Method, device and equipment for optimizing reverse logistics network of electric vehicle battery

Abstract

The invention provides an optimization method, device and equipment for a reverse logistics network of an electric vehicle battery, and relates to the technical field of path planning. The method comprises the following steps: determining the decommissioning number of an electric vehicle battery in a target time period; the method is based on the decommissioning number of electric vehicle batteries in a target time period, the recovery price of the decommissioning batteries, the position of a to-be-built multi-target logistics node, the facility cost of building the multi-target logistics node, the battery processing operation cost, the operation cost of the multi-target logistics node, the battery transfer cost and the waste processing cost. Constructing a decommissioned battery processing cost model; constructing a carbon tax cost model or a carbon transaction cost model based on the total carbon dioxide emission, the carbon dioxide emission tax or the carbon quota; and based on a preset constraint condition, solving the carbon tax reverse logistics network model or the carbon transaction reverse logistics network model to obtain an optimal electric vehicle battery reverse logistics network. According to the method, the reverse logistics network of the electric vehicle battery can be reasonably optimized.

Description

Technical Field

[0001] This invention relates to the field of route planning technology, and in particular to an optimization method, apparatus and equipment for a reverse logistics network for electric vehicle batteries. Background Technology

[0002] Power batteries are the core component of electric vehicles, responsible for providing power. However, the lifespan of power batteries is typically 8-10 years, and they need to be retired when their capacity decays to below 80%. If retired batteries are not properly recycled, they not only lead to the loss of metal resources such as nickel and cobalt, but their heavy metals and electrolytes can also cause soil and water pollution. Therefore, building an efficient reverse logistics network for electric vehicle batteries and properly handling retired batteries has become a crucial issue for the sustainable development of the new energy vehicle industry.

[0003] Reverse logistics refers to the process of electric vehicle batteries returning from consumers to suppliers or manufacturers, involving returns, product recycling, remanufacturing, and waste disposal. While there is some research on reverse logistics networks for electric vehicle batteries, traditional optimization methods are largely based on static data, making it difficult to address the demands of a significant increase in the number of retired electric vehicle batteries. Therefore, optimizing the reverse logistics network for electric vehicle batteries has become a pressing technical problem that needs to be solved. Summary of the Invention

[0004] This invention provides an optimization method, apparatus, and equipment for a reverse logistics network for electric vehicle batteries, in order to solve the problem of inaccurate optimization results in current reverse logistics networks for electric vehicle batteries.

[0005] In a first aspect, embodiments of the present invention provide an optimization method for a reverse logistics network for electric vehicle batteries, comprising:

[0006] Based on historical sales data of electric vehicles, the sales volume of electric vehicles in the target period is predicted.

[0007] Based on the predicted sales volume of electric vehicles during the target period and the proportion of electric vehicle batteries to be retired during the target period, the number of electric vehicle batteries to be retired during the target period is determined.

[0008] Based on the number of electric vehicle batteries retired during the target period, the recycling price of retired batteries, the location of the proposed multi-target logistics node, the facility cost of building the multi-target logistics node, the battery processing cost, the operating cost of the multi-target logistics node, the battery transfer cost, and the waste disposal cost, a retired battery processing cost model is constructed.

[0009] Based on the carbon emissions generated from the construction of multi-target logistics nodes, the carbon emissions generated during the operation of each target logistics node, and the carbon emissions generated during transportation, the total carbon dioxide emissions are determined; based on the total carbon dioxide emissions and carbon emission taxes or carbon quotas, a carbon tax cost model or a carbon trading cost model is constructed.

[0010] Based on the retired battery processing cost model, carbon tax cost model, or carbon trading cost model, construct a carbon tax reverse logistics network model or a carbon trading reverse logistics network model.

[0011] Based on preset constraints, the carbon tax reverse logistics network model or the carbon trading reverse logistics network model is solved to obtain the optimal electric vehicle battery reverse logistics network.

[0012] Secondly, embodiments of the present invention provide an optimization device for a reverse logistics network for electric vehicle batteries, comprising:

[0013] The sales forecasting module is used to predict the sales volume of trolley vehicles within a target period based on historical sales data.

[0014] The retirement prediction module is used to determine the number of electric vehicle batteries to be retired during the target period based on the predicted sales volume of electric vehicles and the proportion of electric vehicle batteries to be retired during the target period.

[0015] The retirement cost module is used to construct a retirement battery processing cost model based on the number of electric vehicle batteries retired within a target period, the recycling price of retired batteries, the location of the proposed multi-target logistics nodes, the facility cost of building the multi-target logistics nodes, the battery processing operation cost, the operating cost of the multi-target logistics nodes, the battery transfer cost, and the waste disposal cost.

[0016] The carbon emission module is used to determine the total carbon dioxide emissions based on the carbon emissions generated from the construction of multi-target logistics nodes, the carbon emissions generated during the operation of each target logistics node, and the carbon emissions generated during transportation; and to construct a carbon tax cost model or a carbon trading cost model based on the total carbon dioxide emissions and carbon emission taxes or carbon quotas.

[0017] The model building module is used to construct a carbon tax reverse logistics network model or a carbon trading reverse logistics network model based on the retired battery processing cost model, carbon tax cost model, or carbon trading cost model.

[0018] The solution module is used to solve the carbon tax reverse logistics network model or the carbon trading reverse logistics network model based on preset constraints, so as to obtain the optimal electric vehicle battery reverse logistics network.

[0019] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0020] In this embodiment of the invention, to reduce the cost and carbon emissions of the reverse logistics network and decrease energy consumption during transportation, the following steps are taken: First, the sales volume of electric vehicles during the target period is predicted using historical sales data. Then, the number of electric vehicle batteries to be retired during the target period is determined based on the predicted sales volume and the proportion of retired electric vehicle batteries during that period. Next, a retired battery processing cost model is constructed based on the location of the proposed multi-target logistics nodes, the facility cost of constructing these nodes, the battery processing cost, the operating cost of the multi-target logistics nodes, the battery transfer cost, and the waste disposal cost. The total carbon dioxide emissions are determined based on the carbon emissions generated during construction, operation, and transportation. Finally, based on the pre-defined constraints, the constructed carbon tax reverse logistics network model or carbon trading reverse logistics network model is solved to obtain the optimal electric vehicle battery reverse logistics network. By predicting electric vehicle sales during the target period and determining the number of retired electric vehicle batteries during that period, an accurate data foundation is provided for the subsequent rational planning of the reverse logistics network. To rationally select a reverse logistics network, this invention constructs a retired battery processing cost model based on economic costs, a carbon tax cost model and a carbon trading cost model based on carbon tax and carbon emission trading, and finally constructs a carbon tax reverse logistics network model that comprehensively considers economic costs and carbon tax, and a carbon trading reverse logistics network model that comprehensively considers economic costs and carbon emission trading. This achieves efficient recycling of electric vehicle batteries, reduces the cost of the reverse logistics network, and increases the economic benefits of recycling. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the implementation of the optimization method for the reverse logistics network of electric vehicle batteries provided in this embodiment of the invention.

[0022] Figure 2 This is a block diagram of an optimization method for the reverse logistics network of electric vehicle batteries provided in an embodiment of the present invention;

[0023] Figure 3 This is a fitted curve of the predicted sales figures for electric vehicles from 2025 to 2027 provided in this embodiment of the invention;

[0024] Figure 4 This is a schematic diagram of the reverse logistics network optimization results under the carbon tax background provided in the embodiments of the present invention;

[0025] Figure 5This is a schematic diagram illustrating the optimization results of the reverse logistics network for electric vehicle batteries under the comprehensive consideration of carbon tax and carbon trading market systems provided in this embodiment of the invention.

[0026] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0027] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0028] As introduced in the background section, the power battery is the core component of an electric vehicle, responsible for providing power. The lifespan of a power battery is typically 8-10 years, and it needs to be retired when its capacity decays to below 80%. With the continuous development of the electric vehicle industry, the number of retired electric vehicle batteries will continue to increase, thus highlighting the importance of electric vehicle battery recycling. Optimizing the reverse logistics network for electric vehicle batteries and providing theoretical support for its low-carbon nature is a pressing technical problem that needs to be solved.

[0029] Reverse logistics refers to the logistics process from consumers back to suppliers or manufacturers, involving processes such as returns, product recycling, remanufacturing, and waste disposal.

[0030] See Figure 1 and Figure 2 The document illustrates a flowchart of the implementation of an optimization method for the reverse logistics network of electric vehicle batteries provided in an embodiment of the present invention, detailed below:

[0031] S110. Based on historical sales data of electric vehicles, predict the sales volume of electric vehicles within the target period.

[0032] The target time period can be in the form of a year or several years, or in the form of a month or several months; there is no limitation here.

[0033] Since historical sales data for trolleybuses is limited, the GM(1,1) model in the grey prediction model is used to predict trolleybus sales.

[0034] Assume the historical sales data of electric vehicles is x. (0) =(x 0 (1),x 0 (2),x 0 (3),…x 0 (n)) By using the GM(1,1) model in the grey prediction model to predict the sales volume of trolleybuses, the predicted sales volume of trolleybuses within the target period is obtained as follows: Since the prediction process of the GM(1,1) model in the grey prediction model is a conventional process, it will not be elaborated here. 0(n) represents the historical sales data of trams in the nth time period.

[0035] In some embodiments, after obtaining the predicted sales volume of trolleybuses within the target time period, in order to ensure the accuracy of the prediction and to ensure that subsequent processing is not affected by the prediction results, it is also necessary to verify the validity of the predicted sales volume of trolleybuses. Three verification methods—residual test, posterior error test, and small error probability test—can be used simultaneously to verify the predicted sales volume of trolleybuses.

[0036] In this embodiment, the residual test process is as follows:

[0037] The historical sales data for electric vehicles is x (0) =(x 0 (1),x 0 (2),x 0 (3),…x 0 (n)),

[0038] The predicted sales figures for electric vehicles during the target period are as follows:

[0039] The residual is: ε (0) =(ε (0) (1),ε (0) (2),…,ε (0) (n));

[0040] The error sequence is as follows:

[0041] The average relative error is:

[0042] Select when At that time, the residual inspection was qualified.

[0043] In this embodiment, the posterior difference test mainly examines the distribution of the residuals, and a smaller test value is better. The posterior difference test process is as follows:

[0044]

[0045] in, The variance of the original data. The variance of the residual data. The mean of the original data. This represents the mean error.

[0046] The formula for testing the posterior difference is:

[0047] In this embodiment, the small error probability test determines the accuracy of the model's predictions by analyzing the distribution of prediction errors; a higher test value is better. The small error probability test process is as follows:

[0048]

[0049] The posterior error is an indicator of accuracy; the smaller the value, the higher the accuracy. The small error probability is also an indicator of accuracy; the larger the value, the higher the model accuracy. Whether the test requirements are met is shown in Table 1 below:

[0050] Table 1 Accuracy Level Comparison Table

[0051] Precision level excellent good qualified Unqualified C C≤0.35 0.35<C≤0.5 0.5≤C≤0.65 C>0.65 p p≥0.95 0.8≤p<0.95 0.7≤p<0.8 P<0.7

[0052] If the accuracy is satisfactory after using the three testing methods described above, the next step can be performed. However, if any one of the three testing methods fails, the process must be repeated, and the grey prediction must be performed again until the prediction result meets the requirements of all three tests simultaneously.

[0053] S120. Based on the predicted sales volume of electric vehicles during the target period and the proportion of electric vehicle batteries to be retired during the target period, determine the number of electric vehicle batteries to be retired during the target period.

[0054] In some embodiments, the number of electric vehicle batteries to be retired during the target period can be determined based on the Stanford model, the predicted sales volume of electric vehicles during the target period, and the proportion of electric vehicle batteries to be retired during the target period.

[0055] In this embodiment, the Stanford model can provide an important basis for optimizing the design of the reverse logistics network for electric vehicle batteries. The mathematical expression of the model is as follows:

[0056]

[0057] Among them, Q i S represents the number of electric vehicle batteries retired in year i, which is the total number of batteries transported from recycling points to testing and evaluation centers in the subsequent reverse logistics network; i Let R be the predicted sales volume of electric vehicle batteries in year i, which is the sales volume predicted by the grey prediction model; i This represents the percentage of electric vehicle batteries that are retired after i years of use.

[0058] Since the number of retired electric vehicles is directly proportional to the population of a region, assuming a consistent electric vehicle penetration rate, the larger the population of a region, the more electric vehicles there are, and the more retired batteries there will be. Therefore, the following formula can be used for weight allocation:

[0059]

[0060] Among them, Q x P represents the allocation amount for the x-th recycling point. x Q represents the population of the region where the x-th recycling point is located; RC This refers to the total number of retired batteries in the entire network, which is the total number of batteries transported from recycling points to testing and evaluation centers in the subsequent reverse logistics network.

[0061] S130. Based on the number of electric vehicle batteries retired during the target period, the recycling price of retired batteries, the location of the proposed multi-target logistics node, the facility cost of building the multi-target logistics node, the battery processing cost, the operating cost of the multi-target logistics node, the battery transfer cost, and the waste disposal cost, construct a retired battery processing cost model.

[0062] In this embodiment, the multi-target logistics node includes multiple testing and evaluation centers in different locations, multiple secondary utilization centers in different locations, and multiple dismantling and remanufacturing plants in different locations.

[0063] In this embodiment, retired batteries need to be transported from target recycling points to target testing and evaluation centers, then from the target testing and evaluation centers to target secondary utilization centers or target dismantling and remanufacturing plants, and finally from the target secondary utilization centers or target dismantling and remanufacturing plants to waste treatment centers. The reverse logistics network for electric vehicle batteries includes multiple recycling points, and the target recycling point, target testing and evaluation center, and target dismantling and remanufacturing plant can all be any one of these. The locations of the recycling points and the waste treatment centers are all fixed.

[0064] In some embodiments, the purchase cost of recycled batteries at recycling outlets is calculated as follows:

[0065] C GZ =∑Q RC P B ;

[0066] Among them, Q RC P represents the total number of retired batteries in the entire network. B The recycling price per unit mass of retired batteries (RMB / ton).

[0067] In some embodiments, the facility cost of constructing a multi-target logistics node includes the cost of constructing a selected target testing and evaluation center, the cost of constructing a selected target secondary utilization center, and the cost of constructing a selected target dismantling and remanufacturing plant. The selected target testing and evaluation center is a selection of several testing and evaluation centers from multiple testing and evaluation centers located in different locations; the selected target secondary utilization center is a selection of several secondary utilization centers from multiple secondary utilization centers located in different locations; and the selected target dismantling and remanufacturing plant is a selection of several dismantling and remanufacturing plants from multiple dismantling and remanufacturing plants located in different locations.

[0068] In this embodiment, the facility cost for constructing multi-target logistics nodes is calculated as follows:

[0069]

[0070] Among them, B C Cost required to construct the target testing and evaluation center (in yuan); B L Cost required to construct the target tiered utilization center (in yuan); B M Cost required to build a dismantling and remanufacturing plant (RMB); Y C To determine whether to build a target detection and evaluation center, if Y C If Y = 0, then it is not established; if Y = 0, then it is not established. C =1, then establish; Y L This indicates whether to construct the target tiered utilization center. If Y L If Y = 0, then it is not established; if Y = 0, then it is not established. L =1, then establish; Y M This indicates whether to construct a target dismantling and remanufacturing plant. If Y M If Y = 0, then it is not established; if Y = 0, then it is not established. M =1, then establish. c is the total number of selected target testing and evaluation centers, l is the total number of selected target secondary utilization centers, and m is the total number of selected target dismantling and remanufacturing plants.

[0071] In some embodiments, the cost of battery processing includes the cost of processing all retired batteries at a selected target testing and evaluation center, the cost of processing the first batch of retired batteries at a selected target cascade utilization center, and the cost of processing the second batch of retired batteries at a selected target dismantling and remanufacturing plant. The sum of the first batch of retired batteries and the second batch of retired batteries represents the total number of retired batteries.

[0072] In this embodiment, the formula for calculating the cost of battery processing is:

[0073]

[0074] Among them, H CProcessing cost (RMB / ton) required for each unit mass of battery selected for testing and evaluation; H L The processing cost (RMB / ton) required for each unit mass of battery selected for secondary utilization; H M The processing cost (RMB / ton) required for each unit mass of battery selected for secondary utilization; Q RC Q represents the number (tons) of batteries transported from recycling point R to the selected testing and evaluation center C, which is the total number of retired batteries; CL Q represents the number (tons) of batteries transported from the selected testing and evaluation center C to the selected secondary utilization center L; CM The number (tons) of batteries transported from the selected testing and evaluation center C to the selected dismantling and remanufacturing plant M.

[0075] In some embodiments, the operating costs of the multi-target logistics node include the operating costs required for the selected target testing and evaluation center to process all retired batteries, the operating costs required for the selected target secondary utilization center to process the first portion of retired batteries, and the operating costs required for the selected target dismantling and remanufacturing plant to process the second portion of retired batteries.

[0076] In this embodiment, the formula for calculating the operating cost of multi-target logistics nodes is as follows:

[0077]

[0078] Among them, O C Operating cost per unit mass of batteries processed by the selected testing and evaluation center (RMB / ton); O L The operating cost (RMB / ton) required to process each unit mass of batteries at the selected secondary utilization center; O M Operating cost (RMB / ton) required to process each unit mass of batteries at the selected dismantling and remanufacturing plant.

[0079] In some embodiments, battery transport costs include the transportation costs of transporting all retired batteries from the target recycling point to the selected target testing and evaluation center, the transportation costs of transporting the first portion of retired batteries from the selected target testing and evaluation center to the selected target secondary utilization center, the transportation costs of transporting the second portion of retired batteries from the selected target testing and evaluation center to the selected target dismantling and remanufacturing plant, the transportation costs of transporting the third portion of retired batteries from the selected target dismantling and remanufacturing plant to the waste treatment center, and the transportation costs of transporting the fourth portion of retired batteries from the selected target secondary utilization center to the waste treatment center.

[0080] In this embodiment, the formula for calculating battery transportation costs is:

[0081]

[0082] Among them, P T The transportation cost per unit distance and per unit mass (yuan / km); S RC S is the distance (km) from the recycling point R to the selected testing and evaluation center C; CL S represents the distance (km) from the selected target detection and evaluation center C to the selected secondary utilization center L; CM S is the distance (km) from the selected testing and evaluation center C to the selected dismantling and remanufacturing plant M; LG S is the distance (km) from the selected secondary utilization center L to the waste treatment center G; MG β is the distance (km) from the selected dismantling and remanufacturing plant M to the waste treatment center G; LG The percentage of batteries transported from the selected secondary utilization center L to the waste treatment center G; β MG The percentage of batteries transported from the selected dismantling and remanufacturing plant M to the waste treatment center G.

[0083] In some embodiments, waste disposal costs include the processing costs of a waste disposal center for processing the third and fourth portions of retired batteries.

[0084] In this embodiment, the formula for calculating waste disposal costs is:

[0085]

[0086] Among them, O G The processing cost (RMB / ton) required by the waste battery treatment center to process each unit mass of battery waste.

[0087] The formula for calculating the cost model of retired battery disposal is as follows:

[0088] C CB =C GZ +C CL +C YY +C YS +C FL +C JS .

[0089] It should be noted that this only considers the recycling of the same type of battery within a single cycle, without further segmentation by battery type, and recycles retired batteries at a uniform price. The waste disposal methods and costs at the secondary utilization center and the dismantling and remanufacturing plant are the same. For ease of calculation, it is assumed that the unit distance and unit mass battery transportation cost between recycling points, testing and evaluation centers, secondary utilization centers, dismantling and remanufacturing plants, and waste treatment centers is the same, and that transportation cost and transportation distance are positively correlated.

[0090] S140. Based on the carbon emissions generated from the construction of multi-target logistics nodes, the carbon emissions generated during the operation of each target logistics node, and the carbon emissions generated during transportation, determine the total carbon dioxide emissions; based on the total carbon dioxide emissions and carbon dioxide emission taxes or carbon quotas, construct a carbon tax cost model or a carbon trading cost model.

[0091] In some embodiments, the carbon emissions generated by constructing multi-target logistics nodes include the carbon emissions generated by constructing selected target cascade utilization centers, the carbon emissions generated by constructing selected target testing and evaluation centers, and the carbon emissions generated by constructing selected target dismantling and remanufacturing plants.

[0092] In this embodiment, the formula for calculating the carbon emissions generated by constructing multi-objective logistics nodes is as follows:

[0093]

[0094] Among them, E BC Carbon emissions (tonnes) generated by the newly selected target cascade utilization center L; E BL Carbon emissions (tonnes) generated by the newly constructed target testing and evaluation center C; E BM Carbon emissions (tonnes) generated by the selected target dismantling and remanufacturing plant M for new construction.

[0095] In some embodiments, the carbon emissions generated during the operation of each target logistics node include the carbon emissions generated by the selected target testing and evaluation center processing all retired batteries, the carbon emissions generated by the selected target secondary utilization center processing the first part of retired batteries, and the carbon emissions generated by the selected target dismantling and remanufacturing plant processing the second part of retired batteries.

[0096] In this embodiment, the formula for calculating the carbon emissions generated during the operation of each target logistics node is as follows:

[0097]

[0098] Among them, E C The carbon emissions (tons) generated per unit mass of battery processed by the selected target testing and evaluation center C; E L E represents the carbon emissions (tons) generated per unit mass of battery processed by the selected target cascade utilization center L; M The carbon emissions (in tons) generated per unit mass of battery processed at the selected target dismantling and remanufacturing plant M.

[0099] In some embodiments, the carbon emissions generated during transportation are determined based on the fuel consumption and carbon emissions generated from transporting retired batteries.

[0100] In this embodiment, Bowyer's real-time carbon emission calculation model can be used.

[0101]

[0102] in, This indicates the fuel consumption per unit distance of a transport vehicle when it is unloaded during transportation. Q represents the fuel consumption per unit distance when the transport vehicle is fully loaded during transportation; Q represents the maximum freight capacity of the transport vehicle used during transportation; X represents the transport volume of the transport vehicle during transportation.

[0103] Carbon emissions during transportation can be represented by multiplying the CO2 emission factor by the fuel consumption. The carbon emissions during the transportation of used power batteries are expressed as follows:

[0104] E(Q)=γE T S;

[0105] Where E(Q) represents the carbon emissions during the transportation of Q batteries; γ represents the fuel consumption per unit mileage of the transport vehicle during the transfer process; E T S represents the carbon emissions generated by consuming 1L of fuel; S represents the transportation distance, assuming that the vehicles are always fully loaded during transportation.

[0106] The carbon emissions per ton of battery transported per unit distance can be calculated by dividing E(Q) by the weight of the battery being transported.

[0107] Therefore, the carbon emissions during transportation are:

[0108]

[0109] The above formula can also be converted to:

[0110]

[0111] Therefore, the formula for calculating total carbon dioxide emissions is:

[0112]

[0113] In some embodiments, under a carbon tax framework, the carbon tax cost model is the product of the carbon dioxide emission tax and the total carbon dioxide emissions.

[0114] In this embodiment, the calculation formula for the carbon tax cost model is as follows:

[0115]

[0116] in, For carbon tax.

[0117] In some embodiments, the carbon trading cost model is the product of the difference between total carbon dioxide emissions and carbon allowances and the unit price of carbon emission allowances.

[0118] In this embodiment, carbon allowances are the total greenhouse gas emission limits allocated to key emitting entities for a specific period, with one allowance equal to one ton of carbon dioxide equivalent. Essentially, it transforms carbon emission rights into tradable assets through quantitative control. A certain amount of carbon emission rights is granted to enterprises through sales in different cycles, thus providing opportunities for cost reduction and efficiency improvement. Assume... The value of the carbon allowances granted to each enterprise, assuming a unit price of carbon emission allowances of [value missing]. The carbon trading cost model at this time is:

[0119]

[0120] It can be observed that if a company's carbon emissions are less than the amount of carbon allowances issued free of charge by the government, the company can avoid this part of the carbon emission costs. At the same time, the company can also sell the unused carbon emission rights to make a profit.

[0121] S150. Based on the retired battery processing cost model, carbon tax cost model, or carbon trading cost model, construct a carbon tax reverse logistics network model or a carbon trading reverse logistics network model.

[0122] In this embodiment, the carbon tax reverse logistics network model is the minimum of the sum of the retired battery processing cost model and the carbon tax cost model, and the carbon trading reverse logistics network model is the minimum of the sum of the retired battery processing cost model and the carbon trading cost model.

[0123] Specifically, the carbon tax reverse logistics network model is as follows:

[0124]

[0125] The carbon trading reverse logistics network model is as follows:

[0126]

[0127] S160. Based on preset constraints, solve the carbon tax reverse logistics network model or the carbon trading reverse logistics network model to obtain the optimal electric vehicle battery reverse logistics network.

[0128] In some embodiments, the preset constraints include flow balance constraints, processing capacity constraints of multi-target logistics nodes, quantity constraints of multi-target logistics nodes, non-negativity constraints, and 0-1 constraints. The processing capacity constraints of multi-target logistics nodes include the processing capacity constraints of the selected target inspection and evaluation center, the selected target secondary utilization center, and the selected target inspection, dismantling, and remanufacturing plant. The non-negativity constraints include that the total number of retired batteries, the number of retired batteries processed by the selected target secondary utilization center, and the number of retired batteries processed by the selected target inspection, dismantling, and remanufacturing plant are all greater than or equal to 0. The 0-1 constraints are used to indicate whether the number of selected target inspection and evaluation centers, the number of selected target secondary utilization centers, and the selected target inspection, dismantling, and remanufacturing plants are being upgraded. The quantity constraints of multi-target logistics nodes include that the number of selected target inspection and evaluation centers, the number of selected target secondary utilization centers, and the number of selected target inspection, dismantling, and remanufacturing plants are all greater than or equal to 1.

[0129] In this embodiment, the flow balance constraint is:

[0130]

[0131] Flow balance constraints are implemented to ensure that the number of batteries flowing in at each logistics node equals the number flowing out.

[0132] The processing capacity constraints of multi-target logistics nodes are:

[0133]

[0134]

[0135] in, The maximum processing capacity of the selected target detection and evaluation center. To maximize the processing capacity of the selected target tiers of the center, The maximum processing capacity of the selected dismantling and remanufacturing plant.

[0136] The nonnegativity constraint is to ensure that the data in the reverse logistics network is greater than or equal to 0.

[0137] Q RC ≥0, Q CL ≥0, Q CM ≥0, β LG ≥0, β MG ≥0

[0138] The 0-1 constraint indicates that each node has only two possible scenarios: either it is upgraded or it is not upgraded. 1 means it is upgraded and 0 means it is not upgraded.

[0139] Y C,Y L ,Y M ∈{0,1}.

[0140] The number constraints of multi-target logistics nodes include that the number of selected target testing and evaluation centers, the number of selected target secondary utilization centers, and the number of selected target testing, dismantling and remanufacturing plants are all greater than or equal to 1.

[0141]

[0142] Once the preset constraints, carbon tax reverse logistics network model, or carbon trading reverse logistics network model are determined, the Lingo calculation software can be used to solve the model.

[0143] For ease of understanding, a specific embodiment is provided.

[0144] Company F has 16 electric vehicle battery recycling stations in Beijing. These 16 recycling stations are sales outlets that also serve as electric vehicle battery recycling points. Consumers can purchase electric vehicles at these sales outlets and also recycle their electric vehicle batteries at these outlets. Their locations are shown in Table 1.

[0145] Table 1. Information on F Company's trolley recycling network points

[0146]

[0147]

[0148] Company F plans to build a total of six testing and evaluation centers, the locations of which are shown in Table 2.

[0149] Table 2 Information on Testing and Evaluation Centers

[0150] Testing and Evaluation Center longitude latitude C1 116.431923 39.99183 C2 116.490525 39.874972 C3 116.417738 40.070693 C4 116.261764 39.864845 C5 116.427105 40.00838 C6 116.340414 40.050242

[0151] Company F currently has 2 secondary utilization centers and 2 dismantling and remanufacturing plants, and plans to build 2 more secondary utilization centers and 2 more dismantling and remanufacturing plants. The location information of these 8 logistics nodes is shown in Tables 3 and 4.

[0152] Table 3 Information on Tiered Utilization Center

[0153] Secondary Utilization Center longitude latitude L1 116.703948 39.746292 L2 116.538229 39.824159 L3 116.00187 39.738084 L4 116.551168 40.105477

[0154] Table 4 Information on Dismantling and Remanufacturing Plants

[0155]

[0156]

[0157] By querying, the distance relationships between multiple target logistics nodes can be determined: as shown in Table 5-7, Table 5 shows the distance (km) from each recycling point R to each testing center C.

[0158] serial number C1 C2 C3 C4 C5 C6 R1 3.27 14.61 23.45 18.92 6.84 15.33 R2 6.55 12.38 27.89 21.07 4.73 18.64 R3 8.92 19.74 12.56 25.31 9.67 10.85 R4 15.03 9.45 34.12 30.58 13.22 22.97 R5 26.74 7.89 41.35 35.21 24.56 31.08 R6 11.89 16.23 18.97 22.45 8.95 14.12 R7 9.67 5.32 28.14 16.78 7.31 12.45 R8 24.56 18.95 39.67 32.89 21.34 27.12 R9 13.21 14.78 25.89 19.67 10.23 16.89 R10 22.45 27.12 31.23 28.67 19.56 24.78 R11 18.67 21.45 26.78 23.12 15.89 20.12 R12 7.89 10.23 19.45 15.67 6.45 12.34 R13 9.12 13.45 14.78 18.23 8.12 11.45 R14 20.34 24.56 36.78 31.23 18.67 26.45 R15 16.78 19.23 28.45 24.12 14.56 20.89 R16 14.56 17.89 22.34 20.45 12.34 18.12

[0159] Table 6. Distances (km) from each testing center C to the secondary utilization center L

[0160] serial number C1 C2 C3 C4 C5 C6 L1 36 23.2 44 40.09 38 45.89 L2 21 6.96 29 24.09 23 30.27 L3 46 44.52 51 26.33 47 45.16 L4 16 26.11 12 36.4 15 18.99

[0161] Table 7. Distances (km) from each testing center C to the dismantling and remanufacturing plant M

[0162] serial number C1 C2 C3 C4 C5 C6 M1 30 15.74 38 28.37 32 38.77 M2 29 42.5 21 38.38 27 19.36 M3 34 24.24 42 19.35 36 39.17 M4 22 31.93 16 41.48 20 22.5

[0163] Based on surveys and data collection, the required cost parameters for the reverse logistics network of electric vehicle batteries are shown in Table 8-11.

[0164] Table 8. Relevant data from the testing and evaluation center.

[0165]

[0166]

[0167] Table 9. Relevant data from the tiered utilization center

[0168] Secondary Utilization Center L1 L2 L3 L4 Construction cost (ten thousand yuan) 1100 1105 1300 1098 Carbon emissions from construction (tons) 4100 3500 3700 3600 Maximum processing capacity (10,000 tons / cycle) 8.2 9.5 7.7 6.5 Carbon emissions per unit of battery processed (tons) 0.23 0.21 0.22 0.22 Operating cost per unit of battery (RMB / ton) 180 185 178 182 Unit battery processing cost (RMB / ton) 3500 3480 3490 3440

[0169] Table 10. Relevant data from dismantling and remanufacturing plants.

[0170] Dismantling and Remanufacturing Plant M1 M2 M3 M4 Construction cost (ten thousand yuan) 1700 1685 1677 1678 Carbon emissions from construction (tons) 4350 4270 4300 4240 Maximum processing capacity (10,000 tons / cycle) 11.5 12.5 11.9 10.8 Carbon emissions per unit of battery processed (tons) 0.28 0.31 0.29 0.27 Operating cost per unit of battery (RMB / ton) 200 210 200 190 Unit battery processing cost (RMB / ton) 4890 4900 4840 4790

[0171] Table 11 Other cost parameter data

[0172] parameter numerical values Price per unit mass of recycled batteries (RMB / ton); 10000 Transportation cost per unit distance and per unit mass (yuan / ton / km); 0.5 Carbon tax price per unit of carbon emissions (RMB / ton); 400 Carbon emissions (tons) generated by transporting a unit mass of battery per 1 km 0.0003 The percentage of batteries transported from secondary utilization centers to waste treatment centers 0.1 The percentage of batteries transported from dismantling and remanufacturing plants to waste treatment centers 0.05 The proportion of batteries sent from the testing and evaluation center to the dismantling and remanufacturing plant 0.6 Battery proportion sent from the testing and evaluation center to the secondary utilization center 0.4 Weight of each battery (tons) 0.5 Cost of processing each unit of battery waste (RMB) 800

[0173] Once the above basic data is obtained, electric vehicle sales can be predicted.

[0174] The sales data for electric vehicles in Beijing from 2019 to 2024 are shown in Table 12.

[0175] Table 12 Sales data of electric vehicles in Beijing, 2019-2024

[0176]

[0177] Using the GM(1,1) model to predict car sales data, with C = 150000, the solution to the whitening equation is: The final prediction results after subtracting the accumulated values ​​are shown in Table 13.

[0178] Table 13 Prediction Results

[0179] years Data value (vehicles) Forecast value (vehicles) Rounded-down predicted value (vehicles) 2019 81513 81513 81513 2020 95353 88207.51 88207 2021 129796 119567.05 119567 2022 162017 162075.53 162075 2023 209206 219696.64 219696 2024 316492 297,803.20 297,803 2025 / 353,977.54 353978 2026 / 486,941.83 486942 2027 / 640,999.38 640999

[0180] After performing residual tests, posterior error tests, and small error probability tests on the above prediction data, the prediction results of the invention all satisfy these three tests. Figure 3 The fitted curve is the predicted sales curve of electric vehicles in Beijing from 2025 to 2027.

[0181] The prediction results of the retirement volume using the Stanford model are shown in Table 14:

[0182] Table 14 Stanford Model's Retirement Prediction

[0183]

[0184] As shown in Table 14, it is estimated that Beijing will recycle 116,477 electric vehicle batteries by 2027. According to publicly available information, Company F has a market share of approximately 10%, which translates to 11,648 batteries being recycled by Company F. The retired electric vehicle batteries in the city are distributed among Company F's 16 existing recycling stations in the seven urban districts, with each district's population weighted as a percentage of the total population of the seven districts. After allocating the quantities among the districts, dividing by the number of recycling stations within each district yields the number of batteries processed at each recycling station. Based on the population density of each district obtained from publicly available data from the Beijing Municipal Government, the weighted battery recycling station's estimated battery recycling volume in 2027 is shown in Table 15.

[0185] Table 15: Battery Recycling Quantity at Recycling Outlets

[0186] Recycling Point Administrative region Population (10,000 people) Assign weights (%) Quantity recycled (pieces) R1 Chaoyang District 345 4.02％ 468 R2 Chaoyang District 345 4.02％ 468 R3 Chaoyang District 345 4.02％ 468 R4 Chaoyang District 345 4.02％ 468 R5 Chaoyang District 345 4.02％ 468 R6 Chaoyang District 345 4.02％ 468 R7 Fengtai District 202 7.05％ 821 R8 Daxing District 199 6.95％ 810 R9 Fengtai District 202 7.05％ 821 R10 Shijingshan District 57 3.98％ 463 R11 Haidian District 313 7.29％ 849 R12 Haidian District 313 7.29％ 849 R13 Haidian District 313 7.29％ 849 R14 Tongzhou District 184 12.85％ 1495 R15 Shunyi District 132 9.22％ 1073 R16 Daxing District 199 6.95％ 810 total / / 100％ 11648

[0187] Once the number of electric vehicle batteries to be retired within the target time period is determined, the carbon tax reverse logistics network model or carbon trading reverse logistics network model provided earlier in this invention can be constructed and simulated using Lingo 18.0.

[0188] Under the carbon tax framework, a carbon dioxide emission tax of 400 yuan per ton was selected. The constructed carbon tax reverse logistics network model was solved, and the results are shown in Table 16. The selection of logistics nodes is shown in Table 14.

[0189] Table 16 Cost Situation under Carbon Tax Background

[0190] Solve the objective Solve the numerical values Total cost (RMB) 192719500.02 Total battery purchase cost (RMB) 58238500 Total construction cost (RMB) 91280000 Total operating processing cost (RMB) 30055740.28 Total transportation cost (RMB) 244360.14 Total waste disposal cost (RMB) 326087.6 Total carbon emission cost (RMB) 12,574,812 Total carbon emissions (tons) 31437.03 Total carbon emissions from construction (tonnes) 29240 Total carbon emissions per unit of volume (tons) 2047.95 Total carbon emissions from transportation (tons) 149.08

[0191] The selection of logistics nodes is shown in Table 17:

[0192] Table 17 Logistics Facility Selection

[0193] facility type Selection Results Testing and Evaluation Center C2, C3, C4, C5 Secondary Utilization Center L2, L4 Dismantling and Remanufacturing Plant M3, M4

[0194] At this point, batteries collected from recycling points R1, R2, R3, and R6 are sent to testing and evaluation center C5; batteries collected from recycling points R4, R5, R7, R8, R14, and R16 are sent to testing and evaluation center C2; batteries collected from recycling points R9, R10, R11, and R12 are sent to testing and evaluation center C4; and batteries collected from recycling points R13 and R15 are sent to testing and evaluation center C3. Testing and evaluation centers C3 and C5 then inspect and evaluate the batteries before sending them to secondary utilization center L4; and testing and evaluation centers C2 and C4 then monitor and evaluate the batteries before sending them to dismantling and remanufacturing plant M3. The resulting optimal reverse logistics network for electric vehicle batteries is as follows: Figure 4 As shown.

[0195] Under the carbon trading framework, when carbon emissions exceed carbon quotas, to meet the reverse logistics network construction needs of Company F, it is necessary to build 4 testing and evaluation centers, 2 secondary utilization centers, and 2 dismantling and remanufacturing plants. Solving the carbon trading reverse logistics network model yields the costs shown in Table 18.

[0196] Table 18 System Costs Under the Background of Exceeding Carbon Quotas

[0197]

[0198]

[0199] In the context of carbon trading, when carbon emissions are lower than the government's carbon allowance, the reverse logistics network model for carbon trading is solved, and the costs are shown in Table 19:

[0200] Table 19 System Costs Under the Background of Carbon Quotas Below Standard

[0201] Solve the objective Solve the numerical values Total cost (RMB) 179119500.02 Total battery purchase cost (RMB) 58238500 Total construction cost (RMB) 91280000 Total operating processing cost (RMB) 30055740.28 Total transportation cost (RMB) 244360.14 Total waste disposal cost (RMB) 326087.6 Total carbon emission cost (RMB) -1025188 Total carbon emissions (tons) 31437.03 Total carbon emissions from construction (tonnes) 29240 Total carbon emissions per unit of volume (tons) 2047.95 Total carbon emissions from transportation (tons) 149.08

[0202] In cases where the carbon allowance is below or above the allowance, the selected logistics facilities follow the same flow direction as the batteries. The selection of logistics nodes is shown in Table 20.

[0203] Table 20 Logistics Facility Selection

[0204] facility type Selection Results Testing and Evaluation Center C2, C3, C4, C5 Secondary Utilization Center L2, L4 Dismantling and Remanufacturing Plant M3, M4

[0205] At this point, batteries collected from recycling points R1, R2, R3, and R6 are sent to testing and evaluation center C5; batteries collected from recycling points R4, R5, R7, R8, R14, and R16 are sent to testing and evaluation center C2; batteries collected from recycling points R9, R10, R11, and R12 are sent to testing and evaluation center C4; and batteries collected from recycling points R13 and R15 are sent to testing and evaluation center C3. Testing and evaluation centers C3 and C5 then inspect and evaluate the batteries before sending them to secondary utilization center L4; and testing and evaluation centers C2 and C4 then monitor and evaluate the batteries before sending them to dismantling and remanufacturing plant M3. It can be observed that the optimization results are the same as those under the carbon tax framework, and... Figure 4 As shown.

[0206] To comprehensively consider the optimization results of the reverse logistics network for electric vehicle batteries under the carbon tax and carbon trading market systems, C2, C3, C4, and C5 can be selected as the locations for testing and evaluation centers; L2 and L4 as the locations for secondary utilization centers; and M3 and M4 as the locations for dismantling and remanufacturing plants. The battery flow is as follows: Figure 5 As shown.

[0207] The optimization method provided by this invention aims to reduce the cost and carbon emissions of reverse logistics networks and decrease energy consumption during transportation. First, it predicts trolley sales volume for a target period using historical trolley sales data. Then, it determines the number of trolley batteries to be retired during the target period based on the predicted sales volume and the proportion of retired trolley batteries. Next, it constructs a retired battery processing cost model based on the location of the proposed multi-target logistics nodes, the facility costs of constructing these nodes, battery processing costs, operating costs of the nodes, battery transfer costs, and waste disposal costs. The total carbon dioxide emissions are determined based on the carbon emissions generated during construction, operation, and transportation. Finally, based on pre-defined constraints, it solves the constructed carbon tax reverse logistics network model or carbon trading reverse logistics network model to obtain the optimal trolley battery reverse logistics network. By predicting trolley sales volume and determining the number of retired trolley batteries during the target period, it provides an accurate data foundation for the subsequent rational planning of the reverse logistics network. To rationally select a reverse logistics network, this invention constructs a retired battery processing cost model based on economic costs, a carbon tax cost model and a carbon trading cost model based on carbon tax and carbon emission trading, and finally constructs a carbon tax reverse logistics network model that comprehensively considers economic costs and carbon tax, and a carbon trading reverse logistics network model that comprehensively considers economic costs and carbon emission trading. This achieves efficient recycling of electric vehicle batteries, reduces the cost of the reverse logistics network, and increases the economic benefits of recycling.

[0208] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0209] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0210] Furthermore, embodiments of the present invention also provide an optimization device for a reverse logistics network for electric vehicle batteries. For ease of explanation, only the parts related to embodiments of the present invention are shown, and are detailed below:

[0211] An optimization device for the reverse logistics network of electric vehicle batteries, comprising:

[0212] The sales forecasting module is used to predict the sales volume of trolley vehicles within a target period based on historical sales data.

[0213] The retirement prediction module is used to determine the number of electric vehicle batteries to be retired during the target period based on the predicted sales volume of electric vehicles and the proportion of electric vehicle batteries to be retired during the target period.

[0214] The retirement cost module is used to construct a retirement battery processing cost model based on the number of electric vehicle batteries retired within a target period, the recycling price of retired batteries, the location of the proposed multi-target logistics nodes, the facility cost of building the multi-target logistics nodes, the battery processing operation cost, the operating cost of the multi-target logistics nodes, the battery transfer cost, and the waste disposal cost.

[0215] The carbon emission module is used to determine the total carbon dioxide emissions based on the carbon emissions generated from the construction of multi-target logistics nodes, the carbon emissions generated during the operation of each target logistics node, and the carbon emissions generated during transportation; and to construct a carbon tax cost model or a carbon trading cost model based on the total carbon dioxide emissions and carbon emission taxes or carbon quotas.

[0216] The model building module is used to construct a carbon tax reverse logistics network model or a carbon trading reverse logistics network model based on the retired battery processing cost model, carbon tax cost model, or carbon trading cost model.

[0217] The solution module is used to solve the carbon tax reverse logistics network model or the carbon trading reverse logistics network model based on preset constraints, so as to obtain the optimal electric vehicle battery reverse logistics network.

[0218] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 6As shown, the electronic device 6 of this embodiment includes a processor 60 and a memory 61. The memory 61 stores a computer program 62. When the processor 60 executes the computer program 62, it implements the steps in the various method embodiments described above. Alternatively, when the processor 60 executes the computer program 62, it implements the functions of each module / unit in the various device embodiments described above.

[0219] For example, computer program 62 may be divided into one or more modules / units, which are stored in memory 61 and executed by processor 60 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 62 in electronic device 6.

[0220] Electronic device 6 may include, but is not limited to, processor 60 and memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 6 may also include input / output devices, network access devices, buses, etc.

[0221] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0222] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0223] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An optimization method for a reverse logistics network for electric vehicle batteries, characterized in that, include: Based on historical sales data of electric vehicles, predict the sales volume of electric vehicles within the target period. Based on the predicted sales volume of electric vehicles during the target period and the proportion of electric vehicle batteries to be retired during the target period, the number of electric vehicle batteries to be retired during the target period is determined. Based on the number of electric vehicle batteries retired during the target period, the recycling price of retired batteries, the location of the proposed multi-target logistics node, the facility cost of building the multi-target logistics node, the battery processing cost, the operating cost of the multi-target logistics node, the battery transfer cost, and the waste disposal cost, a retired battery processing cost model is constructed. The total carbon dioxide emissions are determined based on the carbon emissions generated from the construction of multi-target logistics nodes, the carbon emissions generated during the operation of each target logistics node, and the carbon emissions generated during transportation. Construct a carbon tax cost model or a carbon trading cost model based on total carbon dioxide emissions, carbon dioxide emission tax, or carbon quotas. Based on the retired battery processing cost model, the carbon tax cost model, or the carbon trading cost model, construct a carbon tax reverse logistics network model or a carbon trading reverse logistics network model. Based on preset constraints, the carbon tax reverse logistics network model or the carbon trading reverse logistics network model is solved to obtain the optimal electric vehicle battery reverse logistics network.

2. The method for optimizing the reverse logistics network for electric vehicle batteries according to claim 1, characterized in that, The prediction of trolleybus sales during the target period based on historical trolleybus sales data includes: Based on the grey prediction model and historical sales data of trolleybuses, the sales volume of trolleybuses within the target period is predicted. The determination of the number of electric vehicle batteries to be retired during the target period, based on the predicted sales volume of electric vehicles within the target period and the proportion of electric vehicle batteries to be retired during the target period, includes: Based on the Stanford model, the predicted sales volume of electric vehicles during the target period, and the proportion of electric vehicle batteries to be retired during the target period, the number of electric vehicle batteries to be retired during the target period is determined.

3. The method for optimizing the reverse logistics network for electric vehicle batteries according to claim 1, characterized in that, The multi-target logistics node includes multiple testing and evaluation centers in different locations, multiple secondary utilization centers in different locations, and multiple dismantling and remanufacturing plants in different locations. The reverse logistics network for electric vehicle batteries involves transporting batteries from target recycling points to target testing and evaluation centers, from the target testing and evaluation centers to target secondary utilization centers or target dismantling and remanufacturing plants, and then from the target secondary utilization centers or target dismantling and remanufacturing plants to waste treatment centers. The reverse logistics network includes multiple recycling points, where any one of the target recycling points, the target testing and evaluation centers, or the target dismantling and remanufacturing plants can be any one of the dismantling and remanufacturing plants.

4. The optimization method for the reverse logistics network of electric vehicle batteries according to claim 3, characterized in that, The facility costs for constructing multi-target logistics nodes include the cost of constructing a selected target testing and evaluation center, the cost of constructing a selected target tiered utilization center, and the cost of constructing a selected target dismantling and remanufacturing plant. The battery processing cost includes the cost of processing all retired batteries at the selected target testing and evaluation center, the cost of processing the first batch of retired batteries at the selected target cascade utilization center, and the cost of processing the second batch of retired batteries at the selected target dismantling and remanufacturing plant. The operating costs of the multi-target logistics nodes include the operating costs required for the selected target testing and evaluation center to process all retired batteries, the operating costs required for the selected target cascade utilization center to process the first part of retired batteries, and the operating costs required for the selected target dismantling and remanufacturing plant to process the second part of retired batteries. The battery transportation costs include the transportation costs of transporting all retired batteries from the target recycling point to the selected target testing and evaluation center, the transportation costs of transporting the first part of retired batteries from the selected target testing and evaluation center to the selected target secondary utilization center, the transportation costs of transporting the second part of retired batteries from the selected target testing and evaluation center to the selected target dismantling and remanufacturing plant, the transportation costs of transporting the third part of retired batteries from the selected target dismantling and remanufacturing plant to the waste treatment center, and the transportation costs of transporting the fourth part of retired batteries from the selected target secondary utilization center to the waste treatment center. The waste disposal cost includes the processing costs of the waste disposal center for processing the third and fourth batches of retired batteries.

5. The method for optimizing the reverse logistics network for electric vehicle batteries according to claim 3, characterized in that, The carbon emissions generated by the construction of multi-target logistics nodes include the carbon emissions generated by the construction of selected target cascade utilization centers, the carbon emissions generated by the construction of selected target testing and evaluation centers, and the carbon emissions generated by the construction of selected target dismantling and remanufacturing plants. The carbon emissions generated during the operation of each target logistics node include the carbon emissions generated by the selected target testing and evaluation center in processing all retired batteries, the carbon emissions generated by the selected target secondary utilization center in processing the first part of retired batteries, and the carbon emissions generated by the selected target dismantling and remanufacturing plant in processing the second part of retired batteries. The carbon emissions generated during the transportation process are determined based on the fuel consumption and carbon emissions generated during the transportation of retired batteries. The carbon tax cost model is the product of carbon dioxide emission tax and total carbon dioxide emissions. The carbon trading cost model is the product of the difference between total carbon dioxide emissions and carbon allowances and the unit price of carbon emission allowances.

6. The method for optimizing the reverse logistics network for electric vehicle batteries according to claim 3, characterized in that, The preset constraints include flow balance constraints, processing capacity constraints of multi-target logistics nodes, quantity constraints of multi-target logistics nodes, non-negativity constraints, and 0-1 constraints; the processing capacity constraints of multi-target logistics nodes include the processing capacity constraints of the selected target detection and evaluation center, the processing capacity constraints of the selected target tiered utilization center, and the processing capacity constraints of the selected target detection, dismantling, and remanufacturing plant. The quantity constraints of the multi-target logistics nodes include that the number of selected target testing and evaluation centers, the number of selected target secondary utilization centers, and the number of selected target testing, dismantling, and remanufacturing plants are all greater than or equal to 1; the 0-1 constraints are used to indicate whether the number of selected target testing and evaluation centers, the number of selected target secondary utilization centers, and the selected target testing, dismantling, and remanufacturing plants are upgraded; the non-negative constraints include that the total number of retired batteries, the number of retired batteries processed by the selected target secondary utilization centers, and the number of retired batteries processed by the selected target testing, dismantling, and remanufacturing plants are all greater than or equal to 0.

7. The method for optimizing the reverse logistics network for electric vehicle batteries according to any one of claims 1-6, characterized in that, The carbon tax reverse logistics network model is the minimum value of the sum of the retired battery processing cost model and the carbon tax cost model, and the carbon trading reverse logistics network model is the minimum value of the sum of the retired battery processing cost model and the carbon trading cost model.

8. The method for optimizing the reverse logistics network for electric vehicle batteries according to any one of claims 1-6, characterized in that, After forecasting electric vehicle sales for the target period, the method further includes: The predicted sales figures for trolleybuses during the target period are subjected to residual tests, posterior error tests, and small error probability tests.

9. An optimization device for a reverse logistics network of electric vehicle batteries, characterized in that, include: The sales forecasting module is used to predict the sales volume of electric vehicles within a target period based on historical sales data of electric vehicles. The retirement prediction module is used to determine the number of electric vehicle batteries to be retired during the target period based on the predicted sales volume of electric vehicles and the proportion of electric vehicle batteries to be retired during the target period. The retirement cost module is used to construct a retirement battery processing cost model based on the number of electric vehicle batteries retired within a target period, the recycling price of retired batteries, the location of the proposed multi-target logistics nodes, the facility cost of building the multi-target logistics nodes, the battery processing operation cost, the operating cost of the multi-target logistics nodes, the battery transfer cost, and the waste disposal cost. The carbon emission module is used to determine the total carbon dioxide emissions based on the carbon emissions generated from the construction of multi-target logistics nodes, the carbon emissions generated during the operation of each target logistics node, and the carbon emissions generated during transportation; and to construct a carbon tax cost model or a carbon trading cost model based on the total carbon dioxide emissions and carbon emission taxes or carbon quotas. A model building module is used to build a carbon tax reverse logistics network model or a carbon trading reverse logistics network model based on the retired battery processing cost model, the carbon tax cost model, or the carbon trading cost model. The solution module is used to solve the carbon tax reverse logistics network model or the carbon trading reverse logistics network model based on preset constraints, so as to obtain the optimal electric vehicle battery reverse logistics network.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 8.

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