Energy recovery-based scheduling and facility configuration collaborative optimization method for battery swap mining truck

Abstract

This invention discloses a collaborative optimization method for scheduling and facility configuration of battery-swapping mining trucks based on energy recovery, relating to the field of open-pit mine production scheduling technology. The method includes establishing an energy consumption assessment model for electric mining trucks based on vehicle longitudinal dynamics and considering multiple factors; constructing a collaborative optimization model for open-pit mine battery-swapping station planning and mining truck scheduling based on the mining truck energy consumption assessment model, according to the energy recovery of electric mining trucks and actual production needs; and solving the model using the logic-based Benders decomposition algorithm to optimize the layout of open-pit mine battery-swapping stations, determining the matching relationship between mining trucks and battery-swapping stations, the number of batteries required for battery-swapping stations, and the number of transport shifts between loading and unloading points. Therefore, adopting the above-mentioned collaborative optimization method for scheduling and facility configuration of battery-swapping mining trucks based on energy recovery can effectively improve the working efficiency of electric shovels in open-pit mines, the transportation efficiency of electric mining trucks, reduce transportation costs, and lower the investment cost of battery-swapping station facilities, providing technical support for electric mining truck transportation in open-pit mines.

Description

Technical Field

[0001] This invention relates to the field of open-pit mine production scheduling technology, and in particular to a collaborative optimization method for scheduling and facility configuration of battery-swapping mining trucks based on energy recovery. Background Technology

[0002] Open-pit mining is a typical high-energy-consuming and high-carbon-emission industry. The extraction, transportation, and processing of ore consume large amounts of fuel and electricity, accounting for approximately 10% of global greenhouse gas emissions. Electric mining trucks feature zero emissions and regenerative braking energy recovery, especially on long downhill sections, where their regenerative braking system can significantly reduce power consumption and transportation costs. To achieve green and low-carbon development, the adoption of electric mining trucks has become an important direction for green mining development.

[0003] Electric mining trucks can be recharged through charging and battery swapping. Battery swapping, with its faster swapping speed and flexible station deployment, has gained increasing industry attention and has been piloted in several mining areas. However, existing research on mining truck scheduling largely focuses on traditional fuel-powered vehicles, lacking systematic consideration and modeling of the unique operating characteristics of electric mining trucks (such as energy recovery and battery swapping requirements) and the varying slopes of open-pit mine terrain. This leads to inaccurate energy consumption estimates and scheduling strategies that are difficult to adapt to electrification scenarios. Furthermore, the transportation process for electric mining trucks has shifted from "loading-transporting-unloading" to "loading-transporting-unloading-battery swapping," requiring comprehensive consideration of the location of battery swapping facilities, the number of batteries, and scheduling strategies. Existing research has not adequately addressed this aspect, making it difficult to support the efficient and continuous operation of electric mining trucks.

[0004] Therefore, there is an urgent need for a method that takes into account the energy consumption of electric mining trucks and achieves coordinated optimization of electric mining truck scheduling and battery swapping facility configuration, so as to improve the operating efficiency and energy utilization level of open-pit mine transportation system, support the continuous operation of electric mining trucks and the sustainable development of open-pit mines. Summary of the Invention

[0005] The purpose of this invention is to provide a collaborative optimization method for scheduling and facility configuration of battery-swapping mining trucks based on energy recovery. This method can optimize the layout of battery-swapping stations, battery configuration, and mining truck scheduling, improve the working efficiency of production equipment, reduce transportation and facility investment costs, and provide technical support for the electrification of open-pit mine transportation.

[0006] To achieve the above objectives, this invention provides a collaborative optimization method for scheduling and facility configuration of battery-swapping mining trucks based on energy recovery, comprising the following steps: S1. Based on vehicle longitudinal dynamics, and combined with various factors such as road conditions, vehicle speed, and load, establish an energy consumption assessment model for electric mining trucks. S2. Based on the mining truck energy consumption assessment model, and according to the energy recovery of electric mining trucks and actual production needs, construct a collaborative optimization model for the planning of open-pit mine battery swapping stations and the scheduling of mining trucks. S3. Design a logic-based Benders decomposition algorithm to solve the model, optimize the layout of open-pit mine battery swapping stations, determine the matching relationship between mining trucks and battery swapping stations, the number of batteries that the battery swapping stations need to prepare, and the transportation schedule between loading and unloading points.

[0007] Furthermore, in S1, the energy consumption assessment model for electric mining trucks includes: In the path Energy consumption of electric mining trucks Represented as: ; in, ; ; In the formula, For electric mining trucks passing through the section The cumulative energy consumed during the period For road section The distance between them For battery efficiency, For the efficiency of the motor and transmission system. The weight of the mining card. For the load capacity of mining trucks, For the acceleration of the mining truck, For the resistance of electric mining trucks, For road rolling resistance, For slope resistance, This refers to air resistance.

[0008] Furthermore, in S2, the objective function of the collaborative optimization model for open-pit mine battery swapping station planning and mine truck scheduling includes the idle time cost of electric shovels, battery swapping travel cost, and charging / swapping cost; Among them, the idle time cost of electric shovels for: ; In the formula, The unit time cost of an electric shovel. For electric shovel assembly, To use an electric shovel To the uninstallation point The number of fully loaded transport missions, electric shovel Loading time, electric shovel The corresponding set of unloading points electric shovel Task priority; Battery swapping operating costs for: ; In the formula, This represents the average transportation cost per unit distance when mining trucks are unloaded. To uninstall the credit union, For the collection of battery swapping stations, Should we go to the battery swapping station at the loading / unloading point? Battery swapping , , The distances between the unloading point and the battery swapping station, between the battery swapping station and the electric shovel, and between the unloading point and the electric shovel; Charging and battery swapping costs The calculation is as follows: ; In the formula, For electricity costs, For battery purchase costs; Electricity costs for: ; ; In the formula, For electricity price, The total energy consumption for the current shift to complete its transportation task; Energy consumption to complete all transportation tasks: ; In the formula, for Mining Card from Electric Shovel To the uninstallation point The number of fully loaded transport missions; for Mining cards from unloading point To electric shovel The number of empty transport missions for Mining Card from Electric Shovel To the uninstallation point Electricity consumed for Mining cards from unloading point To electric shovel Electricity consumed; Total energy consumption for battery swapping: ; In the formula, For loading and unloading points Between Number of battery swaps for mining-type cards , For the set of unloading points, A collection of various mining cards; electric shovel With unload point Transportation routes The initial battery level of the mining card. , for Mining card battery capacity, for Mining-type cards from battery swapping stations To electric shovel Electricity consumed; for The additional energy consumption of battery swapping for mining-type cards , , express Mining Cards and Electric Shovels Unload point Whether there is a matching relationship To unload from the point To the battery swapping station Electricity consumed; Battery purchase cost for: ; In the formula, The configuration cost per battery cell, For battery swapping station The number of batteries required; ; ; ; ; In the formula, For the duration of the shift The number of batteries required to be configured for each path within the system. For use between loading and unloading points Number of battery swaps for mining-type cards The time required for an empty battery to be fully charged. Battery swapping for electric mining trucks Threshold, The charging power for the battery swapping station.

[0009] Furthermore, in S3, the logic-based Benders decomposition algorithm divides the collaborative optimization model of open-pit mine battery swapping station planning and mining truck scheduling into the main problem of battery swapping station planning. And the scheduling sub-problem of battery swapping mining cards The expression is as follows: , ; , ; in, , , These are the costs of the electric shovel being idle, the cost of driving while the battery is being swapped, and the cost of charging and swapping the battery. middle represent target value , , , These represent the weighting coefficients for the idle time cost of the electric shovel, the battery swapping travel cost, and the charging / swapping cost, respectively; constraint (1) indicates that the number of battery swapping stations set up is equal to the planned number of battery swapping stations; constraint (2) indicates that all unloading points generating battery swapping demand are assigned to designated battery swapping stations for battery swapping; constraint (3) indicates that mining trucks can only go to locations where battery swapping stations are set up for battery swapping operations; constraint (4) indicates that the remaining power of the mining truck is sufficient for the mining truck to go to the battery swapping station; constraint (5) indicates the upper limit of the number of transportation tasks served by the electric shovel within a work shift; constraint (6) indicates the upper limit of the number of transportation tasks served by the unloading point within a work shift; constraint (7) indicates... The electric shovel needs to meet the planned mining volume. Constraint (8) indicates that the unloading point needs to meet the planned throughput and will not exceed its operating capacity. Constraint (9) indicates that the number of various types of mining trucks required for the current shift does not exceed the available number of such mining trucks. Constraint (10) indicates that the total number of fully loaded transportation tasks for various types of mining trucks is equal to the total number of fully loaded transportation tasks. Constraint (11) indicates that the total number of empty transportation tasks for various types of mining trucks is equal to the total number of empty transportation tasks. Constraint (12) indicates that the number of transportation tasks to the loading and unloading point is the same. Constraint (13) indicates that the number of transportation tasks away from the loading and unloading point is the same. Constraint (15) indicates a binary variable. and The range of values ​​is .

[0010] Furthermore, the logic-based Benders decomposition algorithm solves the model, including the following steps: Step 1: Input road network data, including road network nodes, slope between nodes, and length; mine card data, including mine card quality, load capacity, and windward area parameters; open-pit mine production plan, including planned production volume and planned throughput at loading and unloading points; Step 2: Calculate the shortest path between loading and unloading points using Dijkstra's algorithm, and calculate the energy consumption of each path using the electric mining truck energy consumption evaluation model. Step 3: Solve the master problem of battery swapping station planning. If this is the first time solving the problem, proceed to step 5; otherwise, proceed to step 4. Step 4: Determine if the solution to the main problem has changed. If it has not changed, proceed to step 10; if it has changed, proceed to step 5. Step 5: Update the solution to the battery swapping mining card scheduling subproblem; Step 6: Update the upper and lower bounds; Step 7: Determine whether the algorithm termination condition has been met. ,in To ensure convergence accuracy, if the target is met, proceed to step 12; otherwise, proceed to step 8. Step 8: Determine if there is a solution to the battery swapping mining card scheduling subproblem. If there is a solution, proceed to step 9; if there is no solution, proceed to step 10. Step 9: Add the optimal cut to the main problem, and then execute step 11; Step 10: Add feasible cuts to the main problem, and then proceed to step 11; Step 11: Update the main issue of the battery swapping station planning, and then proceed to step 3; Step 12: Output the optimal solution.

[0011] Therefore, the present invention employs the above-mentioned collaborative optimization method for battery swapping mining truck scheduling and facility configuration based on energy recovery, and has the following technical effects: (1) When realizing the scheduling of electric mining trucks and the planning optimization of battery swapping stations, this invention fully considers the impact of open-pit mine slope and electric mining truck energy recovery on energy consumption, and establishes an energy consumption assessment model for electric mining trucks, which can reasonably assess the energy consumption of electric mining trucks in open-pit mine road networks.

[0012] (2) This invention comprehensively considers the operational capabilities and production needs of electric mining trucks, electric shovels, and unloading points, and constructs a collaborative optimization model for the scheduling of electric mining trucks and the planning of battery swapping stations in open-pit mines. It optimizes the layout of battery swapping stations, the number of spare batteries at each battery swapping station, the charging and swapping matching relationship between mining trucks and battery swapping stations, and the transportation tasks of electric mining trucks at each loading and unloading point.

[0013] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a collaborative optimization method for scheduling and facility configuration of battery-swapping mining trucks based on energy recovery; Figure 2 This is a schematic diagram of the open-pit mine road network in an embodiment of the collaborative optimization method for scheduling and facility configuration of battery-swapping mining trucks based on energy recovery, where X, Y, and Z are longitude, latitude, and elevation, respectively; Figure 3 This is a schematic diagram showing the results of battery swapping station site selection and battery reserve quantity in an embodiment of the collaborative optimization method for battery swapping mining truck scheduling and facility configuration based on energy recovery; Figure 4This is an example of a collaborative optimization method for scheduling and facility configuration of battery-swapping mining trucks based on energy recovery, illustrating the number of battery swaps for electric mining trucks and the battery demand between transportation loading and unloading points. Detailed Implementation

[0015] The present invention will be explained in more detail through the following embodiments. The purpose of disclosing the present invention is to protect all changes and modifications within the scope of the present invention. The present invention is not limited to the following embodiments.

[0016] like Figure 1 As shown, this invention provides a collaborative optimization method for scheduling and facility configuration of battery-swapping mining trucks based on energy recovery. It is applicable to scenarios involving the transportation scheduling of electric mining trucks and the planning of battery-swapping stations during open-pit mine production scheduling. The method includes the following steps: S1. Based on the longitudinal dynamics of the vehicle, the traction force of the electric mining truck With resistance The relationship between them is: ; in, ; ; In the formula, The weight of the mining card. For the load capacity of mining trucks, For the acceleration of the mining truck, For road rolling resistance, For slope resistance, For air resistance, The rolling resistance constant is It is the acceleration due to gravity. air density, The air drag coefficient, The windward area of ​​the mining truck. This refers to the speed of the mining card.

[0017] Based on the above mechanical analysis, and considering the influence of various factors such as road conditions, vehicle speed, and load, an energy consumption assessment model for electric mining trucks is established, as follows: The power required for an electric mining truck to overcome resistance The calculation formula is as follows: ; In the formula, For battery efficiency, For the efficiency of the motor and transmission system.

[0018] When the electric mining truck is traveling downhill, the truck brakes and decelerates, thus recovering energy. Regenerative braking power. Represented as: .

[0019] According to the route passed by the electric mining truck Power during and Accumulated energy consumption for: ; In the formula, For electric mining trucks on the road section The passage time.

[0020] Assuming the average speed of the electric mining truck is ,Right now Road section Through time Electric mining trucks are passing through the section of road. Cumulative energy consumption during the period It can be represented as: ; In the formula, For road section The distance between them.

[0021] Therefore, the path energy consumption for: .

[0022] S2. Based on the mining truck energy consumption assessment model, considering the energy recovery of electric mining trucks and actual production needs, construct a collaborative optimization model for the planning of open-pit mine battery swapping stations and the scheduling of mining trucks.

[0023] Specifically, the objective function of the collaborative optimization model for open-pit mine battery swapping station planning and mine truck scheduling includes three parts: electric shovel idle time cost, battery swapping travel cost, and charging / swapping cost. (1) The formula for calculating the idle time cost of an electric shovel is as follows: ; In the formula, The unit time cost of an electric shovel; To use an electric shovel To the uninstallation point The number of fully loaded transport missions; electric shovel Loading time; electric shovel The corresponding set of unloading points; electric shovel The task priority, with a value range of 100%. A higher value indicates a lower priority.

[0024] (2) The formula for calculating the cost of battery swapping is as follows: ; In the formula, This represents the average transportation cost per unit distance when mining trucks are unloaded. For the set of unloading points, Should we go to the battery swapping station at the loading / unloading point? Battery swapping , , The distances between the unloading point and the battery swapping station, between the battery swapping station and the electric shovel, and between the unloading point and the electric shovel.

[0025] (3) The formula for calculating the cost of charging and battery swapping is as follows: According to the energy consumption assessment model, each path Initial power of mining card The calculation formula is: ; In the formula, for Mining card battery capacity, for The amount of electricity consumed by the mining truck from the battery swapping station to the electric shovel.

[0026] Additional transportation time caused by battery swapping for: ; In the formula, , , This refers to the empty transport time of the mining truck between the electric shovel and the unloading point, between the unloading point and the battery swapping station, and between the battery swapping station and the electric shovel. This refers to the battery swapping time.

[0027] Additional energy consumption due to battery swapping behavior of mining cards for: ; In the formula, , For binary variables, representing Mining Cards and Electric Shovels Unload point Does it have a matching relationship? for Mining cards from unloading point To the battery swapping station Electricity consumed; for Mining cards from unloading point To electric shovel Electricity consumed.

[0028] Time required to fully charge an empty battery for: ; In the formula, Battery swapping for electric mining trucks Threshold, The charging power for the battery swapping station.

[0029] Energy consumption to complete all transportation tasks for: ; In the formula, for Mining Card from Electric Shovel To the uninstallation point The number of fully loaded transport missions, for Mining cards from unloading point To electric shovel The number of empty transport missions for Mining Card from Electric Shovel To the uninstallation point Electricity consumed.

[0030] Between loading and unloading points Number of battery swaps for mining-type cards The calculation formula is: .

[0031] Total energy consumption of battery swapping Represented as: .

[0032] Total energy consumption for the current shift to complete the transportation task for: .

[0033] Electricity costs can be simplified as follows: ; In the formula, For electricity prices.

[0034] Based on shift duration The number of batteries required for each path The calculation formula is: .

[0035] Battery swapping station Number of batteries required The calculation formula is: .

[0036] Battery purchase cost for: ; In the formula, This refers to the configuration cost per battery cell.

[0037] In summary, the cost of charging and battery swapping can be simplified as follows: .

[0038] The constraints of the collaborative optimization model for open-pit mine battery swapping station planning and mine truck scheduling are as follows: Constraint (1) indicates that the number of battery swapping stations set up is equal to the planned number of battery swapping stations, and the expression is: ; In the formula, The candidate set of battery swapping stations includes unloading points and the ten points with the highest repetition rate before the path; The number of battery swapping stations that need to be planned; Representative position Whether to set up a battery swapping station: 1 for yes, 0 for no.

[0039] Constraint (2) indicates that all unloading points generating battery swapping demand are assigned to designated battery swapping stations for battery swapping, and the expression is: .

[0040] Constraint (3) indicates that mining trucks can only travel to locations where battery swapping stations are set up to perform battery swapping operations. The expression is: .

[0041] Constraint (4) indicates that the mining card has enough remaining power to travel to the battery swapping station, and its expression is: .

[0042] Constraint (5) represents the upper limit of the number of transport tasks that an electric shovel can handle within a work shift, and its expression is: .

[0043] Constraint (6) represents the upper limit of the number of transport tasks served by the unloading point within a work shift, where For unloading point Unloading time, expressed as: .

[0044] Constraint (7) indicates that the electric shovel must meet the planned mining volume, and its expression is: ; In the formula, electric shovel The planned production.

[0045] Constraint (8) indicates that the unloading point must meet the planned throughput and will not exceed its operational capacity, and its expression is: ; In the formula, For unloading point The planned throughput.

[0046] Constraint (9) The number of mining cards of each type required for the current shift shall not exceed the available number of mining cards of that type, as expressed in the following expression: ; In the formula, for The number of available mining cards.

[0047] Constraint (10) indicates that the total number of fully loaded transportation tasks for all types of mining trucks equals the total number of fully loaded transportation tasks, expressed as: .

[0048] Constraint (11) indicates that the total number of empty mining truck transportation tasks equals the total number of empty transportation tasks, expressed as: .

[0049] Constraint (12) indicates that the number of transport tasks to and from the loading / unloading points is the same, and its expression is: .

[0050] Constraint (13) indicates that the number of transport tasks at the loading and unloading points is the same, and its expression is: .

[0051] Constraint (14) represents an integer variable. and The range of values ​​for is expressed as: .

[0052] Constraint (15) represents a binary variable. and The range of values ​​for is expressed as: .

[0053] S3. Design a logic-based Benders decomposition algorithm to solve the model, optimize the layout of open-pit mine battery swapping stations, determine the matching relationship between mining trucks and battery swapping stations, the number of batteries that the battery swapping stations need to prepare, and the transportation schedule between loading and unloading points.

[0054] Specifically, the logic-based Benders decomposition algorithm divides the collaborative optimization model of open-pit mine battery swapping station planning and mining truck scheduling into the main problem of battery swapping station planning. And the scheduling sub-problem of battery swapping mining cards The expression is: , ; , ; in, middle represent target value , , , These represent the weighting coefficients for the idle time cost of the electric shovel, the driving cost of battery swapping, and the charging / swapping cost, respectively.

[0055] By solving The optimal solution can be obtained. Determine the lower bound of the model Upper bound of the model can be Confirmed. According to... The solution can generate two types of cuts, as follows: (1) Feasible cutting: like If it is not feasible, then it means An unreasonable solution was provided. Therefore, a feasible cut is added to remove the unreasonable solution, so that a reasonable solution is provided next time. The expression for the feasible cut is: .

[0056] Feasibility guarantee The new solution changes the value of at least one decision variable compared to the previous solution. This ensures that the next solution will produce a different solution.

[0057] (2) Optimal cut: like It is feasible, and optimal value Greater than ,Right now Then, optimal cut boosting needs to be added. lower bound .when The original problem is thus solved optimally. The expression for the optimal cut is: ; The logic-based Benders decomposition algorithm has the following specific solution steps: Step 1: Input road network data, including road network nodes, slope and length between nodes, mine card data, including mine card quality, load, windward area parameters, and open-pit mine production plan, including planned production volume and planned throughput at loading and unloading points. Step 2: Calculate the shortest path between loading and unloading points using Dijkstra's algorithm, and calculate the energy consumption of each path using the electric mining truck energy consumption evaluation model. Step 3: Solve the master problem of battery swapping station planning. If this is the first time solving the problem, proceed to step 5; otherwise, proceed to step 4. Step 4: Determine if the solution to the main problem has changed. If it has not changed, proceed to step 10; if it has changed, proceed to step 5. Step 5: Update the solution to the battery swapping mining card scheduling subproblem; Step 6: Update the upper and lower bounds; Step 7: Determine whether the algorithm termination condition has been met. ,in To ensure convergence accuracy, if the target is met, proceed to step 12; otherwise, proceed to step 8. Step 8: Determine if there is a solution to the battery swapping mining card scheduling subproblem. If there is a solution, proceed to step 9; if there is no solution, proceed to step 10. Step 9: Add the optimal cut to the main problem, and then execute step 11; Step 10: Add feasible cuts to the main problem, and then proceed to step 11; Step 11: Update the main issue of the battery swapping station planning, and then proceed to step 3; Step 12: Output the optimal solution.

[0058] In one embodiment, a Python program is used to write and execute the solution algorithm. By optimizing the above model, the layout scheme of the open-pit mine battery swapping station, the battery swapping matching relationship between the mine truck and the battery swapping station, the number of batteries that each battery swapping station needs to prepare, and the transportation shifts between loading and unloading points can be obtained. Figure 3 The optimized battery swapping station deployment plan and the number of batteries required for each station are presented. Figure 4 This refers to the number of battery swaps required for electric mining truck transportation between loading and unloading points and the number of batteries needed for the lines.

[0059] Therefore, the present invention adopts the above-mentioned energy recovery-based method for the coordinated optimization of scheduling and facility configuration of electric mining trucks, which effectively improves the working efficiency of electric shovels in open-pit mines and the transportation efficiency of electric mining trucks, reduces transportation costs, lowers the investment cost of battery swapping station facilities, and provides technical support for the transportation of electric mining trucks in open-pit mines.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for collaborative optimization of battery swap mine truck scheduling and facility configuration based on energy recovery, characterized in that, Includes the following steps: S1. Based on vehicle longitudinal dynamics, and combined with various factors such as road conditions, vehicle speed, and load, establish an energy consumption assessment model for electric mining trucks. The energy consumption assessment model for electric mining trucks includes: The path Energy consumption of an electric mine truck is represented as: ； in, ； ； In the formula, For electric mining trucks passing through the section The cumulative energy consumed during the period For road section The distance between them For battery efficiency, For the efficiency of the motor and transmission system. The weight of the mining card. For the load capacity of mining trucks, For the acceleration of the mining truck, For the resistance of electric mining trucks, For road rolling resistance, For slope resistance, For air resistance; S2. Based on the mining truck energy consumption assessment model, and according to the energy recovery of electric mining trucks and actual production needs, construct a collaborative optimization model for the planning of open-pit mine battery swapping stations and the scheduling of mining trucks. The objective function of the collaborative optimization model for planning and scheduling of open-pit mine battery swapping stations includes the idle time cost of electric shovels, the battery swapping travel cost, and the charging and swapping cost. Wherein, the idle time cost of the electric shovel is: ； In the formula, The unit time cost of an electric shovel. For electric shovel assembly, For shift duration, To use an electric shovel To the uninstallation point The number of fully loaded transport missions, electric shovel Loading time, electric shovel The corresponding set of unloading points electric shovel Task priority; Battery swap travel cost Is: ； In the formula, This represents the average transportation cost per unit distance when mining trucks are unloaded. For the set of unloading points, For the collection of battery swapping stations, Should we go to the battery swapping station at the loading / unloading point? Battery swapping , , The distances between the unloading point and the battery swapping station, between the battery swapping station and the electric shovel, and between the unloading point and the electric shovel; Cost of charging and replacing The calculation is as follows: ； In the formula, For electricity costs, For battery purchase costs; Electricity costs Is: ； ； In the formula, For electricity price, The total energy consumption for the current shift to complete its transportation task; Energy consumption to complete all transportation tasks: ； In the formula, A collection of various mining cards, for Mining Card from Electric Shovel To the uninstallation point The number of fully loaded transport missions. for Mining cards from unloading point To electric shovel The number of empty transport missions for Mining Card from Electric Shovel To the uninstallation point Electricity consumed for Mining cards from unloading point To electric shovel Electricity consumed; Total energy consumption for battery replacement behavior: ； In the formula, For loading and unloading points Between Number of battery swaps for mining-type cards ; Battery swapping for electric mining trucks Threshold, electric shovel With unload point Transportation routes The initial battery level of the mining card. , for Mining card battery capacity, for Mining-type cards from battery swapping stations To electric shovel Electricity consumed; for The additional energy consumption of battery swapping for mining-type cards , , express Mining Cards and Electric Shovels Unload point Does it have a matching relationship? for Mining cards from unloading point To the battery swapping station Electricity consumed; Battery acquisition cost Is: ； In the formula, For the configuration cost of a single battery, For battery swapping station The number of batteries required; ； ； ； In the formula, For the duration of the shift The number of batteries required to be configured for each path within the system. The time required for an empty battery to be fully charged. The charging power for the battery swapping station; S3. Design a logic-based Benders decomposition algorithm to solve the model, optimize the layout of open-pit mine battery swapping stations, determine the matching relationship between mining trucks and battery swapping stations, the number of batteries that the battery swapping stations need to prepare, and the transportation schedule between loading and unloading points.

2. The energy recovery-based swap battery mine truck scheduling and facility configuration collaborative optimization method according to claim 1, characterized in that, In S3, the logic-based Benders decomposition algorithm divides the collaborative optimization model of open-pit mine battery swapping station planning and mining truck scheduling into the main problem of battery swapping station planning. And the scheduling sub-problem of battery swapping mining cards The expression is as follows: ， ； ， ； in, , , These are the costs of the electric shovel being idle, the cost of driving while the battery is being swapped, and the cost of charging and swapping the battery. middle represent target value , , , These represent the weighting coefficients for the idle time cost of the electric shovel, the battery swapping travel cost, and the charging / swapping cost, respectively; constraint (1) indicates that the number of battery swapping stations set up is equal to the planned number of battery swapping stations; constraint (2) indicates that all unloading points generating battery swapping demand are assigned to designated battery swapping stations for battery swapping; constraint (3) indicates that mining trucks can only go to locations where battery swapping stations are set up for battery swapping operations; constraint (4) indicates that the remaining power of the mining truck is sufficient for the mining truck to go to the battery swapping station; constraint (5) indicates the upper limit of the number of transportation tasks served by the electric shovel within a work shift; constraint (6) indicates the upper limit of the number of transportation tasks served by the unloading point within a work shift; constraint (7) indicates... The electric shovel needs to meet the planned mining volume. Constraint (8) indicates that the unloading point needs to meet the planned throughput and will not exceed its operating capacity. Constraint (9) indicates that the number of various types of mining trucks required for the current shift does not exceed the available number of such mining trucks. Constraint (10) indicates that the total number of fully loaded transportation tasks for various types of mining trucks is equal to the total number of fully loaded transportation tasks. Constraint (11) indicates that the total number of empty transportation tasks for various types of mining trucks is equal to the total number of empty transportation tasks. Constraint (12) indicates that the number of transportation tasks to the loading and unloading point is the same. Constraint (13) indicates that the number of transportation tasks away from the loading and unloading point is the same. Constraint (15) indicates a binary variable. and The range of values ​​is , Should we go to the battery swapping station at the loading / unloading point? Battery swapping Indicates position Whether to set up a battery swapping station.

3. The method for coordinated optimization of battery swapping mining truck scheduling and facility configuration based on energy recovery as described in claim 2, characterized in that, The logic-based Benders decomposition algorithm solves the model, including the following steps: Step 1: Input road network data, including road network nodes, slope between nodes, and length; mine card data, including mine card quality, load capacity, and windward area parameters; open-pit mine production plan, including planned production volume and planned throughput at loading and unloading points; Step 2: Calculate the shortest path between loading and unloading points using Dijkstra's algorithm, and calculate the energy consumption of each path using the electric mining truck energy consumption evaluation model; Step 3: Solve the master problem of battery swapping station planning. If this is the first time solving the problem, proceed to step 5; otherwise, proceed to step 4. Step 4: Determine if the solution to the main problem has changed. If it has not changed, proceed to step 10; if it has changed, proceed to step 5. Step 5: Update the solution to the battery swapping mining card scheduling subproblem; Step 6: Update the upper and lower bounds; Step 7: Determine whether the algorithm termination condition has been met. ,in To ensure convergence accuracy, if the target is met, proceed to step 12; otherwise, proceed to step 8. Step 8: Determine if there is a solution to the battery swapping mining card scheduling subproblem. If there is a solution, proceed to step 9; if there is no solution, proceed to step 10. Step 9: Add the optimal cut to the main problem, and then execute step 11; Step 10: Add feasible cuts to the main problem, and then proceed to step 11; Step 11: Update the main issue of the battery swapping station planning, and then proceed to step 3; Step 12: Output the optimal solution.

Images