A method and system for dispatching a hybrid fleet of airport tractors

Abstract

The present invention discloses a method and system for dispatching a hybrid fleet of airport tractors. The method includes obtaining the type and function of the airport tractors and confirming the working rules of the airport tractors; obtaining different types of vehicle energy consumption models based on the functions of the airport tractors; optimizing and analyzing the dispatching of the hybrid fleet of airport tractors, setting the total operating cost as the objective function, determining the constraints, and constructing a dispatching model for the hybrid fleet of airport tractors; designing an improved adaptive large neighborhood search algorithm, and solving the dispatching model of the hybrid fleet of airport tractors to obtain the dispatching task allocation and vehicle schedule after the total operating cost is optimized. The present invention effectively reduces the operating costs and emissions of the airport, improves the security efficiency, and reduces flight delays. At the same time, by comparing the costs under different vehicle ratios, the optimal vehicle configuration of the airport under different angles can be obtained, providing reference data for the configuration of the airport fleet.

Description

Technical Field

[0001] The invention belongs to the technical field of airport operation and management, and in particular relates to a method and system for dispatching an airport tractor oil-electric hybrid fleet. Background Art

[0002] In recent years, the global warming problem caused by large-scale greenhouse gas emissions has become increasingly serious, and the issue of carbon dioxide emission reduction has received widespread attention. Ground support vehicles are one of the main sources of carbon emissions at airports. In order to reduce carbon emissions on the ground at airports, we should accelerate the gradual replacement of airport fuel vehicles with electric vehicles.

[0003] At present, the research on airport tractor dispatching at home and abroad mainly focuses on pure fuel fleets or pure electric fleets. The research on hybrid oil-electric vehicles is mostly based on the logistics industry, and the energy consumption of electric vehicle services is not considered. There are few studies on the dispatching of airport tractor hybrid oil-electric fleets, and a relatively mature, scientific and reasonable theoretical method for dispatching tractor hybrid oil-electric fleets has not yet been formed. Summary of the invention

[0004] The technical problem to be solved by the present invention is: to propose a method and system for dispatching an airport tractor oil-electric hybrid fleet, which can obtain the optimal vehicle configuration of the airport from different angles by comparing the costs under different vehicle ratios, provide reference data for airport fleet configuration, and provide optimization solutions for airport tractor dispatch under pure fuel operation, oil-electric hybrid operation and pure electric operation, effectively reduce the airport's operating costs and emissions, improve security efficiency, and reduce flight delays.

[0005] The present invention adopts the following technical solutions to solve the above technical problems:

[0006] A method for dispatching a hybrid fleet of airport tractors, comprising:

[0007] S1. Clarify the types and functional composition of airport tractors and confirm the general working rules of airport tractors.

[0008] S2. Based on the functions of airport tractors, energy consumption models of different categories of vehicles are obtained.

[0009] S3. Optimize the scheduling of the airport tractor hybrid fleet, set the total operating cost as the objective function, determine the constraints, and build an airport tractor hybrid fleet scheduling model.

[0010] S4. Design an improved adaptive large neighborhood search algorithm to solve the airport tractor hybrid fleet scheduling model to obtain the scheduling task allocation and vehicle schedule after optimizing the total operating cost.

[0011] S5. Input the case data under the set fleet configuration into the airport tractor hybrid fleet scheduling optimization model and solve it to obtain the objective function value and the optimized scheduling plan.

[0012] Furthermore, in step S1, the types of airport tractor vehicles are classified according to energy consumption types, including but not limited to fuel vehicles and electric vehicles, among which electric tractors have both aircraft towing and APU service replacement functions; the service objects of the airport tractor are departing flights, and the work content is to drive along the vehicle road to the corresponding parking position within a predetermined time window and provide traction service for a certain period of time.

[0013] Furthermore, in step S3, the goal of the airport tractor hybrid fleet scheduling model is to complete the launch task of all flights within the planned time window, and to achieve and minimize the total operating cost. The operating cost includes three parts: energy consumption cost, time cost and emission cost. The parameters used in the model are explained below:

[0014] Airport tractor operation directed network G, G = (V' 0,N+1 ,A); V = {1,...N} represents the parking space node set; F represents the charging pile set; F' represents the access charging pile set; V' represents the union of V and F'; nodes 0 and N+1 represent garages, where 0 represents the outbound node and N+1 represents the return node; A is the arc set, A = {(i,j)|i,j∈V' 0,N+1 ,i≠j}; M represents the set of tractors.

[0015] If two nodes appear as the subscript of a set, it means that the set not only represents the node set itself, but also includes the garage node indicated by the subscript. For example, V'0 represents the set of V, F' and garage node 0.

[0016] According to the airport support vehicle operation management regulations, the tractor needs to be within the time window [e i ,l i ] to the parking space node i∈V and provide push-out service. The service duration of each push-out vehicle t is s it , the driving speed during the push-out period is v it , the energy consumption is b i and f i The tractor fleet consists of n E Electric tractors and ICThe maximum battery capacity of the tractor is B, and the minimum power threshold is p%. When the power is lower than the minimum power threshold, it needs to go to the charging station to charge, and the charging rate is r. In this paper, a full charging strategy is adopted, that is, each time it is fully charged, it will run again, so the charging time depends on the charging rate r and the battery power consumed. The attributes of each arc in set A include: distance d ij , the speed v of vehicle t ijt , driving time t ijt =d ij / v ijt , energy consumption b ijt (electric tractor) and f ijt (Fuel tractor). For each arc (i, j) ∈ A, this paper defines the following decision variables: If tractor t passes through arc (i, j), then variable x ijt is 1, otherwise it is 0. it and it Respectively represent the arrival of tractor t at node i∈V' 0,N+1 time and remaining power.

[0017] The objective function of the airport tractor hybrid fleet scheduling model is:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] In the above formula, f tm represents the time cost function, c (EM) represents the cost per unit time, represents the electricity cost function, represents the unit electricity cost, represents the fuel cost function, represents the unit fuel cost, represents the emission cost function, c (EM) represents the unit carbon emission cost, c em represents the carbon emissions per unit distance, v jt represents the towing speed of the t-th tractor at the j-th parking lot node, V'0 represents the set of V, F' and parking lot node 0, and f obj represents the overall objective function, x iN+1t Indicates whether the t-th tractor passes through arc (i, N+1), tiN+1t represents the time that the t-th tractor travels on the arc (i, N+1), x 0it Indicates whether the t-th tractor passes through the arc (0, t), t 0it represents the time that the t-th tractor travels on the arc (0, t), V' N+1 Represents a set of N+1 and parking bay nodes.

[0024] The constraints of the airport tractor hybrid fleet scheduling model are:

[0025] P ijt (m t )=(0.5·ρ α ·A f ·c d ·(v ijt ) 2 +c r ·m t ·g)·v ijt

[0026] P jt (m t +m a )=(0.5·ρ α ·A f ·c d ·(v jt ) 2 +c r ·(m t +m a )·g)·v jt

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] Among them, ρ α Represents the air density, A f represents the front area of ​​the tractor, c d represents the aerodynamic drag coefficient, v ijt represents the towing speed of the t-th tractor on the arc (i, j), c r represents the rolling friction coefficient, g represents the gravity constant, x 0jt Indicates whether the t-th tractor is traveling on the arc (0, j), l i represents the latest service start time of the i-th parking space node, τ jt represents the time when the t-th tractor arrives at the j-th parking node, V0 represents the parking node set including the departure garage node, e i represents the earliest service start time of the i-th parking lot node i, F'0 represents the charging pile node set including the departure garage node, x qjt Indicates whether the t-th tractor is traveling on the arc (0, j), b it represents the service energy consumption of the t-th tractor at parking position node i, y jt represents the remaining energy consumption of the t-th tractor at the j-th parking position node, V' 0,N+1 represents the parking space node set including the departure and arrival garage nodes, e0 represents the earliest opening time of the garage, l N+1 Indicates the latest closing time of the garage.

[0042] Furthermore, in step S4, ALNS (Adaptive Large Neighborhood Search) is improved. The detailed steps of the improved algorithm are as follows:

[0043] S401, generate an initial solution. Select the earliest n flights as the first task of the n tractors, assign the unassigned flight tasks to each tractor according to the principle of minimizing the cost function, and arrange for vehicles with insufficient power to go to charging piles; ignore all charging points, sort the routes in descending order according to the total value of the time window and power constraints after ignoring them, and select the first n IC The tractor path is converted into a fuel vehicle path by removing the charging station node.

[0044] S402: Randomly select the number of nodes to be operated, and select the destroy and repair operators according to the weight probability, reconstruct the original nodes, and generate a new solution.

[0045] S403, constructing the evaluation function and penalty function of the solution according to the objective function and constraint conditions of the airport tractor hybrid fleet scheduling model, and using the SA (Simulated Annealing) criterion to determine whether to accept the solution as the optimal solution.

[0046] S404: Determine whether the maximum number of iterations has been reached. If not, update the operator selection probability weight according to the solution acceptance result and go to step S402; otherwise, output the optimal solution to obtain the vehicle path planning result x under the oil-electric hybrid operation under the total operation cost optimization. ijt .

[0047] Furthermore, in step S5, the case data under the set fleet configuration is input into the airport tractor hybrid fleet scheduling optimization model and solved, so as to obtain the optimal scheduling scheme and its corresponding cost under the configuration. By adjusting parameters such as vehicle ratio and repeating the calculation, the time, energy consumption, emission cost and delay duration of different vehicle ratio schemes are compared and analyzed to determine the optimal vehicle ratio in a certain scenario. In combination with the scheduling results in scenarios of different scales, the influence of scenario characteristics on the scheduling results is analyzed.

[0048] Furthermore, the present invention also proposes an airport tractor oil-electric hybrid fleet dispatching system, comprising:

[0049] The traction vehicle performance acquisition module is used to obtain the type and function of the airport traction vehicle and confirm the working rules of the airport traction vehicle.

[0050] The vehicle energy consumption model building module is used to obtain different types of vehicle energy consumption models based on the functions of airport tractors.

[0051] The airport tractor oil-electric hybrid fleet scheduling model construction module is used to optimize the airport tractor oil-electric hybrid fleet scheduling, set the total operating cost as the objective function, determine the constraints, and construct the airport tractor oil-electric hybrid fleet scheduling model.

[0052] The scheduling scheme optimization module is used to design an improved adaptive large neighborhood search algorithm. By solving the airport tractor hybrid fleet scheduling model, the scheduling task allocation and vehicle schedule after the total operating cost is optimized are obtained.

[0053] Furthermore, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for dispatching an airport tractor hybrid fleet when executing the computer program.

[0054] Furthermore, the present invention also proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for dispatching an airport tractor hybrid fleet is executed.

[0055] The present invention adopts the above technical solution, and compared with the prior art, its significant technical effects are as follows:

[0056] The present invention clarifies the types, functions and working processes of airport aircraft tractors, describes in detail the energy consumption calculation method of aircraft tractors, establishes a tractor scheduling optimization model with the minimum total operating cost, improves the adaptive large neighborhood search algorithm suitable for oil-electric hybrid vehicle scheduling, and proposes a quantitative fleet ratio decision method, which provides a theoretical basis for the scheduling of oil-electric hybrid fleets of airport aircraft tractors, and is conducive to improving airport operating efficiency and reducing airport operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of the overall implementation of the present invention.

[0058] Figure 2 The figure is an overall implementation flow chart of the improved adaptive large neighborhood search algorithm of the present invention.

[0059] Figure 3 This is a network diagram of the two-car operation scenario of an embodiment of the present invention.

[0060] Figure 4 This is a graph showing how the operating cost changes with the proportion of electric vehicles in two scenarios of an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0062] The present invention proposes a method for dispatching a hybrid fleet of airport tractors. Figure 1 As shown, including:

[0063] S1. Obtain the type and function of the airport tractor and confirm the working rules of the airport tractor, including:

[0064] Airport tractor vehicle types are classified according to energy consumption types, including fuel tractors and electric tractors.

[0065] Among them, electric tractors have two functions: aircraft towing and APU service replacement; airport tractors are responsible for aircraft towing at the airport. The aircraft tractors put into use start from the parking area, drive to the designated aircraft stand to provide aircraft towing services for departing flights, and finally return to the parking area. According to the airport support vehicle operation management regulations, aircraft tractors should drive on the airport's designated vehicle roads and arrive at the designated aircraft stand 10 minutes before the flight departs to prepare for service.

[0066] S2. Based on the accurate calculation of vehicle endurance, based on the mechanical model considering rolling friction resistance and aerodynamic resistance, and based on the function of the airport tractor, the energy consumption models of different types of vehicles are obtained. The specific contents are as follows:

[0067] The vehicle energy consumption model includes the electric tractor energy consumption model and the fuel tractor energy consumption model, which is built on the mechanical energy calculation model.

[0068] Among them, the specific expression of the mechanical energy calculation model is:

[0069] F r =c r ·m t g cos(α)

[0070] F a =0.5·ρ α ·A f ·c d ·v 2

[0071] P M =(m t ·a+0.5·ρ α ·A f ·c d ·v 2 +m t g sin(α)+c r ·m t ·g·cos(α))·v

[0072] P ijt (m t )=(0.5·ρ α ·A f ·c d ·(v ijt ) 2 +c r ·m t ·g)·v ijt

[0073] Pjt (m t +m a )=(0.5·ρ α ·A f ·c d ·(v jt ) 2 +c r ·(m t +m a )·g)·v jt

[0074] Among them, F r is the friction resistance, F a is the aerodynamic resistance, P M is the total mechanical energy, P ijt (m t ) is the mechanical energy required for the t-th vehicle to travel on the arc (i, j), P jt (m t +m a ) is the mechanical energy required for service at the parking stand, c r is the rolling friction coefficient, m t is the mass of the tractor, g is the gravity constant, and α is the road slope angle. α is the air density, A f is the front area of ​​the tractor, c d is the aerodynamic drag coefficient, v is the speed. a is the acceleration of the tractor, and since the vehicle in this paper is assumed to be traveling at a constant speed, its value is 0. m a is the weight of the aircraft towed by the tractor, v ijt is the drag speed of vehicle t on arc (i, j), v jt is the towing speed of the t-th vehicle at parking position node j. In this embodiment, sin(α)≈0, cos(α)≈1.

[0075] The specific expression of the energy consumption model of electric tractor is:

[0076]

[0077]

[0078]

[0079]

[0080] Where b ijt Indicates driving energy consumption, Indicates the towing energy consumption of the electric tractor. Indicates APU replacement energy consumption, b jt represents the service energy consumption, φ represents the conversion efficiency from electrical energy to mechanical energy, Indicates the discharge efficiency of the battery, t ijt represents the travel time of the t-th tractor on arc (i, j), M E Represents the electric tractor set, s jt represents the service time of the t-th tractor at the j-th parking position node, E AIR It is the power consumed by the aircraft air conditioning system, T AIR Indicates the time of providing power to the aircraft air conditioning system, E ILL It is the power consumed by the aircraft lighting system, T ILL Indicates the time of providing power to the aircraft lighting system, E LAU It is the power consumed by the aircraft engine starting, T LAU Indicates the time that power is provided to start the aircraft engines.

[0081] The specific expression of the fuel tractor energy consumption model is:

[0082]

[0083]

[0084] In the formula, f ijt represents the driving energy consumption of a fuel vehicle, f jt represents the service energy consumption of fuel vehicles, ξ represents the fuel-air mass ratio, κ represents the calorific value of fuel, ψ represents the unit conversion coefficient, k represents the engine friction coefficient, N e represents the engine speed, D represents the engine displacement, η represents the fuel engine efficiency parameter, η tf Represents the transmission system efficiency parameter, M IC Represents a collection of fuel tractors, It represents the towing energy consumption of the t-th tractor at the j-th parking position node.

[0085] S3. Optimize and analyze the dispatch of the airport tractor oil-electric hybrid fleet, set the total operating cost as the objective function, determine the constraints, and build an airport tractor oil-electric hybrid fleet dispatch model. The specific contents are as follows:

[0086] S301, set parameters: airport tractor operation directed network G, G = (V' 0,N+1 ,A); V = {1,...N} represents the parking space node set; F represents the charging pile set; F' represents the access charging pile set; V' represents the union of V and F'; nodes 0 and N+1 represent garages, 0 represents the outbound node, and N+1 represents the return node; A represents the arc set, A = {(i,j)|i,j∈V' 0,N+1 ,i≠j},; M represents the set of tractors.

[0087] S302, during the launch of the service, the tractor fleet consists of n E Electric tractors and IC Composed of fuel tractors, n E +n IC =n;s it represents the duration of the t-th tractor, v it represents the driving speed, b i and f i represents the push-out energy consumption, B represents the maximum battery capacity of the tractor, p% represents the minimum charge threshold, r represents the charging rate, τ it represents the time when the t-th tractor arrives at the node, y it Represents the remaining power when the t-th tractor arrives at the node.

[0088] x ijt is a decision variable, indicating whether vehicle t passes through arc (i, j); if the t-th tractor passes through arc (i, j), the variable xijt is 1, otherwise it is 0.

[0089] S303. The specific expression of the objective function is:

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] In the formula, f tm represents the time cost function, c (TM) represents the cost per unit time, represents the electricity cost function, represents the unit electricity cost, represents the fuel cost function, represents the unit fuel cost, represents the emission cost function, c (EM) represents the unit carbon emission cost, c em Represents the carbon emissions per unit distance, d ij represents the distance of each arc, v jt represents the towing speed of the t-th tractor at the j-th parking lot node, V'0 represents the set of V, F' and parking lot node 0, and f obj represents the overall objective function, x iN+1t Indicates whether the t-th tractor passes through arc (i, N+1), t iN+1trepresents the time that the t-th tractor travels on the arc (i, N+1), x 0it Indicates whether the t-th tractor passes through the arc (0, t), t 0it represents the time that the t-th tractor travels on the arc (0, t), V' N+1 Represents a set of N+1 and parking bay nodes.

[0096] S304. The specific expression of the constraint condition is:

[0097] P ijt (m t )=(0.5·ρ α ·A f ·c d ·(v ijt ) 2 +c r ·m t ·g)·v ijt (6);

[0098] P jt (m t +m a )=(0.5·ρ α ·A f ·c d ·(v jt ) 2 +c r ·(m t +m a )·g)·v jt (7);

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] Among them, x 0jt Indicates whether the t-th tractor is traveling on the arc (0, j), l i represents the latest service start time of the i-th parking space node, τ jt represents the time when the t-th tractor arrives at the j-th parking node, V0 represents the parking node set including the departure garage node, e i represents the earliest service start time of the i-th parking lot node i, F'0 represents the charging pile node set including the departure garage node, x qjt Indicates whether the t-th tractor is traveling on the arc (0, j), b it represents the service energy consumption of the t-th tractor at parking position node i, y jt represents the remaining energy consumption of the t-th tractor at the j-th parking position node, V' 0,N+1 represents the parking space node set including the departure and arrival garage nodes, e0 represents the earliest opening time of the garage, l N+1 Indicates the latest closing time of the garage.

[0114] Formula (1) represents the objective function, which is to minimize the sum of the time cost, energy cost and emission cost of electric tractors and fuel tractors; Formula (2) represents the calculation of the time cost of the tractor; Formula (3) represents the calculation of the energy cost of the electric tractor; Formula (4) represents the calculation of the energy cost of the fuel tractor; Formula (5) is the calculation of the emission cost; Formulas (6-10) are the energy consumption model established in step 2; Formula (11) limits each service to only one electric vehicle or fuel vehicle; Formula (12) ensures that the number of times an electric tractor enters each node is equal to the number of times it departs from the node; Formulas (13) and (14) ensure that the number of fuel or electric tractors called is not greater than the total number of vehicle configurations; Formula (15) ensures that when a tractor travels from a parking space or garage to any other node, the time it takes to reach the next node is greater than the time in the previous section. The formula (16) ensures that when the tractor goes from the charging station node to any other node, the time to reach the next node is greater than the sum of the arrival time, charging time and driving time of the previous node, where the charging time is linearly related to the power consumption; the formula (17) ensures that the time when the tractor arrives at the parking position node is within the planned time window; the formula (18) ensures that when the electric tractor goes from the parking position node to any node, the arrival power of the next node is less than the arrival power of the previous node minus the service and driving energy consumption, and if it starts from the charging station, the power is fully charged; the formula (19) ensures that the power of the electric vehicle at each node cannot exceed the battery capacity and cannot be lower than the minimum power threshold; the formula (20) defines the 0,1 decision variable; the formula (21) sets the value range of the vehicle arrival time variable.

[0115] S4, such as Figure 2 As shown in the figure, an improved adaptive large neighborhood search algorithm is designed. By solving the airport tractor oil-electric hybrid fleet scheduling model, the scheduling task allocation and vehicle schedule after the total operating cost is optimized are obtained, which solves the problem of difficult search for feasible solutions and easy to fall into local optimality. The specific contents are as follows:

[0116] S401, generate an initial solution. Select the earliest n flights as the first task of the n tractors, assign the unassigned flight tasks to each tractor according to the principle of minimizing the cost function, and arrange for vehicles with insufficient power to go to charging piles; ignore all charging points, sort the routes in descending order according to the total value of the time window and power constraints after ignoring them, and select the first n IC The tractor path is converted into a fuel vehicle path by removing the charging station node.

[0117] S402: Randomly select the number of nodes to be operated, and select the destroy and repair operators according to the weight probability, reconstruct the original nodes, and generate a new solution.

[0118] S403, constructing an evaluation function and a penalty function of a solution according to the objective function and constraint conditions of the airport tractor hybrid fleet scheduling model, and using the SA criterion to determine whether to accept the solution as the optimal solution.

[0119] S404: Determine whether the maximum number of iterations has been reached. If not, update the operator selection probability weight according to the solution acceptance result and go to step S402; otherwise, output the optimal solution to obtain the vehicle path planning result x under the oil-electric hybrid operation under the total operation cost optimization. ijt .

[0120] S5. Input the case data under the set fleet configuration into the airport tractor hybrid fleet scheduling model to obtain the objective function value and the optimized scheduling plan. The specific contents are as follows:

[0121] The case data under the set fleet configuration is input into the airport tractor hybrid fleet scheduling model to obtain the optimal scheduling plan and its corresponding cost under this configuration. By adjusting parameters such as vehicle ratio and repeating the calculation, the time, energy consumption, emission cost and delay duration of different vehicle ratio schemes are compared and analyzed to determine the optimal vehicle ratio in a certain scenario. Combined with the scheduling results in scenarios of different scales, the impact of scenario characteristics on the scheduling results is analyzed.

[0122] This example takes a hub airport as the research object and constructs two scenarios of different sizes. The network structure is as follows: Figure 3 As shown, Figure 3 (a) shows the network structure diagram under scenario 1. Figure 3 (b) shows the network structure diagram under scenario 2. According to the airport scene design diagram and the flight plan for a certain day, the airport parking stand nodes, vehicle parking nodes, road network scale and flight distribution characteristics can be obtained. The detailed data of the two scenarios are shown in Table 1.

[0123] Table 1 Characteristics of scenario 1 and scenario 2

[0124]

[0125] Based on the vehicle data of a certain support vehicle service provider at the airport, Table 2 shows the basic parameters of fuel and electric tractors. Unlike electric tractors, fuel tractors do not consider the energy capacity and replenishment process, mainly because fuel vehicles have long endurance and short refueling time. In addition, fuel tractors and electric tractors have different vehicle masses, energy consumption rate models, idling rates, towing rates, and energy costs.

[0126] Table 2 Parameters of vehicles used at an airport

[0127]

[0128]

[0129] Other parameters are shown in Table 3.

[0130] Table 3 Remaining parameters

[0131]

[0132] Taking scenario 2 as an example, the number of vehicles is set to 6, and the ratio of oil-electric vehicles is 1:1. The scheduling plan obtained by solving the model is shown in Table 4.

[0133] Table 4 Scheduling scheme for a certain airport under scenario 2

[0134]

[0135] By adjusting the fuel-electric ratio of the fleet and repeating the calculation, the total cost changes and individual cost components under different schemes are as follows: Figure 4 As shown, Figure 4 (a) reflects the sensitivity analysis results under scenario 1. It can be seen that the total operating cost is the lowest when pure electric vehicles are used in scenario 1. Figure 4 (b) reflects the sensitivity analysis results under scenario 2. Under scenario 2, the total operating cost is the lowest when the ratio of gasoline vehicles to electric vehicles is 1:1.

[0136] Table 5 shows the average flight delay time under different vehicle ratios in scenario 1 and scenario 2. The delay index is obtained by dividing the total number of violations of the time window constraints in the scheduling plan by the total number of flights.

[0137] Table 5 Changes in delays in different dispatching scenarios at an airport with the proportion of electric vehicles

[0138]

[0139] In general, the delay time in both scenarios increases with the increase of the proportion of electric vehicles, mainly because electric vehicles using a full charging strategy cannot leave the charging station during charging, reducing the total number of available vehicles in some time periods. Among them, scenario 2 is more affected by vehicle electrification and is more prone to operational delays. When the two scenarios adopt the optimal vehicle ratio strategy, the average flight delay time in scenario 1 is about 4 times that of scenario 2, but after full electrification, the average flight delay time in scenario 2 is 1.6 times that of scenario 1. Mainly due to the obvious peak hours of flights in scenario 2, under the full charging strategy, the pure electric fleet is constrained by the charging time and it is difficult to meet the service needs during peak hours. Therefore, for airports with front-row terminals and obvious peak characteristics, if you want to avoid the impact of electrification on flight punctuality, it is recommended to use vehicles with larger battery capacity, or increase the ratio of fast charging piles to reduce delays caused by insufficient capacity.

[0140] The embodiment of the present invention also proposes an airport tractor oil-electric hybrid fleet dispatching system, including a tractor performance acquisition module, a vehicle energy consumption model construction module, an airport tractor oil-electric hybrid fleet dispatching model construction module, a dispatching scheme optimization module, and a computer program that can be run on a processor. It should be noted that each module in the above system corresponds to the specific steps of the method provided in the embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0141] The embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. It should be noted that when the processor executes the computer program, the specific steps of the method provided in the embodiment of the present invention are corresponding to the specific steps of the method provided in the embodiment of the present invention, and the processor has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0142] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. It should be noted that when the computer program is executed by the processor, it corresponds to the specific steps of the method provided in the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0143] The present invention can provide an optimization solution for the dispatch of airport tractors under pure fuel operation, hybrid operation and pure electric operation, effectively reducing the airport's operating costs and emissions, improving security efficiency, and reducing flight delays. At the same time, by comparing the costs under different vehicle ratios, the optimal airport vehicle configuration under different angles can be obtained, providing reference data for airport fleet configuration, which has certain practical significance.

[0144] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for dispatching a hybrid fleet of airport tractors, characterized in that: include: S1. Obtain the type and function of the airport tractor and confirm the working rules of the airport tractor; S2. Based on the functions of airport tractors, energy consumption models of different types of vehicles are obtained; the specific contents are as follows: The vehicle energy consumption model includes the electric tractor energy consumption model and the fuel tractor energy consumption model, which is built on the mechanical energy calculation model; The specific expression of the energy consumption model of electric tractor is: Where b ijt Indicates driving energy consumption, Indicates the towing energy consumption of the electric tractor. Indicates APU replacement energy consumption, b jt represents the service energy consumption, P ijt (m t ) represents the mechanical energy required for the t-th tractor to travel on the arc (i, j), φ represents the conversion efficiency from electrical energy to mechanical energy, Indicates the discharge efficiency of the battery, t ijt represents the travel time of the t-th tractor on arc (i, j), M E represents the electric tractor set, P jt (m t +m a ) represents the mechanical energy required for service at the parking stand, m t Indicates the weight of the tractor, m a Indicates the weight of the aircraft towed by the tractor, s jt represents the service time of the t-th tractor at the j-th parking position node, E AIR It is the power consumed by the aircraft air conditioning system, T AIR Indicates the time of providing power to the aircraft air conditioning system, E ILL It is the power consumed by the aircraft lighting system, T ILL Indicates the time of providing power to the aircraft lighting system, E LAU It is the power consumed by the aircraft engine starting, T LAU Indicates the time that power is provided to start the aircraft engine; The specific expression of the fuel tractor energy consumption model is: In the formula, f ijt represents the driving energy consumption of a fuel vehicle, f jt represents the service energy consumption of fuel vehicles, ξ represents the fuel-air mass ratio, κ represents the calorific value of fuel, ψ represents the unit conversion coefficient, k represents the engine friction coefficient, N e represents the engine speed, D represents the engine displacement, η represents the fuel engine efficiency parameter, η tf Represents the transmission system efficiency parameter, M IC Represents a collection of fuel tractors, represents the towing energy consumption of the t-th tractor at the j-th parking position node; S3. Optimize and analyze the dispatch of the airport tractor oil-electric hybrid fleet, set the total operating cost as the objective function, determine the constraints, and build an airport tractor oil-electric hybrid fleet dispatch model; the specific contents are: S301, set parameters: airport tractor operation directed network G, G = (V' 0,N+1 ,A); V = {1,...N} represents the parking space node set; F represents the charging pile set; F' represents the access charging pile set; V' represents the union of V and F'; nodes 0 and N+1 represent garages, 0 represents the outbound node, and N+1 represents the return node; A represents the arc set, A = {(i,j)|i,j∈V' 0,N+1 ,i≠j}; M represents the set of tractors; S302, during the service launch period, the tractor fleet consists of n E Electric tractors and IC Composed of fuel tractors, n E +n IC =n;s it represents the duration of the t-th tractor, v it represents the driving speed, b i and f i represents the push-out energy consumption, B represents the maximum battery capacity of the tractor, p% represents the minimum charge threshold, r represents the charging rate, τ it represents the time when the t-th tractor arrives at the node, y it represents the remaining power when the t-th tractor arrives at the node; x ijt is a decision variable, indicating whether vehicle t passes through arc (i, j); If the t-th tractor passes through arc (i, j), then the variable x ijt is 1, otherwise it is 0; S303. The specific expression of the objective function is: In the formula, f tm represents the time cost function, c (TM) represents the cost per unit time, represents the electricity cost function, represents the unit electricity cost, represents the fuel cost function, represents the unit fuel cost, represents the emission cost function, c (EM) represents the unit carbon emission cost, c em Represents the carbon emissions per unit distance, d ij represents the distance of each arc, v jt represents the towing speed of the t-th tractor at the j-th parking position node, V'0 represents the set of V, F' and parking position node 0, and f obj represents the overall objective function, x iN+1t Indicates whether the t-th tractor passes through arc (i, N+1), t iN+1t represents the time that the t-th tractor travels on the arc (i, N+1), x 0it Indicates whether the t-th tractor passes through the arc (0, t), t 0it represents the time that the t-th tractor travels on the arc (0, t), V' N+1 Represents a set of nodes including N+1 and parking bays; S304. The specific expression of the constraint condition is: P ijt (m t )=(0.5·ρ α ·A f ·c d ·(v ijt ) 2 +c r ·m t ·g)·v ijt P jt (m t +m a )=(0.5·ρ α ·A f ·c d ·(v jt ) 2 +c r ·(m t +m a )·g)·v jt Among them, ρ α represents the air density, A f represents the front area of ​​the tractor, c d represents the aerodynamic drag coefficient, v ijt represents the towing speed of the t-th tractor on the arc (i, j), c r represents the rolling friction coefficient, g represents the gravity constant, x 0jt Indicates whether the t-th tractor is traveling on the arc (0, j), l i represents the latest service start time of the i-th parking space node, τ jt represents the time when the t-th tractor arrives at the j-th parking node, V0 represents the parking node set including the departure garage node, e i represents the earliest service start time of the i-th parking lot node i, F'0 represents the charging pile node set including the departure garage node, x qjt Indicates whether the t-th tractor is traveling on the arc (0, j), b it represents the service energy consumption of the t-th tractor at parking position node i, y jt represents the remaining energy consumption of the t-th tractor at the j-th parking position node, V' 0,N+1 represents the parking space node set including the departure and arrival garage nodes, e0 represents the earliest opening time of the garage, l N+1 Indicates the latest closing time of the garage; S4. Design an improved adaptive large neighborhood search algorithm to solve the airport tractor hybrid fleet scheduling model to obtain the scheduling task allocation and vehicle schedule after the total operating cost is optimized; the specific contents are as follows: S401, select the earliest n flights as the first task of the n tractors, assign the unassigned flight tasks to each tractor according to the principle of minimizing the cost function, and arrange for the vehicles with insufficient power to go to the charging pile; ignore all charging points, sort the routes in descending order according to the total value of the time window and power constraint after ignoring them, and select the first n IC The tractor path is converted into a fuel vehicle path by removing the charging station node; S402, randomly select the number of nodes to be operated, and select the destroy and repair operators according to the weight probability, reconstruct the original node, and generate a new solution; S403, constructing an evaluation function and a penalty function of a solution according to the objective function and constraint conditions of the airport tractor hybrid fleet scheduling model, and using a simulated annealing criterion to determine whether to accept the solution as the optimal solution; S404: Determine whether the maximum number of iterations has been reached. If not, update the operator selection probability weight according to the solution acceptance result and go to step S402; otherwise, output the optimal solution to obtain the vehicle path planning result x under the oil-electric hybrid operation under the total operation cost optimization. ijt ; S5. Input the case data under the set fleet configuration into the airport tractor hybrid fleet scheduling model to obtain the objective function value and the optimized scheduling plan.

2. The method for dispatching a hybrid fleet of airport tractors according to claim 1, characterized in that: In step S1, the types of airport tractor vehicles are classified according to energy consumption types, including but not limited to fuel tractors and electric tractors; Among them, the electric tractor has two functions: aircraft towing and APU service replacement; the airport tractor serves departing flights, and its work content is to drive along the vehicle road to the corresponding parking position within the established time window and provide towing service for a set period of time.

3. The method for dispatching a hybrid fleet of airport tractors according to claim 1, characterized in that: In step S5, the case data under the set fleet configuration is input into the airport tractor hybrid fleet scheduling model to obtain the optimal scheduling plan and its corresponding cost under the configuration. By adjusting parameters such as vehicle ratio and repeating the calculation, the time, energy consumption, emission cost and delay duration of different vehicle ratio schemes are compared and analyzed to determine the optimal vehicle ratio in a certain scenario. Combined with the scheduling results in scenarios of different scales, the impact of scenario characteristics on the scheduling results is analyzed.

4. An airport tractor hybrid fleet dispatching system, characterized in that: include The traction vehicle performance acquisition module is used to obtain the type and function of the airport traction vehicle and confirm the working rules of the airport traction vehicle; The vehicle energy consumption model building module is used to obtain different types of vehicle energy consumption models based on the functions of airport tractors; the specific contents are: The vehicle energy consumption model includes the electric tractor energy consumption model and the fuel tractor energy consumption model, which is built on the mechanical energy calculation model; Among them, the specific expression of the mechanical energy calculation model is: F r =c r ·m t ·g·cos(α) F a =0.5·ρ α ·A f ·c d ·v 2 P M =(m t ·a+0.5·p α ·A f ·c d ·v 2 +m t ·g·sin(α)+c r ·m t ·g·cos(α))·v P ijt (m t )=(0.5·ρ α ·A f ·c d ·(v ijt ) 2 +c r ·m t ·g)·v ijt P jt (m t +m a )=(0.5·ρ α ·A f ·c d ·(v jt ) 2 +c r ·(m t +m a )·g)·v jt Among them, F r is the friction resistance, F a is the aerodynamic resistance, P M is the total mechanical energy, P ijt (m t ) is the mechanical energy required for the t-th vehicle to travel on the arc (i, j), P jt (m t +m a ) is the mechanical energy required for service at the parking stand, c r is the rolling friction coefficient, m t is the mass of the tractor, g is the gravity constant, α is the road slope angle, ρ α is the air density, A f is the front area of ​​the tractor, c d is the aerodynamic drag coefficient, v is the speed, a is the acceleration of the tractor, m a is the weight of the aircraft towed by the tractor, v ijt is the drag speed of vehicle t on arc (i, j), v jt is the towing speed of the t-th vehicle at parking stand node j; The specific expression of the energy consumption model of electric tractor is: Where b ijt Indicates driving energy consumption, Indicates the towing energy consumption of the electric tractor. Indicates APU replacement energy consumption, b jt represents the service energy consumption, φ represents the conversion efficiency from electrical energy to mechanical energy, Indicates the discharge efficiency of the battery, t ijt represents the travel time of the t-th tractor on arc (i, j), M E Represents the electric tractor set, s jt represents the service time of the t-th tractor at the j-th parking position node, E AIR It is the power consumed by the aircraft air conditioning system, T AIR Indicates the time of providing power to the aircraft air conditioning system, E ILL It is the power consumed by the aircraft lighting system, T ILL Indicates the time of providing power to the aircraft lighting system, E LAU It is the power consumed by the aircraft engine starting, T LAU Indicates the time that power is provided to start the aircraft engine; The specific expression of the fuel tractor energy consumption model is: In the formula, f ijt represents the driving energy consumption of a fuel vehicle, f jt represents the service energy consumption of fuel vehicles, ξ represents the fuel-air mass ratio, κ represents the calorific value of fuel, ψ represents the unit conversion coefficient, k represents the engine friction coefficient, N e represents the engine speed, D represents the engine displacement, η represents the fuel engine efficiency parameter, η tf Represents the transmission system efficiency parameter, M IC Represents a collection of fuel tractors, represents the towing energy consumption of the t-th tractor at the j-th parking position node; The airport tractor oil-electric hybrid fleet dispatch model construction module is used to optimize the dispatch of the airport tractor oil-electric hybrid fleet, set the total operating cost as the objective function, determine the constraints, and construct the airport tractor oil-electric hybrid fleet dispatch model; the specific contents are: Step 1: Airport tractor operation directed network G, G = (V' 0,N+1 ,A); V = {1,...N} represents the parking space node set; F represents the charging pile set; F' represents the access charging pile set; V' represents the union of V and F'; nodes 0 and N+1 represent garages, 0 represents the outbound node, and N+1 represents the return node; A represents the arc set, A = {(i,j)|i,j∈V' 0,N+1 ,i≠j}; M represents the set of tractors; Step 2: The tractor fleet consists of n E Electric tractors and IC Composed of fuel tractors, n E +n IC =n;s it represents the duration of the t-th tractor, v it represents the driving speed, b i and f i represents the push-out energy consumption, B represents the maximum battery capacity of the tractor, p% represents the minimum charge threshold, r represents the charging rate, τ it represents the time when the t-th tractor arrives at the node, y it represents the remaining power when the t-th tractor arrives at the node; x ijt is a decision variable, indicating whether vehicle t passes through arc (i, j); Step 3: The specific expression of the objective function is: In the formula, f tm represents the time cost function, c (TM) represents the cost per unit time, represents the electricity cost function, represents the unit electricity cost, represents the fuel cost function, represents the unit fuel cost, represents the emission cost function, c (EM) represents the unit carbon emission cost, c em Represents the carbon emissions per unit distance, d ij represents the distance of each arc, v jt represents the towing speed of the t-th tractor at the j-th parking position node, V'0 represents the set of V, F' and parking position node 0, and f obj represents the overall objective function, x iN+1t Indicates whether the t-th tractor passes through arc (i, N+1), t iN+1t represents the time that the t-th tractor travels on the arc (i, N+1), x 0it Indicates whether the t-th tractor passes through the arc (0, t), t 0it represents the time that the t-th tractor travels on the arc (0, t), V' N+1 Represents a set of nodes including N+1 and parking bays; Step 4: The specific expression of the constraint condition is: P ijt (m t )=(0.5·ρ α ·A f ·c d ·(v ijt ) 2 +c r ·m t ·g)·v ijt P jt (m t +m a )=(0.5·ρ α ·A f ·c d ·(v jt ) 2 +c r ·(m t +m a )·g)·v jt Among them, x 0jt Indicates whether the t-th tractor is traveling on the arc (0, j), l i represents the latest service start time of the i-th parking space node, τ jt represents the time when the t-th tractor arrives at the j-th parking node, V0 represents the parking node set including the departure garage node, e i represents the earliest service start time of the i-th parking lot node i, F'0 represents the charging pile node set including the departure garage node, x qjt Indicates whether the t-th tractor is traveling on the arc (0, j), b it represents the service energy consumption of the t-th tractor at parking position node i, y jt represents the remaining energy consumption of the t-th tractor at the j-th parking position node, V' 0,N+1 represents the parking space node set including the departure and arrival garage nodes, e0 represents the earliest opening time of the garage, l N+1 Indicates the latest closing time of the garage; The scheduling optimization module is used to design an improved adaptive large neighborhood search algorithm. By solving the airport tractor hybrid fleet scheduling model, the scheduling task allocation and vehicle schedule after the total operating cost is optimized are obtained. The specific contents are as follows: Step 1: Select the earliest n flights as the first task of the n tractors, assign the unassigned flight tasks to each tractor according to the principle of minimizing the cost function, and arrange for the vehicles with insufficient power to go to the charging pile; ignore all charging points, sort the routes in descending order according to the total value of the time window and power constraint after ignoring them, and select the first n IC The tractor path is converted into a fuel vehicle path by removing the charging station node; Step 2: Randomly select the number of nodes to be operated, and select the destroy and repair operators according to the weight probability to reconstruct the original nodes and generate a new solution; Step 3: construct the evaluation function and penalty function of the solution according to the objective function and constraint conditions of the airport tractor hybrid fleet scheduling model, and use the SA criterion to determine whether to accept the solution as the optimal solution; Step 4: Determine whether the maximum number of iterations has been reached. If not, update the operator selection probability weight according to the solution acceptance result and go to step 2; otherwise, output the optimal solution to obtain the vehicle path planning result x under oil-electric hybrid operation with total operating cost optimization. ijt .

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is performed.

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