Electric bus and electric vehicle cooperative scheduling charging method and system

Abstract

This invention belongs to the field of image processing technology and discloses a method and system for coordinated scheduling and charging of electric buses and electric vehicles. The method includes: acquiring electric bus data and electric vehicle data for a target area; wherein the electric bus data includes bus route data and electric bus parameters, and the electric vehicle data includes vehicle route data and electric vehicle parameters; constructing a mixed-integer programming model based on the bus route data, electric bus parameters, vehicle route data, and electric vehicle parameters, using an objective function and constraints; solving the mixed-integer programming model using an adaptive large neighborhood search algorithm to obtain the coordinated scheduling result of electric buses and electric vehicles in the target area; and scheduling electric buses and electric vehicles according to the coordinated scheduling result, using the scheduled electric buses to wirelessly charge the electric vehicles. This invention can realize coordinated scheduling and charging of electric buses and electric vehicles in large-scale scenarios.

Description

Technical Field

[0001] This invention relates to the field of wireless charging technology for electric vehicles, and in particular to a method and system for coordinated charging of electric buses and electric vehicles. Background Technology

[0002] With the global energy structure transformation and people's increasing environmental awareness, the electric vehicle market is showing a rapid growth trend.

[0003] However, traditional wired charging requires vehicles to be parked at fixed charging stations, which not only takes up extra time but also suffers from problems such as unreasonable charging station layout and low utilization rate. Therefore, electric vehicles face a technical bottleneck of insufficient charging infrastructure coverage. In addition, electric vehicles also suffer from technical bottlenecks such as short driving range and long charging time, which seriously restrict the popularization and promotion of electric vehicles.

[0004] To address the charging challenges of electric vehicles, research into applying wireless charging technology to electric vehicle charging is gaining momentum. However, much of this research focuses on optimizing the deployment of charging facilities for single vehicle models, lacking a systematic design for the coordinated charging of multiple vehicle types in urban transportation systems. This results in the overlap of peak and off-peak charging demands for different vehicle models, exacerbating grid load fluctuations.

[0005] Electric buses have the advantages of large battery capacity, fixed routes, and regular travel patterns. However, there is no mature technology to use electric buses for charging electric vehicles. Technologies such as charging path planning and wireless charging retrofitting of electric vehicles limit the large-scale application of this technology in the field of urban transportation.

[0006] The existing technology still lacks a mature technical solution for the coordinated charging of electric vehicles through electric buses, therefore, the existing technology needs to be improved. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a method and system for coordinated scheduling and charging of electric buses and electric vehicles, in order to address the shortcomings of existing technologies and solve the problem of the lack of a sustainable discrimination model for AI image generation models.

[0008] The technical solution adopted by this invention to solve the technical problem is as follows:

[0009] In a first aspect, the present invention provides a method for coordinated charging of electric buses and electric vehicles, comprising:

[0010] Acquire data on electric buses and electric vehicles in the target area; wherein, the electric bus data includes bus route data and electric bus parameters, and the electric vehicle data includes vehicle route data and electric vehicle parameters;

[0011] Based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters, a mixed integer programming model is constructed using the objective function and constraints.

[0012] The mixed integer programming model is solved using an adaptive large neighborhood search algorithm to obtain the coordinated scheduling results of electric buses and electric vehicles in the target area;

[0013] Based on the coordinated scheduling results, the electric bus and the electric vehicle are scheduled, and the scheduled electric bus wirelessly charges the electric vehicle.

[0014] In one implementation, the bus route data includes one or more combinations of the following: travel range and speed requirements for rechargeable sections; the electric bus parameters include one or more combinations of the following: wireless charging retrofit status, bus model, battery capacity, service frequency, and time-of-use electricity pricing.

[0015] In one implementation, the vehicle route data includes one or more combinations of origin and destination points and departure time; the electric vehicle parameters include one or more combinations of rechargeable window, battery state of charge, and wireless charging cost.

[0016] In one implementation, the step of constructing a mixed-integer programming model based on the bus route data, the electric bus parameters, the vehicle route data, and the electric vehicle parameters, using an objective function and constraints, includes:

[0017] Based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters, determine the objective function and constraints of the mixed integer programming model;

[0018] Construct a mixed integer programming model based on the objective function and the constraints.

[0019] In one implementation, the step of using an adaptive large neighborhood search algorithm to solve the mixed integer programming model to obtain the coordinated scheduling results of electric buses and electric vehicles in the target area includes:

[0020] An initial solution is generated based on the principle of proximity; wherein, the initial solution is used to match the electric bus with the electric vehicle and plan the driving route;

[0021] Generate a destruction operator; wherein the destruction operator is used to destroy the solution corresponding to the destruction operator in the mixed integer programming model;

[0022] Generate an insertion operator; wherein the insertion operator is used to repair the corrupted solution in the mixed integer programming model;

[0023] Based on the destruction operator and the insertion operator, an iterative solution process is performed a preset number of times to output the final solution of the mixed integer programming model, and the cooperative scheduling result corresponding to the final solution is obtained.

[0024] In one implementation, the iterative solution process based on the destruction operator and the insertion operator for a preset number of times, outputting the final solution of the mixed integer programming model, and obtaining the cooperative scheduling result corresponding to the final solution, includes:

[0025] Determine the first weight of the destruction operator and the second weight of the insertion operator;

[0026] In each iterative solution process, a first probability of each destruction operator being selected is calculated based on the first weight, and a second probability of each insertion operator being selected is calculated based on the second weight;

[0027] Based on the first probability and the second probability, the destruction operator and the insertion operator are randomly selected, the solution corresponding to the selected destruction operator is destroyed, the destroyed solution is repaired according to the selected insertion operator, and a new solution is output.

[0028] Obtain the optimal solution of the current mixed integer programming model, compare the new solution with the optimal solution, update or retain the optimal solution based on the comparison result, update the first weight based on the selection of the destruction operator, and update the second weight based on the selection of the insertion operator; wherein, the optimal solution of the first iteration solution process is the initial solution;

[0029] The system performs an iterative solution process a preset number of times, outputs the final solution of the mixed integer programming model, and obtains the collaborative scheduling result corresponding to the final solution; wherein, the final solution is the optimal solution output by the last iterative solution process.

[0030] In one implementation, the wireless charging of the electric vehicle by the dispatched electric bus includes:

[0031] The inverter of the dispatched electric bus converts the DC power from the on-board power battery into high-frequency AC power, and creates an alternating magnetic field around the transmitting coil group of the electric bus.

[0032] The electric vehicle is controlled to travel within a preset range in the same lane as the dispatched electric bus, so as to charge the battery of the electric vehicle through the induced current generated by the alternating magnetic field.

[0033] Secondly, the present invention provides a coordinated charging system for electric buses and electric vehicles, comprising:

[0034] The parameter acquisition module is used to acquire electric bus data and electric vehicle data for the target area; wherein, the electric bus data includes bus route data and electric bus parameters, and the electric vehicle data includes vehicle route data and electric vehicle parameters;

[0035] The model building module is used to construct a mixed integer programming model based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters, using an objective function and constraints.

[0036] The model solving module is used to solve the mixed integer programming model using an adaptive large neighborhood search algorithm to obtain the coordinated scheduling results of electric buses and electric vehicles in the target area;

[0037] The scheduling execution module is used to schedule the electric bus and the electric vehicle according to the coordinated scheduling result, and to wirelessly charge the electric vehicle through the scheduled electric bus.

[0038] Thirdly, the present invention provides a charging terminal for coordinated scheduling of electric buses and electric vehicles, comprising: a processor and a memory, wherein the memory stores a charging program for coordinated scheduling of electric buses and electric vehicles, and the charging program for coordinated scheduling of electric buses and electric vehicles, when executed by the processor, is used to implement the operation of the charging method for coordinated scheduling of electric buses and electric vehicles as described in the first aspect.

[0039] Fourthly, the present invention also provides a computer-readable storage medium storing a program for coordinated scheduling and charging of electric buses and electric vehicles, which, when executed by a processor, is used to implement the operation of the coordinated scheduling and charging method for electric buses and electric vehicles as described in the first aspect.

[0040] The present invention, by employing the above technical solution, has the following effects:

[0041] This invention provides a method and system for coordinated scheduling and charging of electric buses and electric vehicles, comprising: acquiring electric bus data and electric vehicle data for a target area; wherein, the electric bus data includes bus route data and electric bus parameters, and the electric vehicle data includes vehicle route data and electric vehicle parameters; constructing a mixed-integer programming model based on the bus route data, electric bus parameters, vehicle route data, and electric vehicle parameters, using an objective function and constraints; solving the mixed-integer programming model using an adaptive large neighborhood search algorithm to obtain the coordinated scheduling result of electric buses and electric vehicles in the target area; scheduling electric buses and electric vehicles according to the coordinated scheduling result, and wirelessly charging electric vehicles through the scheduled electric buses; this invention can realize coordinated scheduling and charging of electric buses and electric vehicles in large-scale scenarios. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0043] Figure 1 This is a flowchart of the coordinated scheduling and charging method for electric buses and electric vehicles in this invention.

[0044] Figure 2 This is a flowchart illustrating how the collaborative scheduling charging embedded adaptive large neighborhood search algorithm solves for the optimal solution in a mixed integer programming model of a target region in one implementation of the present invention.

[0045] Figure 3 This is a schematic diagram of a dispatched electric bus wirelessly charging an electric vehicle in one implementation of the present invention.

[0046] Figure 4 This is a functional schematic diagram of the terminal in one implementation of the present invention.

[0047] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0049] Exemplary methods

[0050] To address the charging challenges of electric vehicles, research into applying wireless charging technology to electric vehicle charging is gaining momentum. However, much of this research focuses on optimizing the deployment of charging facilities for single vehicle models, lacking a systematic design for the coordinated charging of multiple vehicle types in urban transportation systems. This results in the overlap of peak and off-peak charging demands for different vehicle models, exacerbating grid load fluctuations.

[0051] Electric buses possess the advantages of large battery capacity, fixed routes, and predictable travel patterns. However, current technology lacks a mature solution for using electric buses to charge electric vehicles. Technologies such as charging route planning and wireless charging retrofitting for electric vehicles limit the large-scale application of this solution in urban transportation. Therefore, existing technology still lacks a mature solution for the coordinated charging of electric vehicles using electric buses, and thus requires further improvement.

[0052] To address the above-mentioned technical problems, this invention provides a method for coordinated scheduling and charging of electric buses and electric vehicles, comprising: acquiring electric bus data and electric vehicle data for a target area; wherein, the electric bus data includes bus route data and electric bus parameters, and the electric vehicle data includes vehicle route data and electric vehicle parameters; constructing a mixed-integer programming model based on the bus route data, electric bus parameters, vehicle route data, and electric vehicle parameters, using an objective function and constraints; solving the mixed-integer programming model using an adaptive large neighborhood search algorithm to obtain the coordinated scheduling result of electric buses and electric vehicles in the target area; scheduling electric buses and electric vehicles according to the coordinated scheduling result, and wirelessly charging electric vehicles through the scheduled electric buses; this invention can realize coordinated scheduling and charging of electric buses and electric vehicles in large-scale scenarios.

[0053] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for coordinated scheduling and charging of electric buses and electric vehicles, including the following steps:

[0054] Step S100: Obtain electric bus data and electric vehicle data for the target area; wherein, the electric bus data includes bus route data and electric bus parameters, and the electric vehicle data includes vehicle route data and electric vehicle parameters.

[0055] Specifically, in one implementation of this embodiment, step S100 includes the following steps:

[0056] Step S101: Obtain electric bus data for the target area; wherein, the electric bus data includes bus route data and electric bus parameters.

[0057] In this embodiment, the bus route data includes one or more combinations of the following: travel range and speed requirements for charging sections.

[0058] Specifically, the travel range refers to the electric bus network in the target area. Based on the travel range, the bus route data is divided into three categories: trunk routes, regular routes, and branch routes. Further, charging sections are determined based on the divided routes, and the speed requirements for charging sections are calculated.

[0059] In addition, the data on electric bus routes also includes the speed of the electric buses. For example, the average speed of electric buses on main roads within the route range is 30 km / h, and the average speed of electric buses on secondary roads is 20 km / h.

[0060] In this embodiment, the parameters of the electric bus include one or more combinations of the following: wireless charging retrofit status, bus model, battery capacity, service frequency, and time-of-use electricity price.

[0061] Specifically, the wireless charging upgrade status indicates whether electric buses have undergone V2V wireless charging upgrades. For electric buses that have not undergone V2V wireless charging upgrades, the upgrade cost needs to be calculated based on the bus model. Bus models include large, medium, and small models. Different bus models have different battery capacities. Large models have higher battery capacities and correspondingly higher V2V charging power. Medium models have medium battery capacities and V2V charging power. Small models have lower battery capacities and V2V charging power.

[0062] The bus model is also associated with the type of bus route data. Specifically, electric buses on trunk routes are large buses, electric buses on regular routes are medium buses, and electric buses on branch routes are small buses.

[0063] It should be noted that for electric buses that have already been modified for V2V wireless charging, the battery capacity and V2V charging power are actual data. However, for electric buses that have not undergone V2V wireless charging modification, the battery capacity and V2V charging power are standard data or average data for the same model. For electric buses that have not undergone V2V wireless charging modification, the modification cost is calculated based on the bus model. The modification cost includes fixed costs and variable costs. The fixed cost is the inverter cost, and the variable cost is the wireless charging coil cost. The modification cost per electric bus is calculated based on the number of coils configured for each model; the larger the model, the higher the modification cost. For example, for large buses on trunk routes, the unit modification cost is 484,500 yuan, corresponding to a maximum V2V wireless charging power of 80kW.

[0064] In this embodiment, some data on the parameters of electric buses can be obtained from multi-source data of the electric bus network in the target area during historical operating cycles, such as the GPS trajectory of electric buses, service times, bus models, battery capacity, time-of-use electricity prices, passenger flow at route sections, and service life.

[0065] Time-of-use pricing also affects the number of times electric buses can be powered and the cost of power supply. For example, electric buses can be powered more frequently during peak or off-peak hours to reduce power supply costs.

[0066] Step S102: Obtain electric vehicle data for the target area; wherein, the electric vehicle data includes vehicle route data and electric vehicle parameters.

[0067] In this embodiment, the vehicle route data includes one or more combinations of origin and destination points and departure time.

[0068] Specifically, the system obtains the route data of all electric vehicles in the target area, including the origin and destination points and departure times of the electric vehicles. Based on these origin and destination points and departure times, the system can obtain the historical driving route data of the electric vehicles. According to the historical driving route data, the system can also perform route planning for the electric vehicles and select charging sections for them.

[0069] In this embodiment, the electric vehicle parameters include one or more combinations of the following: rechargeable window, battery state of charge, and wireless charging cost.

[0070] Specifically, the State of Charge (SOC) of an electric vehicle is a core parameter used to measure the ratio of the remaining usable capacity of the electric vehicle battery to its fully charged capacity. The value range can be equivalent to 0%-100%. The State of Charge includes the battery state at the moment the electric vehicle departs. In addition, the State of Charge also includes a safe operating range. In this embodiment, the safe upper limit of the State of Charge is set to 90%, and the safe lower limit is set to 10%.

[0071] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for coordinated scheduling and charging of electric buses and electric vehicles, including the following steps:

[0072] Step S200: Based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters, a mixed integer programming model is constructed using the objective function and constraints.

[0073] In this embodiment, a mixed integer programming model is constructed using an objective function and constraints. The mixed integer programming model takes minimizing the sum of the electric bus retrofit cost, the electric vehicle charging cost, and the time cost as its objective function, and comprehensively considers the constraints based on bus data and electric vehicle data.

[0074] Specifically, in one implementation of this embodiment, step S200 includes the following steps:

[0075] Step S201: Determine the objective function and constraints of the mixed integer programming model based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters.

[0076] In this embodiment, based on bus route data, electric bus parameters, and electric vehicle parameters, the conversion cost of electric buses, the energy cost and time cost of charging electric vehicles are calculated. This yields an objective function representing the cost of coordinated scheduling charging, based on the sum of these three costs. Specifically:

[0077] ;

[0078] in, The objective function is... The cost of retrofitting electric buses; The energy cost of charging electric vehicles; The time cost of charging an electric vehicle; all three costs are in yuan.

[0079] In this embodiment, the conversion cost of electric buses refers to the total cost of converting all electric buses in the target area. It is assumed that the electric bus routes in the target area are as follows: The corresponding set of electric bus routes Each route has Electric buses, the set of electric buses on the corresponding routes The corresponding conversion cost for electric buses; It can be calculated using the following formula:

[0080] ;

[0081] in, Indicates the modification of a route The cost of taking the bus, in yuan; Represents a 0-1 variable, if the route electric buses If modifications are made, ,otherwise .

[0082] In this embodiment, the energy cost of charging electric vehicles... This refers to the energy cost of charging an electric bus within its service life, which can be calculated using the following formula:

[0083] ;

[0084] in, Indicate route electric buses The power supply capacity of V2V wireless power supply, in kW; This indicates the charging efficiency of an electric vehicle being charged by an electric bus. Indicate route electric buses The itinerary, corresponding itinerary set ; and For itinerary collection The set of nodes in all routes ; For electric vehicles, corresponding to the set of electric vehicles ; Representing 0-1 variables, if electric vehicles On the road section By route electric buses On the trip Charging, ,otherwise ; This is a two-dimensional matrix representing the road network. Click Distance between points, in km; For the route electric buses The driving speed, in km / h; For electric buses The number of days corresponding to the service life; For electric vehicles The unit charging electricity price is yuan / (kW·h).

[0085] In this embodiment, the time cost of charging an electric vehicle... This refers to the time cost of charging an electric bus within its service life, which can be calculated using the following formula:

[0086] ;

[0087] in, This is the conversion factor for time to monetary cost, expressed in yuan per hour. For electric vehicles Reaching the node At that moment, For electric vehicles The starting point is the location of the starting point. For electric vehicles The destination location is the final stop. Electric vehicles The moment of arrival at the starting point, Electric vehicles The moment of arrival at the destination.

[0088] In this embodiment, based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters, the following constraints are defined:

[0089] Constraint 1: An electric bus can only charge one electric vehicle within the same route and segment, as shown in the following formula:

[0090] .

[0091] Constraint 2: Electric vehicles can only operate on road sections. Only when driving can you be on the road section The formula for charging an electric bus is as follows:

[0092] ;

[0093] in, For 0-1 variables, if electric vehicles On the road section If the vehicle is traveling inside and in the same direction as the electric bus, then... ,otherwise .

[0094] Constraint 3: The battery state of the electric vehicle must always be maintained between the upper and lower safety limits, as shown in the following formula:

[0095] ;

[0096] ;

[0097] in, For electric vehicles At the node Battery power status, That is, the battery charge of the electric vehicle at its starting position; For electric vehicles Initial battery charge level at departure; This refers to the battery capacity of an electric vehicle, expressed in kW·h. This refers to the maximum permissible percentage of the battery capacity of an electric vehicle, i.e., the safe upper limit of the battery's state of charge. This is the minimum permissible percentage of battery capacity for an electric vehicle, i.e., the safe lower limit of the battery's state of charge.

[0098] Constraint 4 ensures that electric vehicles travel within a preset driving path, meaning that the driving path of each electric vehicle must start from the starting point and end at the destination, and for any electric vehicle... It enters the road network node The number of times must be related to the number of times leaving the node. To keep them equal, the corresponding formula is as follows:

[0099] ;

[0100] in, , For 0-1 variables, if electric vehicles At the node with destination location If they travel between them and in the same direction as the electric bus, then ,otherwise This formula means that the travel path of each electric vehicle must end at the destination.

[0101] ;

[0102] in, , For 0-1 variables, if electric vehicles At the starting position With nodes If they travel between them and in the same direction as the electric bus, then ,otherwise This formula means that the driving path of each electric vehicle must start from the starting point.

[0103] ;

[0104] in, ,and ; For itinerary collection The nodes in all routes; the meaning of this formula is that for any electric vehicle It enters the road network node The number of times must be related to the number of times leaving the node. Keep them equal.

[0105] Constraint 5: The time when the car leaves the starting point cannot be earlier than the time when the charging request is issued. The corresponding formula is as follows:

[0106] ;

[0107] in, For electric vehicles The set of departure times The time required to issue a charging request for electric vehicles.

[0108] Constraint 6: The electric vehicle travels on any road segment At that time, its average speed must not exceed the maximum speed limit. The unit is km / h, and the corresponding formula is as follows:

[0109] ;

[0110] in, For electric vehicles Reaching the node The moment; For electric vehicles Reaching the node The moment; It is a sufficiently large positive number used to set constraints.

[0111] Constraint 7, for any electric vehicle electric buses and road sections When electric vehicles Before electric buses Previous Nodes And electric vehicles With bus All in the road section When driving, electric vehicles Can be taken by bus The charging formula is as follows:

[0112] ;

[0113] in, Indicate route medium-sized buses On the trip Reaching the node At that moment.

[0114] Constraint 8: During the matching process of coordinated charging scheduling between electric buses and electric vehicles, if the route... electric buses If there is any activity that supplies power to electric vehicles, then the electric bus will need to be modified, and the corresponding formula is as follows:

[0115] ;

[0116] .

[0117] Constraint 9, Route electric buses On the trip The number of power supply cycles cannot exceed the maximum allowed by the collaborative wireless charging mode, as shown in the following formula:

[0118] ;

[0119] in, For the line electric buses On the trip The maximum number of V2V wireless charging cycles is 2 times per trip for electric buses during peak hours and 6 times per trip during off-peak or low-peak hours.

[0120] Constraint 10, Electric Vehicles On the road section On the bus When charging, the node is reached. With nodes The timing needs to be consistent with the bus At the node With nodes The times remain equal, and the corresponding formula is as follows:

[0121] ;

[0122] ;

[0123] ;

[0124] .

[0125] Constraint 11, constrain any electric vehicle Driving on any road The range of power variation over time is related to the power supply efficiency of electric buses. Choose the road section This constraint only takes effect while the vehicle is in motion, and the corresponding formula is as follows:

[0126] ;

[0127] ;

[0128] in, Indicates electric vehicles At the node The remaining battery power; This indicates the rate at which an electric vehicle consumes electricity, expressed in kW·h / km. This indicates the charging efficiency of an electric vehicle being charged by an electric bus.

[0129] Step S202: Construct a mixed integer programming model based on the objective function and the constraints.

[0130] In this embodiment, based on the above objective function and constraints, a mixed integer programming model is constructed to obtain the minimum value of the total cost of retrofitting electric buses, travel time cost, and charging cost of electric vehicles during the period of minimizing the service life of electric buses.

[0131] Furthermore, the constructed mixed-integer programming model was also used to make decisions on five types of variables, whose value ranges are as follows:

[0132] ;

[0133] ；

[0134] ；

[0135] .

[0136] like Figure 1 As shown, this embodiment of the invention provides a method for coordinated charging of electric buses and electric vehicles, including the following steps:

[0137] Step S300: Use the adaptive large neighborhood search algorithm to solve the mixed integer programming model to obtain the coordinated scheduling results of electric buses and electric vehicles in the target area.

[0138] In this embodiment, the Adaptive Large Neighborhood Search (ALNS) algorithm is used to solve the mixed integer programming model. By training the ALNS algorithm, the Cooperative Charging-embedded-Adaptive Large Neighborhood Search (CC-ALNS) algorithm is obtained. The CC-ALNS algorithm is then used to solve the mixed integer programming model of the target area, and the optimal solution is obtained as the result of the coordinated scheduling of electric buses and electric vehicles in the target area.

[0139] like Figure 2 The diagram shows a flowchart of the collaborative scheduling charging embedded adaptive large neighborhood search algorithm used in this embodiment to solve the optimal solution in the mixed integer programming model of the target region; specifically, it includes the following steps:

[0140] Step a, generate the initial solution;

[0141] Step b: Set the score of all destruction and insertion operators to 1, and the number of iterations... Setting it to 0 defines the initial solution. ,in, It is a sufficiently large positive number;

[0142] Step c: Randomly select operators based on their selection probability; whereby operators include destruction operators and insertion operators;

[0143] Step d involves disrupting a certain proportion of electric vehicle routing and charging decisions, as well as bus modification decisions, through a disruptive operator.

[0144] Step e involves connecting the electric vehicle path and charging decision that are currently disrupted by the insertion operator, and inserting the bus modification decision according to the rules.

[0145] Step f: Use the shortest path algorithm to connect the paths of the electric vehicles;

[0146] Step g: Calculate the total cost of the iteration;

[0147] Step h: Record the solution set currently being iterated;

[0148] Step i, calculate the total cost and with the current minimum total cost Compare;

[0149] Step j, when season = The current solution is set as the optimal solution, and the destruction and insertion operators participating in this iteration are scored according to the solution improvement rules, with the iteration number set as follows: And reset the number of times no improvement was made. ,make Return to step c;

[0150] Step k, when At that time, the destruction and insertion operators participating in this iteration will be scored according to the solution improvement rules, and the iteration number will be increased. No number of improvements Proceed to step 1;

[0151] Step 1: Determine if the number of iterations is within the maximum number of iterations, i.e. And determine whether the maximum number of improvements is within the maximum number of improvements, i.e. ;

[0152] Step m: If the number of iterations is within the maximum number of iterations, and the maximum number of improvements is also within the maximum number of improvements, then... , Return to step c;

[0153] Step n: If the number of iterations is not within the maximum number of iterations, or the maximum number of improvements is not within the maximum number of improvements, then... or Proceed to step o;

[0154] Step o: Output the optimal solution. The output optimal solution is taken as the final solution, that is, the final solution is the optimal solution output in the last iteration.

[0155] Specifically, in one implementation of this embodiment, step S300 includes the following steps:

[0156] Step S301: Generate an initial solution based on the proximity principle; wherein the initial solution is used to match the electric bus with the electric vehicle and plan the driving route.

[0157] In this embodiment, the nearest bus route and a powered electric bus are matched with the electric vehicle based on the principle of proximity. The route is planned based on the shortest path algorithm to ensure that the electric vehicle has enough power to reach the destination and obtain the initial solution.

[0158] Step S302: Generate a destruction operator; wherein the destruction operator is used to destroy the solution corresponding to the destruction operator in the mixed integer programming model.

[0159] In this embodiment, the destruction operators include the destruction operator with the highest cost of electric vehicle travel, the destruction operator with the highest cost of electric bus modification, and a random destruction operator, which are used to proportionally destroy the solutions corresponding to the destruction operators in a mixed integer programming model in order to expand the search space.

[0160] Specifically, the highest destruction operator for electric vehicle travel costs is used to select the energy cost of charging electric vehicles from the current feasible solutions based on the destruction ratio. Time cost of charging electric vehicles The highest proportion of electric vehicles corresponds to the highest sum of their travel routes. Then, the travel paths and charging matching solutions of these electric vehicles are disrupted, thereby greedily reducing the cost of electric vehicle travel. After the travel solutions of electric vehicles are disrupted, the modification solution for electric buses is determined based on the disrupted charging matching solutions. To perform a search, in Among the electric buses, if they belong to the route electric buses If no V2V wireless charging is implemented for any of the electric vehicles, then the decision variables for the conversion of this electric bus are... .

[0161] The highest-cost destruction operator for electric vehicle travel is used to sort the already modified electric buses from highest to lowest cost based on the proportion of electric vehicles to be destroyed. Electric vehicles wirelessly powered by these buses via V2V are then added to a destruction set until the length of the destruction set equals the number of electric vehicles to be destroyed. Then, these vehicles are destroyed. The system greedily reduces the cost of retrofitting buses by analyzing the travel path and charging matching solutions. After the travel solution for electric vehicles is disrupted, the system adjusts the retrofitting solution for electric buses based on the disrupted charging matching solution. To perform a search, in Among the electric buses, if the route electric buses Without V2V wireless charging for electric vehicles, the decision variables for the bus modification are... .

[0162] A random disruption operator is used to randomly select a corresponding number of electric vehicles based on the proportion of electric vehicles to be disrupted, and then disrupt the path and charging matching solutions of these electric vehicles. After the travel solutions of the electric vehicles are disrupted, a modification solution for the electric buses is derived based on the disrupted charging matching solutions. To perform a search, in Among the electric buses, if they belong to the route electric buses If no V2V wireless charging is implemented for any of the electric vehicles, then the decision variables for the conversion of this electric bus are... .

[0163] Step S303: Generate an insertion operator; wherein the insertion operator is used to repair the corrupted solution in the mixed integer programming model.

[0164] In this embodiment, the insertion operators include the insertion operator with the lowest electric vehicle travel cost, the insertion operator with the lowest number of electric bus retrofits, and the insertion operator with the lowest unit retrofit cost, which are used to repair / reconstruct high-quality feasible solutions that have been damaged in the mixed integer programming model.

[0165] Specifically, the electric vehicle (EV) travel cost minimum insertion operator is used to sequentially search for the shortest path among the EVs in the disrupted set using Dijkstra's algorithm. Within this shortest path, the operator searches for the shortest bus route reachable by the EV. After route selection, a suitable EV is matched to provide power within a reasonable timeframe. If an EV can reach its destination via the shortest path, it will not be matched for charging again; otherwise, the next bus route will be searched based on proximity, and the matching operation will be performed. After all EV travel paths and charging matching are repaired, the operator repairs the solution for the EV bus modification. This makes the decision variables of all electric buses involved in power supply... .

[0166] The insertion operator for minimizing the number of electric buses to be upgraded is used to sequentially search for the shortest paths among the electric vehicles in the disrupted set using Dijkstra's algorithm. It then searches for bus routes that overlap most with these shortest paths, aiming to minimize the number of electric buses required for upgrades and thus reduce the cost of upgrading. After the bus routes are determined, the operator performs charging matching based on the electric vehicles in the disrupted set. In selecting electric buses, it prioritizes those that have already been upgraded for power supply. If no upgraded electric buses meet the criteria, the operator selects unupgraded electric buses based on the current route for power supply matching. After matching, if an electric vehicle can reach its destination via the shortest route, it will not undergo further charging matching; otherwise, it needs to find the next bus segment based on proximity and perform the matching operation. After all the travel paths and charging matching of electric vehicles are repaired, the operator repairs the solution for upgrading the electric buses. This makes the decision variables of all electric buses involved in power supply... .

[0167] The lowest unit electric bus conversion cost interpolation algorithm is used to prioritize searching for the corresponding route with the lowest unit electric bus conversion cost. This algorithm connects the path of each electric vehicle in the damaged set to this route as much as possible, ensuring that there are overlapping sections between the electric vehicles and the bus route. Dijkstra's algorithm is then used to connect the two ends of the overlapping sections to the origin and destination of the electric vehicles. For any electric vehicle, if its path cannot be connected to the route, the algorithm searches for the second lowest unit electric bus conversion cost bus route, ensuring that the electric vehicle's path overlaps with this route, until a bus route can connect the electric vehicle's path. After all the electric vehicle paths in the damaged set are connected, each electric vehicle is matched with an electric bus based on the charging segment and corresponding route. After charging matching is completed, if an electric vehicle can reach its destination via the shortest path, it will not be matched again; otherwise, the next bus segment must be found based on the proximity principle for the matching operation. After all the electric vehicle travel paths and charging matching are repaired, the operator repairs the electric bus conversion solution. This makes the decision variables of all electric buses involved in power supply... .

[0168] Step S304: Based on the destruction operator and the insertion operator, perform an iterative solution process for a preset number of times, output the final solution of the mixed integer programming model, and obtain the cooperative scheduling result corresponding to the final solution.

[0169] Step S304a: Determine the first weight of the destruction operator and the second weight of the insertion operator.

[0170] In this embodiment, the first weight of the destruction operator and the second weight of the insertion operator are determined, and the first weight is... Second weight Set as the initial value, which is calculated based on the number of destruction and insertion operators.

[0171] Step S304b: In each iteration of the solution process, a first probability of each of the destruction operators being selected is calculated based on the first weight, and a second probability of each of the insertion operators being selected is calculated based on the second weight.

[0172] In this embodiment, during each iteration of the solution process, the Roulette Wheel Selection algorithm is used to select operators. The first probability of each disrupting operator being selected is calculated based on the first weight, and the second probability of each inserting operator being selected is calculated based on the second weight.

[0173] Specifically, for the first probability, the first weight sum of all candidate destruction operators is first calculated, and the first probability of each destruction operator is the ratio of the first weight corresponding to the destruction operator to the first weight sum; similarly, for the second probability, the second probability of each insertion operator is the ratio of the second weight corresponding to the insertion operator to the second weight sum.

[0174] Step S304c: Based on the first probability and the second probability, randomly select the destruction operator and the insertion operator, destroy the solution corresponding to the selected destruction operator, repair the destroyed solution according to the selected insertion operator, and output a new solution.

[0175] In this embodiment, a destruction operator is randomly selected based on a first probability. First, a first random number is generated between 0 and the sum of the first weights. By accumulating the first weights of each destruction operator, when the first weights exceed the generated first random number, the last accumulated destruction operator is selected as the destruction operator.

[0176] Similarly, the insertion operator is selected based on the second probability. A second random number is generated between 0 and the sum of the second and second weights. The second weight of each insertion operator is accumulated. When the second weight exceeds the generated second random number, the last accumulated insertion operator is selected as the insertion operator.

[0177] In this embodiment, the solution corresponding to the selected destruction operator is first destroyed, then the destroyed solution is repaired according to the selected insertion operator, and a new solution is output.

[0178] Step S304d: Obtain the optimal solution of the current mixed integer programming model, compare the new solution with the optimal solution, update or retain the optimal solution based on the comparison result, update the first weight based on the selection of the destruction operator, and update the second weight based on the selection of the insertion operator; wherein, the optimal solution of the first iteration solution process is the initial solution.

[0179] In this embodiment, the optimal solution of the current mixed integer programming model is obtained, the new solution is compared with the optimal solution, the optimal solution is updated or retained based on the comparison result, the first weight is updated based on the selection of the destruction operator, the second weight is updated based on the selection of the insertion operator, and the non-optimal solution is accepted in combination with the simulated annealing Metropolis criterion to balance search efficiency and solution quality.

[0180] Specifically, after comparing the new solution with the optimal solution, there are four possible comparison results:

[0181] Result 1: The new solution is better than the original optimal solution; wherein, the optimal solution in the first iteration is the initial solution.

[0182] Result 2: The new solution is better than the current solution, but it is not the optimal solution; where the current solution is the new solution output in the previous iteration.

[0183] Result 3: The optimal solution is better than the new solution, but the new solution is accepted.

[0184] Result 4: The optimal solution is better than the new solution, and the new solution is not accepted.

[0185] Furthermore, based on the above four comparison results, scores are added to the destruction and repair operators, and the optimal solution is updated or retained based on the comparison results, including:

[0186] When the comparison result is 1, the scores of both the destruction operator and the repair operator increase. Update the optimal solution;

[0187] When the comparison result is result 2, the scores of both the destruction operator and the repair operator increase. Preserve the optimal solution;

[0188] When the comparison result is 3, the scores of both the destruction operator and the repair operator increase. Preserve the optimal solution;

[0189] When the comparison result is 4, the scores of the destruction operator and the repair operator remain unchanged, and the optimal solution is retained;

[0190] Among them, there are .

[0191] In this embodiment, the first weight is updated based on the selection of the destruction operator, and the corresponding formula is as follows:

[0192] ;

[0193] in, Indicates the destruction operator In the In the The first weight corresponding to the next iteration; Indicates the number of times the destruction operator is selected; This is a reaction factor used to control weight adjustments based on changes in operator energy.

[0194] Similarly, the second weight is updated based on the selection of the insertion operator, and the corresponding formula is as follows:

[0195] ;

[0196] in, Indicates the insertion operator In the In the The second weight corresponding to the next iteration; This indicates the number of times the insertion operator was selected.

[0197] In this embodiment, the Metropolis criterion from the simulated annealing algorithm is used. This allows the algorithm to accept poor solutions initially to explore the solution space more broadly, and gradually reduces the probability of accepting poor solutions as the number of iterations increases. Specifically, the Metropolis acceptance criterion... This can be expressed by the following formula:

[0198] ;

[0199] in, This refers to the difference between the objective function values ​​of the current solution and the new solution. The current temperature represents the "width" of the search process, initially higher and gradually decreasing with iterations. This criterion allows the algorithm to accept poor solutions in the early stages, explore a wider solution space, and gradually converge to a local optimum by decreasing the temperature.

[0200] Step S304e: Perform an iterative solution process for a preset number of times, output the final solution of the mixed integer programming model, and obtain the collaborative scheduling result corresponding to the final solution.

[0201] In this embodiment, the iterative solution process is executed for a preset number of times. The calculation stops when the maximum number of iterations or the maximum number of iterations without improvement is reached, and the final solution of the mixed integer programming model is output to obtain the collaborative scheduling result corresponding to the final solution.

[0202] In this embodiment, the final solution is the optimal solution output by the last iteration.

[0203] In this embodiment, after obtaining the cooperative scheduling result corresponding to the final solution, the method further includes:

[0204] Sensitivity analysis was conducted by adjusting the maximum V2V charging power of electric buses (20kW-80kW) and the energy consumption per unit mile of electric vehicles (0.1kW·h / km-0.2kW·h / km) to analyze the trend of total cost changes.

[0205] Cost comparison: By comparing with the traditional fast charging mode, the advantages of the collaborative charging mode in terms of total cost, charging infrastructure deployment cost and electric vehicle travel cost are verified.

[0206] like Figure 1 As shown, this embodiment of the invention provides a method for coordinated charging of electric buses and electric vehicles, including the following steps:

[0207] Step S400: Based on the coordinated scheduling result, schedule the electric bus and the electric vehicle, and wirelessly charge the electric vehicle using the scheduled electric bus.

[0208] It should be noted that before scheduling the electric buses and electric vehicles through the collaborative scheduling results, and wirelessly charging the electric vehicles through the scheduled electric buses, the process also includes: determining the modification plan for the electric buses based on the collaborative scheduling results.

[0209] It should be noted that achieving coordinated charging between electric buses and electric vehicles refers to wirelessly charging electric vehicles using the power supply of electric buses. This is a core means of balancing "bus operation safety" and "timely charging of electric vehicles".

[0210] like Figure 3 The diagram shown illustrates the wireless charging principle of the dispatched electric bus for electric vehicles in this embodiment, reflecting the principle of wireless charging in coordinated dispatch charging, which satisfies the following conditions:

[0211] Constructing an energy flow, using electric buses as mobile power stations;

[0212] Use a coupling method based on the dynamic standard of "step-by-step coupling";

[0213] Constructing an information flow between electric vehicles and electric buses;

[0214] Construct a control flow that prioritizes safety and has appropriate power.

[0215] Specifically, in one implementation of this embodiment, step S400 includes the following steps:

[0216] Step S401: Determine the conversion plan for electric buses based on the collaborative scheduling results.

[0217] In this embodiment, the coordinated scheduling results of electric buses and electric vehicles in the target area also include the transformation plan for electric buses. Through a mixed integer programming model, the feasibility and economy of transforming electric buses that have not been transformed are evaluated based on bus route type, vehicle parameters, driving speed, operating mileage and the charging demand distribution of electric vehicles. Buses with transformation value are selected for V2V wireless charging transformation, and suitable wireless charging coils and inverters are configured to ensure that the transformed buses have the ability to supply power to electric vehicles while meeting their own operating power needs.

[0218] Specifically, a mixed-integer programming model is used to prioritize electric buses in the target area that have high operating frequency, routes covering densely populated areas of electric vehicle travel, and low unit modification costs. The resulting set of electric buses is then used to select modification schemes, and V2V wireless charging is applied to the electric buses in the modification schemes.

[0219] Step S402: Based on the coordinated scheduling result, schedule the electric bus and the electric vehicle.

[0220] In this embodiment, based on the collaborative scheduling result, an information flow between electric vehicles and electric buses is constructed to schedule the electric buses and electric vehicles.

[0221] Specifically, the construction of an information flow based on electric vehicles and electric buses includes:

[0222] The system obtains the demand for electric vehicles by automatically calculating the "recharge demand during the journey" based on the navigation destination, remaining battery power, and traffic congestion ahead through the vehicle's onboard terminal. The system then broadcasts a request message containing the user's identity hash, the required battery power, and a segment of the expected driving trajectory via V2X.

[0223] The "power supply capacity" of the electric buses after V2V wireless charging is periodically broadcast via resource broadcast. The message includes the remaining discharge capacity, maximum continuous power, current route, and arrival time at the next stop.

[0224] Based on request messages and broadcast messages, the edge computing nodes complete the calculation of the "two-vehicle encounter window" in the digital twin road network of the target area. That is, only when the two vehicles have a continuous shared road segment of greater than or equal to a preset kilometer within the next 5-10 minutes, and the relative speed difference is less than or equal to the minimum threshold of relative speed difference, will the "pairing successful" command be issued, and suggestions such as vehicle speed, lane, and charging start and stop time will be given.

[0225] Closed-loop tracking is implemented. After successful pairing, the paired electric bus and electric vehicle enter the "energy platooning" mode. The output voltage, current, and phase of the bus inverter are adjusted in real time through a closed-loop wireless communication link to maintain optimal power transmission. The electric vehicle can leave the platoon at any time for safety or comfort reasons. If the electric vehicle leaves the platoon, the electric bus immediately shuts off its transmitting coil.

[0226] In step S403, the inverter of the dispatched electric bus converts the DC power from the on-board power battery into high-frequency AC power, and forms an alternating magnetic field around the transmitting coil group of the electric bus.

[0227] In this embodiment, by constructing an energy flow, the dispatched electric bus is used as a mobile power station. The inverter converts the DC power from the on-board power battery into high-frequency AC power and forms an alternating magnetic field around the transmitting coil group of the electric bus.

[0228] Specifically, the energy flow is constructed by using electric buses as mobile power stations, including:

[0229] While fulfilling their operational duties, electric buses are transformed into "mobile distributed wireless charging stations." A high-power transmitting coil array is installed at the bottom of the electric bus. An inverter converts the direct current (DC) from the onboard battery into high-frequency alternating current (AC), creating an alternating magnetic field around the coil. A receiving coil is installed at the bottom of the electric vehicle requiring charging. When both vehicles travel in the same lane at similar speeds, the alternating magnetic field passes through the receiving coil, inducing a current that charges the battery after passing through the onboard rectifier. The entire energy path is: electric bus battery → inverter → transmitting coil → magnetic field coupling → receiving coil → rectifier → electric vehicle battery.

[0230] Step S404: Control the electric vehicle to travel within a preset range in the same lane as the dispatched electric bus, so as to charge the battery of the electric vehicle through the induced current generated by the alternating magnetic field.

[0231] In this embodiment, the electric vehicle is scheduled to travel within a preset range in the same lane as the scheduled electric bus. The alternating magnetic field passes through the receiving coil of the electric vehicle to generate an induced current. The scheduled electric vehicle and the scheduled electric bus use a coupling method based on the dynamic standard of "coupling while moving".

[0232] Specifically, the coupling method based on the "step-by-step coupling" dynamic standard includes:

[0233] To overcome lateral deviation, longitudinal undulation, and changes in front and rear distance during driving, multiple coils are arranged in sections. Several independent coil units are arranged longitudinally on the transmitting side. The vehicle control unit of the electric bus is powered on in a "segmented relay" manner according to the real-time position of the receiving vehicle, ensuring that only the coils within the effective coupling window work, thereby reducing magnetic leakage and heat loss.

[0234] Magnetic shielding and magnetic guiding mechanisms are set up, and ferrite or soft magnetic composite materials are added around the coil and between the vehicle body of the electric bus to form a low magnetic resistance channel, which confines the magnetic lines of force within the transmitting and receiving gap and reduces interference to the on-board electronic equipment.

[0235] Using a mechanical servo mechanism, the transmitting coil of the electric bus can be suspended on a servo lowering plate. The vehicle's relative lateral offset is measured in real time by vision or millimeter-wave radar. The servo mechanism automatically centers within ±10cm of the coil mounting bracket to improve the coupling coefficient.

[0236] In this embodiment, the induced current is processed by the rectifier of the electric vehicle to charge the battery of the electric vehicle, including: processing the induced current by the rectifier of the electric vehicle to construct a control flow that prioritizes safety and has appropriate power to charge the battery of the electric vehicle.

[0237] Specifically, constructing a safety-priority, power-appropriate control flow includes:

[0238] The electric bus features a segmented soft start system. During the power supply process, the system first pre-charges with 5%–10% of the target power. After detecting foreign objects, deviations, and temperature rises within the allowable range, it increases the power in increments of 20%, with the entire process taking less than 2 seconds.

[0239] Two-way identity authentication and access control: ECDSA two-way authentication is completed using an automotive-grade security chip to prevent "fake base stations" or "fake buses" from inducing charging. The session key is set to one-time password and expires immediately after charging is completed.

[0240] The fault-safety strategy is as follows: once the coil temperature rise exceeds the limit, the relative offset between the transmitting and receiving ends is greater than the preset threshold, or the bus's own SOC is lower than the safety red line, the electric bus's on-board system immediately cuts off the inverter PWM, lowers the board to retract the coil, and uploads the fault code to the dispatch platform, completing the disconnection within 0.3 seconds.

[0241] This embodiment achieves the following technical effects through the above technical solution:

[0242] Unlike traditional technologies that predict collective charging demand for fixed fast-charging stations or single vehicle models, this embodiment can construct a collaborative travel chain of "electric bus - electric vehicle" based on the historical coupled travel data of specific target electric buses and electric vehicles. This allows for simultaneous decision-making on the coordinated power supply arrangements and specific charging times between electric buses and electric vehicles, achieving refined collaborative charging scheduling at the "individual level - road segment level - time level." This solves the technical problem that existing urban charging infrastructure planning and bus scheduling operations cannot meet the matching needs of individual "mobile charging" needs, improving the utilization rate of charging resources and the timeliness of electric vehicle users' charging, and satisfying the refined travel needs of electric vehicles for "charging on the go, no need to detour, and immediate departure."

[0243] Exemplary device

[0244] Based on the above embodiments, the present invention also provides a coordinated charging system for electric buses and electric vehicles, comprising:

[0245] The parameter acquisition module is used to acquire electric bus data and electric vehicle data for the target area; wherein, the electric bus data includes bus route data and electric bus parameters, and the electric vehicle data includes vehicle route data and electric vehicle parameters;

[0246] The model building module is used to construct a mixed integer programming model based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters, using an objective function and constraints.

[0247] The model solving module is used to solve the mixed integer programming model using an adaptive large neighborhood search algorithm to obtain the coordinated scheduling results of electric buses and electric vehicles in the target area;

[0248] The scheduling execution module is used to schedule the electric bus and the electric vehicle according to the coordinated scheduling result, and to wirelessly charge the electric vehicle through the scheduled electric bus.

[0249] Based on the above embodiments, the present invention also provides a charging terminal for coordinated dispatching of electric buses and electric vehicles, the principle block diagram of which is as follows: Figure 4 As shown.

[0250] The terminal includes: a processor, a memory, an interface, a display screen, and a communication module connected via a system bus; wherein, the processor of the terminal provides computing and control capabilities; the memory of the terminal includes a computer-readable storage medium and internal memory; the computer-readable storage medium stores an operating system and computer programs; the internal memory provides an environment for the operation of the operating system and computer programs in the computer-readable storage medium; the interface is used to connect to external devices; the display screen is used to display relevant information; and the communication module is used to communicate with a cloud server or other devices.

[0251] When executed by the processor, this computer program is used to implement the operation of a coordinated charging method for electric buses and electric vehicles.

[0252] It will be understood by those skilled in the art that Figure 4 The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0253] In one embodiment, a terminal is provided, comprising: a processor and a memory, the memory storing a coordinated charging program for electric buses and electric vehicles, which, when executed by the processor, is used to implement the operation of the coordinated charging method for electric buses and electric vehicles as described above.

[0254] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a program for coordinated scheduling and charging of electric buses and electric vehicles, which, when executed by a processor, is used to implement the operation of the above-described coordinated scheduling and charging method for electric buses and electric vehicles.

[0255] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, database, or other media used in the embodiments provided by this invention can include both non-volatile and volatile memory.

[0256] In summary, this invention provides a method and system for coordinated scheduling and charging of electric buses and electric vehicles, comprising: acquiring electric bus data and electric vehicle data for a target area; wherein, the electric bus data includes bus route data and electric bus parameters, and the electric vehicle data includes vehicle route data and electric vehicle parameters; constructing a mixed-integer programming model based on the bus route data, electric bus parameters, vehicle route data, and electric vehicle parameters, using an objective function and constraints; solving the mixed-integer programming model using an adaptive large neighborhood search algorithm to obtain the coordinated scheduling result of electric buses and electric vehicles in the target area; scheduling electric buses and electric vehicles according to the coordinated scheduling result, and wirelessly charging electric vehicles through the scheduled electric buses; this invention can realize coordinated scheduling and charging of electric buses and electric vehicles in large-scale scenarios.

[0257] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for coordinating charging of electric buses and electric vehicles, characterized in that, include: Acquire data on electric buses and electric vehicles in the target area; wherein, the electric bus data includes bus route data and electric bus parameters, and the electric vehicle data includes vehicle route data and electric vehicle parameters; Based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters, a mixed integer programming model is constructed using the objective function and constraints. The mixed integer programming model is solved using an adaptive large neighborhood search algorithm to obtain the coordinated scheduling results of electric buses and electric vehicles in the target area; Based on the collaborative scheduling results, the electric bus and the electric vehicle are scheduled, and the electric bus wirelessly charges the electric vehicle. The method for coordinated charging of electric buses and electric vehicles also includes: Based on bus route data, electric bus parameters, and electric vehicle parameters, the conversion cost of electric buses, the energy cost and time cost of charging electric vehicles are calculated, and an objective function representing the cost of coordinated scheduling charging based on the sum of the three costs is obtained. ； wherein, is an objective function; is a conversion cost of the electric bus; is an energy cost of charging the electric vehicle; is a time cost of charging the electric vehicle; ； in, Indicates the modification of a route The cost of taking the bus, in yuan; Represents a 0-1 variable, if the route electric buses If modifications are made, ,otherwise ; ； in, Indicate route electric buses The power supply capacity of V2V wireless power supply; This indicates the charging efficiency of an electric vehicle being charged by an electric bus. Indicate route electric buses The itinerary, corresponding itinerary set ; and For itinerary collection The set of nodes in all routes ; For electric vehicles, the corresponding set of electric vehicles ; Representing 0-1 variables, if electric vehicles On the road section By route electric buses On the trip Charging, ,otherwise ; This is a two-dimensional matrix representing the road network. Click Distance between points; For the route electric buses The speed of travel; For electric buses The number of days corresponding to the service life; For electric vehicles The unit charging electricity price; ； in, This is the conversion factor for time to monetary cost, expressed in yuan per hour. For electric vehicles Reaching the node At that moment, For electric vehicles The starting point is the location of the starting point. For electric vehicles The destination location is the final stop. Electric vehicles The moment of arrival at the starting point, Electric vehicles The time of arrival at the destination; The process of constructing a mixed-integer programming model based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters, using an objective function and constraints, includes: Based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters, determine the objective function and constraints of the mixed integer programming model; Construct a mixed-integer programming model based on the objective function and the constraints; The method of using an adaptive large neighborhood search algorithm to solve the mixed integer programming model to obtain the coordinated scheduling results of electric buses and electric vehicles in the target area includes: An initial solution is generated based on the principle of proximity; wherein, the initial solution is used to match the electric bus with the electric vehicle and plan the driving route; Generate a destruction operator; wherein the destruction operator is used to destroy the solution corresponding to the destruction operator in the mixed integer programming model; Generate an insertion operator; wherein the insertion operator is used to repair the corrupted solution in the mixed integer programming model; Based on the destruction operator and the insertion operator, an iterative solution process is performed a preset number of times to output the final solution of the mixed integer programming model, and the cooperative scheduling result corresponding to the final solution is obtained.

2. The method for coordinated charging of electric buses and electric vehicles according to claim 1, characterized in that, The bus route data includes one or more combinations of the following: travel range and speed requirements for rechargeable sections; the electric bus parameters include one or more combinations of the following: wireless charging retrofit status, bus model, battery capacity, service frequency, and time-of-use electricity pricing.

3. The method for coordinated charging of electric buses and electric vehicles according to claim 1, characterized in that, The vehicle route data includes one or more combinations of origin and destination points and departure time; the electric vehicle parameters include one or more combinations of rechargeable window, battery status, and wireless charging cost.

4. The method for coordinated charging of electric buses and electric vehicles according to claim 1, characterized in that, The iterative solution process based on the destruction operator and the insertion operator, performed a preset number of times, outputs the final solution of the mixed integer programming model, and obtains the cooperative scheduling result corresponding to the final solution, including: Determine the first weight of the destruction operator and the second weight of the insertion operator; In each iterative solution process, a first probability of each destruction operator being selected is calculated based on the first weight, and a second probability of each insertion operator being selected is calculated based on the second weight; Based on the first probability and the second probability, the destruction operator and the insertion operator are randomly selected, the solution corresponding to the selected destruction operator is destroyed, the destroyed solution is repaired according to the selected insertion operator, and a new solution is output; Obtain the optimal solution of the current mixed integer programming model, compare the new solution with the optimal solution, update or retain the optimal solution based on the comparison result, update the first weight based on the selection of the destruction operator, and update the second weight based on the selection of the insertion operator; wherein, the optimal solution of the first iteration solution process is the initial solution; The system performs an iterative solution process a preset number of times, outputs the final solution of the mixed integer programming model, and obtains the collaborative scheduling result corresponding to the final solution; wherein, the final solution is the optimal solution output by the last iterative solution process.

5. The method for coordinated charging of electric buses and electric vehicles according to claim 1, characterized in that, The wireless charging of the electric vehicle by the dispatched electric bus includes: The inverter of the dispatched electric bus converts the DC power from the on-board power battery into high-frequency AC power, and creates an alternating magnetic field around the transmitting coil group of the electric bus. The electric vehicle is controlled to travel within a preset range in the same lane as the dispatched electric bus, so as to charge the battery of the electric vehicle through the induced current generated by the alternating magnetic field.

6. A coordinated charging system for electric buses and electric vehicles, used to implement the coordinated charging method for electric buses and electric vehicles as described in any one of claims 1-5, characterized in that, include: The parameter acquisition module is used to acquire electric bus data and electric vehicle data for the target area; wherein, the electric bus data includes bus route data and electric bus parameters, and the electric vehicle data includes vehicle route data and electric vehicle parameters; The model building module is used to construct a mixed integer programming model based on the bus route data, the electric bus parameters, the car route data, and the electric vehicle parameters, using an objective function and constraints. The model solving module is used to solve the mixed integer programming model using an adaptive large neighborhood search algorithm to obtain the coordinated scheduling results of electric buses and electric vehicles in the target area; The scheduling execution module is used to schedule the electric bus and the electric vehicle according to the coordinated scheduling result, and to wirelessly charge the electric vehicle through the scheduled electric bus.

7. A collaborative charging terminal for electric buses and electric vehicles, characterized in that, include: The processor and memory, wherein the memory stores a program for coordinated scheduling and charging of electric buses and electric vehicles, and the program for coordinated scheduling and charging of electric buses and electric vehicles, when executed by the processor, is used to implement the operation of the coordinated scheduling and charging method for electric buses and electric vehicles as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for coordinated scheduling and charging of electric buses and electric vehicles. When executed by a processor, the program is used to implement the operation of the coordinated scheduling and charging method for electric buses and electric vehicles as described in any one of claims 1-5.

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