption data, and battery factory inspection energy consumption data;

[0078] The production carbon emission data is calculated based on the production energy consumption data, battery assembly energy consumption data, battery factory inspection energy consumption data and greenhouse gas production release data. The expression formula is:

[0079]

[0080] In the formula, To produce carbon emission data; The amount of fossil fuel consumed in the production energy consumption data; The power loss in the production energy consumption data; is the carbon emission factor of fossil fuels; is the carbon emission factor of electricity consumption; It is the battery production process; Assemble the fossil fuel consumption data for batteries; is the power loss in the battery assembly energy consumption data; r is the battery assembly process; Detect the amount of fossil fuel loss in the energy consumption data of batteries before they leave the factory; Detect the power loss in the energy consumption data of the battery before leaving the factory; This is the battery factory inspection process; For the Conversion factors of greenhouse gases relative to carbon dioxide, The serial number of the type of greenhouse gas released during the production process of the replacement battery; To replace the first Amount of greenhouse gas emissions from processing; is the global warming potential of the hth greenhouse gas.

[0081] By processing carbon emission data and production carbon emission data, a carbon emission reduction accounting model for the cascade utilization of power batteries is established. The expression formula is:

[0082]

[0083] In the formula, To produce carbon emission data, To process carbon emission data, Carbon emission reduction from the recycling of power batteries.

[0084] It should be noted that the carbon emission data of the production process of second-use batteries and replacement batteries will fluctuate over time. For example, the carbon emissions of the dehumidification system of a power battery production base are related to the external environment. The carbon emissions of the dehumidification system in summer will be higher than those in winter. The carbon emission data can be updated regularly or before accounting based on feedback from the production base.

[0085] Based on the cost and benefit data of power battery recycling, a net benefit accounting model for power battery recycling is established, specifically including:

[0086] The cost data of power battery recycling includes energy cost, material cost, labor cost and equipment cost. The power battery recycling cost is calculated based on energy cost, material cost, labor cost and equipment cost. The expression formula is:

[0087] ;

[0088]

[0089] In the formula, Recover the cost of power batteries; Recover the purchase cost of power batteries; Power battery capacitors for cascade utilization; The remaining life of the power battery for cascade utilization; is the cost discount weight of the power battery capacitor; The cost discount weight for the remaining life of the power battery; is the energy cost of the battery recycling and testing process in step i; is the material cost in the battery recycling and testing process in step i; is the labor cost of the battery recycling and testing process in step i; is the equipment cost in the battery recycling and testing process in step i; For battery recycling testing process; is the energy cost in the j-th battery reassembly process; is the material cost in the j-th battery reassembly process; is the labor cost in the j-th battery reassembly process; is the equipment cost in the j-th battery reassembly process; For the battery reassembly process;

[0090] The revenue data of the power battery cascade utilization includes the revenue of the power battery cascade utilization project; a net revenue accounting model for the power battery cascade utilization is established based on the revenue of the power battery cascade utilization project and the power battery recycling cost, and the expression formula is:

[0091]

[0092] In the formula, The net income from the recycling of power batteries; is the income of the d-th power battery cascade utilization project; d is the serial number of the power battery cascade utilization project.

[0093] It should be noted that the cost and benefit data of second-hand batteries and replacement batteries will fluctuate over time. For example, the recycling price of second-hand batteries will be affected by market supply and demand. The cost and benefit data can be updated regularly or before accounting based on feedback from the recycling, production and sales ends.

[0094] A comprehensive benefit evaluation model is constructed through the carbon emission reduction accounting model and the net benefit accounting model, specifically including:

[0095] The carbon emission reduction amount of the power battery recycling is calculated through the carbon emission reduction accounting model; the net income of the power battery recycling is calculated through the net income accounting model;

[0096] The comprehensive benefit evaluation value is calculated based on the carbon emission reduction and net income of the power battery cascade utilization, and a comprehensive benefit evaluation model is established. The expression formula is:

[0097]

[0098]

[0099]

[0100] In the formula, is the comprehensive benefit evaluation value, The net income from the recycling of power batteries; Carbon emission reduction from the secondary utilization of power batteries; and To set weights; Power battery capacitors for cascade utilization; The remaining life of the power battery for cascade utilization; and The maximum and minimum capacitance thresholds for cascade utilization of power batteries; and It is the maximum remaining life threshold and minimum remaining life threshold of the cascade utilization power battery.

[0101] In this embodiment, the optimization strategy for the cascade utilization of power batteries is determined by designers based on market research and corporate development needs. The optimization strategy includes optimization objectives and constraints. The optimization objectives can be maximizing the carbon emission reduction of the cascade utilization of power batteries and maximizing the net benefit of the cascade utilization of power batteries. The constraints can also include constraints on the reorganization cost of the cascade utilization batteries, constraints on the carbon emissions of key components, and constraints on the distance between the recycling end and the production end, etc., which ensures the feasibility and safety of the optimization plan, extends the service life of the battery, and improves the utilization efficiency of resources.

[0102] like Figure 2 As shown in the figure, with the goal of maximizing the comprehensive benefit evaluation value, the multi-objective particle swarm algorithm is used to solve the comprehensive benefit evaluation model to obtain the Pareto solution set, which specifically includes:

[0103] Random Generation Initialize the position, velocity, inertia weight and maximum number of iterations of each particle to obtain the particle population. ; The particle individual As a strategy for optimizing battery second-life utilization;

[0104] During the particle population update iteration process, the inertia weight of the individual particle is updated according to the number of particle population iterations. The expression formula is:

[0105]

[0106] In the formula, is the maximum threshold of inertia weight; is the minimum threshold of inertia weight; is the maximum number of iterations; For the The inertia weight of the individual particle in the iteration; in this embodiment, the inertia weight is numerically adaptively changed with the iteration, and a larger inertia weight value is maintained at the beginning of the iteration. The inertia weight gradually decreases with the increase in the number of iterations, thereby enhancing the local search capability of the algorithm in the later stage.

[0107] The goal is to maximize the evaluation value output by the comprehensive benefit evaluation model, according to the comprehensive benefit evaluation value and the inertia weight of the individual particles Update the position and velocity of the individual particle, expressed as:

[0108]

[0109]

[0110] In the formula, represents the number of iterations of the particle population, and is the learning factor, and is a random number between [0,1]; For the The update step vector of the sth particle during the iteration; For the The update step vector of the sth particle during the iteration; For the The individual particle position corresponding to the optimal comprehensive benefit evaluation value found in the history of the sth particle before the iteration process; For the The individual particle position corresponding to the optimal comprehensive benefit evaluation value found in the history of the entire particle population before the iteration process; For the The position of the sth particle during the iteration; For the The position of the sth particle during the iteration;

[0111] Determine whether the number of iterations has reached the maximum number of iterations. If not, continue to update the position, velocity, and inertia weight of the individual particles. If the maximum number of iterations has been reached, output the updated particle population as the Pareto solution set. Determine the optimization strategy for battery recycling based on the Pareto solution set.

[0112] This embodiment comprehensively considers the environmental and economic benefits of the cascade utilization of power batteries by establishing a carbon emission reduction accounting model and a net income accounting model, and can accurately quantify the comprehensive benefit evaluation value of cascade utilization. The introduction of a multi-objective particle swarm algorithm dynamically adjusts the inertia weight of particles, effectively balancing the global and local search capabilities, improving the algorithm's convergence speed and the quality of the solution, and avoiding falling into local optimal solutions. By iteratively updating the position and speed of particles, the final output Pareto solution set provides decision makers with a variety of optimization strategy options, helping them to balance different benefit objectives, and helping to achieve a win-win situation for the environmental and economic benefits of the cascade utilization of power batteries.

[0113] Example 2

[0114] This embodiment provides a power battery cascade utilization optimization system, which is used to execute the power battery cascade utilization optimization method described in Example 1. The power battery cascade utilization optimization system includes:

[0115] A data acquisition unit, used to acquire processing carbon emission data from the power battery recycling process and production carbon emission data from the replacement battery production process, where the replacement battery has the same performance parameters as the recycled power battery;

[0116] The model building unit establishes a carbon emission reduction accounting model for the cascade utilization of power batteries by processing carbon emission data and production carbon emission data; and establishes a net benefit accounting model for the cascade utilization of power batteries based on the cost and benefit data of the cascade utilization of power batteries;

[0117] The optimization solution unit constructs a comprehensive benefit evaluation model through the carbon emission reduction accounting model and the net benefit accounting model; with the goal of maximizing the evaluation value output by the comprehensive benefit evaluation model, the multi-objective particle swarm algorithm is used to solve the comprehensive benefit evaluation model to obtain a Pareto solution set;

[0118] The output unit determines the optimization strategy of battery recycling based on the Pareto solution set.

[0119] The model building unit builds a comprehensive benefit evaluation model through the carbon emission reduction accounting model and the net benefit accounting model; specifically, it includes:

[0120] The carbon emission reduction amount of the power battery recycling is calculated through the carbon emission reduction accounting model; the net income of the power battery recycling is calculated through the net income accounting model;

[0121] The comprehensive benefit evaluation value is calculated based on the carbon emission reduction and net income of the power battery cascade utilization, and a comprehensive benefit evaluation model is established. The expression formula is:

[0122]

[0123]

[0124]

[0125] In the formula, is the comprehensive benefit evaluation value, The net income from the recycling of power batteries; Carbon emission reduction from the secondary utilization of power batteries; and To set weights; Power battery capacitors for cascade utilization; The remaining life of the power battery for cascade utilization; and The maximum and minimum capacitance thresholds for cascade utilization of power batteries; and It is the maximum remaining life threshold and minimum remaining life threshold of the cascade utilization power battery.

[0126] The optimization solution unit uses a multi-objective particle swarm algorithm to solve the comprehensive benefit evaluation model to obtain a Pareto solution set, specifically including:

[0127] Random Generation Initialize the position, velocity, inertia weight and maximum number of iterations of each particle to obtain the particle population. ; The particle individual As a strategy for optimizing battery second-life utilization;

[0128] During the particle population update iteration process, the inertia weight of the individual particle is updated according to the number of particle population iterations. The expression formula is:

[0129]

[0130] In the formula, is the maximum threshold of inertia weight; is the minimum threshold of inertia weight; is the maximum number of iterations; For the The inertia weight of the individual particle in the iteration;

[0131] According to the comprehensive benefit evaluation value and inertia weight Update the position and velocity of the individual particle, expressed as:

[0132]

[0133]

[0134] In the formula, represents the number of iterations of the particle population, and is the learning factor, and is a random number between [0,1]; For the The update step vector of the sth particle during the iteration; For the The update step vector of the sth particle during the iteration; For the The individual particle position corresponding to the optimal comprehensive benefit evaluation value found in the history of the sth particle before the iteration process; For the The individual particle position corresponding to the optimal comprehensive benefit evaluation value found in the history of the entire particle population before the iteration process; For the The position of the sth particle during the iteration; For the The position of the sth particle during the iteration;

[0135] Determine whether the number of iterations has reached the maximum number of iterations. If not, continue to update the position, velocity, and inertia weight of the individual particles. If the maximum number of iterations has been reached, output the updated particle population as the Pareto solution.

[0136] Example 3

[0137] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for optimizing the cascade utilization of power batteries described in Example 1 is implemented.

[0138] Example 4

[0139] like Figure 3 As shown, this embodiment provides an electronic device, including a storage medium and a processor; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the power battery cascade utilization optimization method described in Example 1.

[0140] Memory can be any electronic, magnetic, optical, or other physical storage device used to store computer programs, information, and data. Memory can be random access memory (RAM), flash memory, a storage disk, or other similar storage media, or a combination of these. This system network element is connected to at least one other network element, including the Internet, a wide area network, or a local area network, via at least one communication interface (wired or wireless).

[0141] The memory and processor are connected via a bus; the bus may be an ISA bus, an EISA bus, a VESA bus, or a PCI bus, and the bus may be divided into a data bus, an address bus, a control bus, an expansion bus, etc. The memory is used to store computer programs, and the processor executes the programs after receiving execution instructions.

[0142] A processor is an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor, etc., but can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The method disclosed in this embodiment can be directly implemented and executed by a hardware compilation processor, or by a combination of hardware and software modules of a decoding processor.

[0143] Example 5

[0144] A fifth aspect of the present invention provides a computer program product, comprising instructions, which, when executed by a processor, enable the processor to execute the power battery cascade utilization optimization method described in Example 1.

[0145] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0149] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing the cascade utilization of power batteries, characterized in that: include: Obtaining processing carbon emission data from the power battery recycling process and production carbon emission data from the replacement battery production process, where the replacement battery has the same performance parameters as the power battery; Establish a carbon emission reduction accounting model for the cascade utilization of power batteries by processing carbon emission data and production carbon emission data; Based on the cost and benefit data of power battery recycling, a net benefit calculation model for power battery recycling is established; A comprehensive benefit evaluation model is constructed through a carbon emission reduction accounting model and a net income accounting model; with the goal of maximizing the evaluation value output by the comprehensive benefit evaluation model, a multi-objective particle swarm algorithm is used to solve the comprehensive benefit evaluation model to obtain a Pareto solution set; and an optimization strategy for battery recycling is determined based on the Pareto solution set.

2. The power battery cascade utilization optimization method according to claim 1, characterized in that: Obtaining carbon emission data from the recycling of power batteries. The specific process includes: Obtaining processing energy consumption data and greenhouse gas release data during the cascade utilization of power batteries; the processing energy consumption data during the cascade utilization of power batteries includes battery recycling and testing energy consumption data and battery reassembly energy consumption data; The processing carbon emission data is calculated based on the battery recycling test energy consumption data, battery reassembly energy consumption data and greenhouse gas processing release data. The expression formula is: ； In the formula, To process carbon emission data; is the carbon emission factor of fossil fuels; is the carbon emission factor of electricity consumption; Detecting fossil fuel depletion in energy consumption data for battery recycling; Detecting power loss in energy consumption data for battery recycling; The amount of fossil fuel lost to reorganize energy consumption data for batteries; The amount of power loss to reconstruct energy consumption data for the battery; For battery recycling testing process; For the battery reassembly process; is the amount of greenhouse gas released during the recycling of power batteries, where k is the type number of greenhouse gas released during the recycling of power batteries; is the conversion factor of the kth greenhouse gas relative to carbon dioxide; is the global warming potential of the kth greenhouse gas.

3. The power battery recycling optimization method according to claim 1, characterized in that: Obtain production carbon emissions data for alternative battery production processes, including: Obtaining production energy consumption data and greenhouse gas emission data during the production of alternative batteries, including production energy consumption data, battery assembly energy consumption data, and battery factory inspection energy consumption data; The production carbon emission data is calculated based on the production energy consumption data, battery assembly energy consumption data, battery factory inspection energy consumption data and greenhouse gas production release data. The expression formula is: ； In the formula, To produce carbon emission data; The amount of fossil fuel consumed in the production energy consumption data; The power loss in the production energy consumption data; is the carbon emission factor of fossil fuels; is the carbon emission factor of electricity consumption; It is the battery production process; Assemble the fossil fuel consumption data for batteries; is the power loss in the battery assembly energy consumption data; r is the battery assembly process; Detect the amount of fossil fuel loss in the energy consumption data of batteries before they leave the factory; Detect the power loss in the energy consumption data of the battery before leaving the factory; This is the battery factory inspection process; For the Conversion factors of greenhouse gases relative to carbon dioxide, The serial number of the type of greenhouse gas released during the production process of the replacement battery; To replace the first Amount of greenhouse gas emissions from processing; is the global warming potential of the hth greenhouse gas.

4. The power battery recycling optimization method according to claim 1, characterized in that: Based on the cost and benefit data of power battery recycling, a net benefit accounting model for power battery recycling is established, specifically including: The cost data of power battery recycling includes energy cost, material cost, labor cost and equipment cost. The power battery recycling cost is calculated based on energy cost, material cost, labor cost and equipment cost. The expression formula is: ； ； In the formula, Recover the cost of power batteries; Recover the purchase cost of power batteries; Power battery capacitors for cascade utilization; The remaining life of the power battery for cascade utilization; is the cost discount weight of the power battery capacitor; The cost discount weight for the remaining life of the power battery; is the energy cost of the battery recycling and testing process in step i; is the material cost in the battery recycling and testing process in step i; is the labor cost of the battery recycling and testing process in step i; is the equipment cost in the battery recycling and testing process in step i; For battery recycling testing process; is the energy cost in the j-th battery reassembly process; is the material cost in the j-th battery reassembly process; is the labor cost in the j-th battery reassembly process; is the equipment cost in the j-th battery reassembly process; For the battery reassembly process; The revenue data of the power battery cascade utilization includes the revenue of the power battery cascade utilization project; a net revenue accounting model for the power battery cascade utilization is established based on the revenue of the power battery cascade utilization project and the power battery recycling cost, and the expression formula is: ； In the formula, The net income from the recycling of power batteries; is the income of the d-th power battery cascade utilization project; d is the serial number of the power battery cascade utilization project.

5. The power battery recycling optimization method according to claim 4, characterized in that: A comprehensive benefit evaluation model is constructed through the carbon emission reduction accounting model and the net benefit accounting model, specifically including: ； ； ； ； In the formula, is the comprehensive benefit evaluation value, The net income from the recycling of power batteries; The carbon emission reduction from the secondary utilization of power batteries is To produce carbon emission data, To process carbon emission data; and To set weights; Power battery capacitors for cascade utilization; The remaining life of the power battery for cascade utilization; and The maximum and minimum capacitance thresholds for cascade utilization of power batteries; and It is the maximum remaining life threshold and minimum remaining life threshold of the cascade utilization power battery.

6. The power battery cascade utilization optimization method according to claim 5, characterized in that: With the goal of maximizing the evaluation value output by the comprehensive benefit evaluation model, the multi-objective particle swarm algorithm is used to solve the comprehensive benefit evaluation model to obtain the Pareto solution set, specifically including: Random Generation Initialize the position, velocity, inertia weight and maximum number of iterations of each particle to obtain the particle population. ; The particle individual As the comprehensive benefit evaluation value corresponding to the battery recycling strategy; During the particle population update iteration process, the inertia weight of the individual particle is updated according to the number of particle population iterations. The expression formula is: ； In the formula, is the maximum threshold of inertia weight; is the minimum threshold of inertia weight; is the maximum number of iterations; For the The inertia weight of the individual particle in the iteration; The goal is to maximize the evaluation value output by the comprehensive benefit evaluation model, according to the comprehensive benefit evaluation value and the inertia weight of the individual particles Update the position and velocity of the individual particle; Determine whether the number of iterations has reached the maximum number of iterations. If not, continue to update the position, velocity, and inertia weight of the individual particles. If the maximum number of iterations has been reached, output the updated particle population as the Pareto solution.

7. The power battery recycling optimization method according to claim 6, characterized in that: According to the comprehensive benefit evaluation value and the inertia weight of the individual particles Update the position and velocity of the individual particle, expressed as: ； ； In the formula, represents the number of iterations of the particle population, and is the learning factor, and is a random number between [0,1]; For the The update step vector of the sth particle during the iteration; For the The update step vector of the sth particle during the iteration; For the The individual particle position corresponding to the optimal comprehensive benefit evaluation value found in the history of the sth particle before the iteration process; For the The individual particle position corresponding to the optimal comprehensive benefit evaluation value found in the history of the entire particle population before the iteration process; For the The position of the sth particle during the iteration; For the The position of the sth particle during the iteration.

8. A power battery cascade utilization optimization system, characterized in that: include: A data acquisition unit, used to acquire processing carbon emission data from the power battery recycling process and production carbon emission data from the replacement battery production process, where the replacement battery has the same performance parameters as the recycled power battery; The model building unit establishes a carbon emission reduction accounting model for the cascade utilization of power batteries by processing carbon emission data and production carbon emission data; and establishes a net benefit accounting model for the cascade utilization of power batteries based on the cost and benefit data of the cascade utilization of power batteries; The optimization solution unit constructs a comprehensive benefit evaluation model through the carbon emission reduction accounting model and the net benefit accounting model; with the goal of maximizing the evaluation value output by the comprehensive benefit evaluation model, the multi-objective particle swarm algorithm is used to solve the comprehensive benefit evaluation model to obtain a Pareto solution set; The output unit determines the optimization strategy of battery recycling based on the Pareto solution set.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the power battery recycling optimization method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a storage medium and a processor; the storage medium is used to store instructions; characterized in that, The processor is used to operate according to the instructions to execute the power battery cascade utilization optimization method according to any one of claims 1 to 7.

11. A computer program product comprising instructions, characterized in that When executed by a processor, the instructions cause the processor to execute the power battery recycling optimization method according to any one of claims 1 to 7.

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