Cooperative scheduling method for electric buses under the time-sharing mode of charging facilities

Abstract

This invention relates to a collaborative scheduling method for electric buses under a time-sharing charging facility model. It belongs to the field of urban traffic management technology, specifically concerning a collaborative scheduling method for electric buses. The purpose of this invention is to address the problems of existing methods, which are geared towards single electric bus routes and neglect the fluctuations in private car charging demand at different times. This can easily lead to low utilization rates due to an excessive number of charging facilities allocated during certain periods, or long waiting times for private cars due to an insufficient number of charging facilities allocated. The process involves: constructing a collaborative optimization model of bus scheduling and charging facility sharing schemes based on expressions for the daily charging cost of electric bus routes and the estimated revenue obtained by bus companies from sharing charging piles with electric cars; solving the collaborative optimization model to obtain the optimal feasible solution and the corresponding objective function value; the optimal feasible solution includes both the electric bus scheduling scheme and the charging facility sharing scheme.

Description

Technical Field

[0001] This invention belongs to the field of urban traffic management technology, specifically relating to a collaborative scheduling method for electric buses. Background Technology

[0002] Compared to gasoline-powered buses, electric buses suffer from limited range and longer charging times. To meet the charging needs of electric buses, bus companies typically build charging stations shared by vehicles on multiple routes, equipped with a sufficient number of charging piles to ensure charging needs are met. However, during most operating hours, buses are on their routes, leaving many charging facilities at stations idle. Meanwhile, the number of electric vehicles in my country has been steadily increasing in recent years, but the construction of charging infrastructure is struggling to meet the growing demand. The time-sharing model for charging facilities involves bus companies, based on their operational plans, systematically sharing idle charging facilities with electric vehicles for a fee. This improves the utilization rate of charging facilities at stations and generates additional revenue for the bus company. However, this sharing model will inevitably affect the charging schedules of electric buses, leading to changes in vehicle scheduling and charging plans for each route. To minimize the impact on electric bus operations while maximizing the revenue of bus companies, it is necessary to establish a vehicle collaborative scheduling optimization method for multiple electric bus routes with charging stations.

[0003] Existing technologies have considered the impact of charging facility sharing on the operation of electric buses, but they are all geared towards single electric bus routes. In actual urban public transport systems, charging stations are deployed within bus depots for shared use by buses on multiple routes. Once the charging facility sharing model is implemented, it will simultaneously affect the operation and scheduling of multiple routes. Furthermore, existing technologies divide the day into multiple time periods, setting a fixed number of charging stations available for private vehicles in each period. This ignores the fluctuations in private vehicle charging demand during different time periods, potentially leading to problems such as low utilization rates due to an excessive number of charging stations allocated during certain periods, or long waiting times for private vehicles due to an insufficient number of charging stations. Summary of the Invention

[0004] The purpose of this invention is to address the problems in existing technologies that consider the impact of charging facility sharing on the operation of electric buses, but are all geared towards single electric bus routes. Furthermore, existing technologies divide the day into multiple time periods, setting a fixed number of charging stations available for private cars in each period, ignoring the fluctuations in private car charging demand during different time periods. This can easily lead to low utilization rates due to an excessive number of charging facilities allocated in certain periods, or long waiting times for private cars due to an insufficient number of charging facilities allocated. Therefore, this invention proposes a collaborative scheduling method for electric buses under a time-sharing charging facility model.

[0005] The specific process of the collaborative scheduling method for electric buses under the time-sharing mode of charging facilities is as follows:

[0006] Step 1: Construct the daily charging cost for electric bus routes Estimated revenue for public transportation companies from sharing charging stations with electric vehicles daily. The expression;

[0007] Step 2: Based on the daily charging cost of electric bus routes Estimated revenue for public transportation companies from sharing charging stations with electric vehicles daily. The expression is used to construct a collaborative optimization model for bus dispatching schemes and charging facility sharing schemes;

[0008] Step 3: Solve the collaborative optimization model of the bus dispatching scheme and the charging facility sharing scheme to obtain the optimal feasible solution and the corresponding objective function value; the optimal feasible solution includes the electric bus dispatching scheme and the charging facility sharing scheme.

[0009] The beneficial effects of this invention are as follows:

[0010] This invention takes a multi-line electric bus system under a charging facility sharing model as the research object. In order to address the problem of mismatch between the electric bus dispatching scheme and the charging facility sharing scheme during operation, it proposes a collaborative dispatching method for urban multi-line electric buses under a charging facility time-sharing sharing model.

[0011] The proposed method comprehensively considers the charging needs of electric buses and electric cars on multiple routes at different times, dynamically adjusts the number of shared charging facilities for each time period, maximizes the utilization rate of charging facilities and the revenue of bus companies while ensuring the normal operation of electric buses, and solves the problems of low utilization rate caused by excessive allocation of charging facilities at certain times, and long waiting time for private cars to charge due to insufficient allocation of charging facilities; at the same time, it provides more charging options for electric car users and alleviates the shortage of public charging resources. Attached Figure Description

[0012] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0013] Specific Implementation Method 1: The specific process of the collaborative scheduling method for electric buses under the time-sharing mode of charging facilities in this implementation method is as follows:

[0014] Step 1: Construct the daily charging cost for electric bus routes Estimated revenue for public transportation companies from sharing charging stations with electric vehicles daily. The expression;

[0015] Step 2: Based on the daily charging cost of electric bus routes Estimated revenue for public transportation companies from sharing charging stations with electric vehicles daily. The expression is used to construct a collaborative optimization model for bus dispatching schemes and charging facility sharing schemes;

[0016] Step 3: Solve the collaborative optimization model of the bus dispatching scheme and the charging facility sharing scheme to obtain the optimal feasible solution and the corresponding objective function value; the optimal feasible solution includes the electric bus dispatching scheme and the charging facility sharing scheme.

[0017] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that it covers the daily charging cost of the electric bus route. The expression is:

[0018] (1)

[0019] In the formula: using Indicates charging distance It is an electric bus Completed shift The subsequent charging process, otherwise ; , Total number of charging trips; , for The total number of electric buses on each electric bus route; , for The total number of daily trips on each electric bus route. The total number of electric bus routes serving a single bus charging station; For electric buses During the charging process The charging cost;

[0020] The analysis period is defined as the time from the departure time of the first bus of the day to the departure time of the first bus of the following day; the analysis period is discretized at 1-minute intervals, using... This indicates the start time of each discrete time period;

[0021] The first and last stops of a bus route near the depot are called the starting station, and the other first and last stop is called the terminal station. A complete bus trip is defined as a round trip of an electric bus from the starting station to the terminal station and back to the starting station. A charging trip is defined as the time from the start of charging to the end of charging for an electric bus.

[0022] Based on the relationship between the charging trip time span and the time-of-use electricity pricing period The calculation is considered in three cases:

[0023] Scenario 1: Charging trip It occurred within a time-of-use pricing period. The expression is:

[0024]

[0025] In the formula: Time-of-use electricity pricing period Electricity price within the region, in yuan / kWh; For electric buses Charging trip The moment charging begins. It is an integer variable; For electric buses Charging trip The moment when charging ends; The charging power for electric buses, measured in kW; Time-of-use electricity pricing period At the beginning of Time-of-use electricity pricing period The end moment.

[0026] Scenario 2: Charging trip Spanning two time-of-use pricing periods, The expression is:

[0027]

[0028] In the formula: Time-of-use electricity pricing period Electricity price within the region, in yuan / kWh; Time-of-use electricity pricing period The end time;

[0029] Scenario 3: Charging trip Spanning more than two time-of-use pricing periods, The expression is:

[0030]

[0031] In the formula: For charging trip The number of time-of-use pricing periods spanned; For the first Time-of-use electricity pricing periods; Time-of-use electricity pricing period Electricity price within the region, in yuan / kWh; Time-of-use electricity pricing period The end time; Time-of-use electricity pricing period The beginning moment; Time-of-use electricity pricing period Electricity price within the region, in yuan / kWh; Time-of-use electricity pricing period The beginning moment; Time-of-use electricity pricing period The end time;

[0032] The unit is yuan; the daily time-of-use electricity price period set is denoted as... .

[0033] The other steps and parameters are the same as in Specific Implementation Method 1.

[0034] Specific Implementation Method Three: This implementation method differs from Specific Implementation Method One or Two in that it includes the estimated daily revenue that the public transport company receives from sharing charging stations for electric vehicles. The expression is:

[0035] (2)

[0036] In the formula: For public transport companies within the time window Estimated revenue from shared charging stations for inward-facing electric vehicles. This refers to the maximum value of the time window numbering for dividing the analysis period into time windows with a certain time interval.

[0037] Other steps and parameters are the same as in specific implementation method one or two.

[0038] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that it includes the estimated daily revenue that the public transport company receives from sharing charging stations for electric vehicles. The process of obtaining the expression is as follows:

[0039] 1) Set up charging stations within the time window Internal sharing The service intensity of each charging station is recorded as follows: , ; For time windows Average arrival rate of internal electric vehicles; For charging service rate; The number of charging stations available to the public at each charging station within each time window. ; The number of charging piles configured within the charging station; For time windows The average charging time for electric cars, in hours (h).

[0040] Define blocking probability To calculate the probability that an electric car driver will be refused service due to a full charging station when there is no waiting queue in the service system, formula (3) is used. :

[0041] (3)

[0042] Alternatively, the blocking probability can be calculated using a recursive method. :

[0043] (4)

[0044] (5)

[0045] In the formula: For time windows The number of charging piles open at the charging stations is: The probability of blocking at that time; and Time windows The number of charging piles open at the charging station is: The probability of blocking and the service strength at that time; For time windows The probability of congestion when the number of open charging piles at a charging station is 0. The factorial symbol; For the first indivual, ;

[0046] 2) Calculate the probability that the waiting time for an electric car arriving at the charging station is greater than 0. :

[0047] (6)

[0048] In the formula: For user-defined operators; The parameter representing the driver's impatience; The actual waiting time for drivers of electric cars; The expression is: , The maximum acceptable waiting time for electric vehicle drivers at charging stations, expressed in hours (h). The mean is , The parameter representing the driver's impatience; The unit is h; if the waiting time for electric car drivers at charging stations exceeds the maximum acceptable waiting time. If they still cannot get charging service, the driver will choose to leave and go to other charging stations; This represents the time a driver must wait to obtain charging service (assuming all car drivers have unlimited patience). The unit is h; The unit is h;

[0049]

[0050]

[0051] In the formula: and All are user-defined operators; , ;

[0052] 3) Calculate the probability that an electric car with a waiting time greater than 0 will forgo charging service. :

[0053] (7)

[0054] 4) Calculate the probability of an electric vehicle abandoning charging services based on 2) and 3). ;

[0055] 5) Calculate the time window for public transport companies Estimated revenue from shared charging stations for inward-facing electric vehicles ;

[0056] 6) Calculate the estimated daily revenue that public transport companies will receive from shared charging stations for electric vehicles. .

[0057] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0058] Specific Implementation Method Five: This implementation method differs from Specific Implementation Methods One to Four in that, in step 4), the probability of an electric vehicle abandoning the charging service is calculated based on steps 2) and 3). ; indicates as:

[0059] (8).

[0060] The other steps and parameters are the same as those in one of the specific implementation methods one to four.

[0061] Specific Implementation Method Six: This implementation method differs from Specific Implementation Methods One to Five in that, in step 5), the calculation of the bus company within the time window... Estimated revenue from shared charging stations for inward-facing electric vehicles The expression is:

[0062] (9)

[0063] In the formula: For public transport companies within the time window The service fee charged internally is in yuan / kWh; The charging power for electric cars, measured in kW; For time windows The duration, in hours; For time windows The expected total charging amount of the electric cars arriving within the area; For time windows The maximum amount of electricity that the charging stations shared with electric vehicles can provide.

[0064] The other steps and parameters are the same as those in one of the specific implementation methods one to five.

[0065] Specific Implementation Method Seven: This implementation method differs from Specific Implementation Methods One through Six in that the specific process of step 2 is as follows:

[0066] Step 2.1: Minimize the daily charging cost of electric bus routes. Estimated revenue for public transportation companies from sharing charging stations with electric vehicles daily. To optimize the target As shown in formula (10):

[0067] (10)

[0068] When electric car drivers use the charging facilities at electric bus depots, they need to pay a usage fee, which consists of two parts: electricity cost and service fee. The electricity cost is calculated based on the actual charging amount and the time-of-use electricity price during the charging period, reflecting the basic cost paid by the car owner for using electricity. The electricity cost does not belong to the bus company's profit, while the service fee is an additional fee charged by the bus company for providing charging services, so only the service fee revenue needs to be calculated.

[0069] Daily charging costs for electric bus companies Estimated daily revenue for public transport companies from shared charging stations for electric vehicles.

[0070]

[0071] Step 2.2: Set the constraints of the optimization model, as shown in formulas (11)-(20):

[0072] (11)

[0073] (12)

[0074] (13)

[0075] (14)

[0076] (15)

[0077] (16)

[0078] (17)

[0079] (18)

[0080] (19)

[0081] (20)

[0082] In the formula: For electric buses Whether to implement shift schedule If electric buses Shift schedule ,but ,otherwise ; For electric buses At any moment The running status, Indicates at time the bus Running on the line, otherwise ; For electric buses At any moment The charging status, Indicates at time the bus Charging, otherwise ; For train schedules The departure time For train schedules Departure time; For electric buses In the schedule The travel time is in minutes. For time windows The beginning moment; For time windows The end time; For electric buses The rated capacity of the battery, in kWh; , These represent the lower and upper bounds of the permissible variation in the state of charge (SBC) of electric bus batteries, with an interval of [range]. ; For electric buses At any moment The remaining electricity, in kWh; This represents the minimum charging time, expressed in minutes. For electric buses During the charging process The charging time in the middle, The unit is min; For electric buses Shift schedule The energy consumption of the journey, in kWh; For electric buses The remaining electricity at time 0, in kWh; For electric buses Remaining power after 1439 minutes, in kWh; For train schedule Electric bus routes The relationship between shifts Electric bus route ,but ,otherwise ; For electric buses Is it on the line? If electric buses are put into operation... On the line Up to operation, then ,otherwise ; For electric buses Whether to implement shift schedule If electric buses Shift schedule ,but ,otherwise .

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] In the formula, For electric buses Charging trip The moment when charging begins; For electric buses Charging trip The moment when charging ends; For electric buses Charging trip Duration, The unit is min; For electric buses Whether to utilize the charging period Charging is required for electric buses. Utilizing charging time Charging, ,otherwise ; For electric buses The departure times for the required shifts are as follows: The energy consumption of previous shifts is accumulated; For electric buses During the scheduled charging trip, the charging start time is at time [time missing]. The charging amount from previous charging trips is added up; For electric buses During the charging process The amount of electricity charged into the device is measured in kWh. For electric buses Whether to utilize the charging period Charging is required for electric buses. Utilizing charging time Charging, ,otherwise .

[0089] The other steps and parameters are the same as those in one of the specific implementation methods one to six.

[0090] Specific Implementation Method Eight: This implementation method differs from Specific Implementation Methods One to Seven in that the specific process of step 3 is as follows:

[0091] Step 3.1: Develop an electric bus dispatching plan as the initial vehicle dispatching plan, and use the minimum number of remaining charging piles at each time window in the initial vehicle dispatching plan as the number of charging piles shared with electric cars to construct the initial charging facility sharing plan.

[0092] The initial vehicle scheduling scheme and the initial charging facility sharing scheme are combined to form the initial feasible solution. The initial feasible solution serves as the root node in the branch and bound algorithm and is also the node to be branched at.

[0093] Let the current optimal feasible solution be The objective function value corresponding to the current optimal feasible solution is ;

[0094] Step 3.2, let the number of iterations be... The initial lower bound of the branch and bound algorithm Set it to 0, and use the objective function value of the initial feasible solution. As the initial upper bound ;

[0095] Step 3.3: Take the collaborative optimization model of vehicle scheduling scheme and charging facility sharing scheme as the original problem, and decompose the original problem into a restricted master problem;

[0096] Step 3.4: Solve the restricted master problem, and construct the pricing subproblem based on the solution to the restricted master problem;

[0097] Step 3.5: Solve the pricing subproblem and determine whether the test number of the optimal solution to the pricing subproblem is negative;

[0098] If the test number of the optimal solution to the pricing subproblem is negative, add all vehicle scheduling schemes with negative test numbers from the process of solving the pricing subproblem to the restricted main problem, and return to step 3.4;

[0099] If the test number of the optimal solution to the pricing subproblem is positive, then the test numbers of all vehicle scheduling schemes generated during the solution of the pricing subproblem are positive, and proceed to step 3.6;

[0100] Step 3.6, place the first The optimal solution to the restricted principal problem at the node of the round of iterations is denoted as: , The corresponding objective function value is denoted as ;

[0101] Step 3.7, Judgment:

[0102] if ,and If it is not an integer, proceed to step 3.8;

[0103] if ,and If it is 0 or 1, then update. , Trim the current node and proceed to step 3.10;

[0104] if Trim the current node and proceed to step 3.10;

[0105] Indicates selection of electric bus ; Indicates not to choose electric buses ;

[0106] Step 3.8, Calculation Select the value closest to 0.5. Find the value closest to 0.5. Corresponding bus and shift ;in, As an intermediate variable;

[0107] Then, constraints are added to the pricing subproblem of the current node. To construct the left child node, constraints are added to the pricing subproblem of the current node. To construct the right child node; let the left and right child nodes be... ;

[0108] Step 3.9: Set the left child node as the new current node, add the right child node to the list of nodes to be processed, and update the iteration count. Return to step 3.4;

[0109] Step 3.10: Determine if the current node list still contains nodes to be processed.

[0110] If the list of nodes to be processed is not empty, select the node with the smallest LB value in the list as the new current node, and update the iteration count. Return to step 3.4; if the list of nodes to be processed is empty, proceed to step 3.11;

[0111] Step 3.11: Record the optimal feasible solution and the corresponding objective function value Optimal feasible solution This includes electric bus dispatching schemes and charging facility sharing schemes.

[0112] The other steps and parameters are the same as those in any of the specific implementation methods one to seven.

[0113] Specific Implementation Method Nine: This implementation method differs from Specific Implementation Methods One to Eight in that: in step 3.3, the collaborative optimization model of vehicle scheduling scheme and charging facility sharing scheme is taken as the original problem, and the original problem is decomposed into a restricted master problem;

[0114] The model for the restricted principal problem is as follows:

[0115] (twenty one)

[0116] st (twenty two)

[0117] (twenty three)

[0118] (twenty four)

[0119] in, For electric buses The charging cost; st is a constraint.

[0120] The other steps and parameters are the same as those in one of the specific implementation methods one to eight.

[0121] Specific Implementation Method Ten: This implementation method differs from Specific Implementation Methods One through Nine in that: in step 3.4, the restricted master problem is solved, and a pricing subproblem is constructed based on the solution; the specific process is as follows:

[0122] Solve the restricted master problem to obtain the dual variables of constraint (22). The dual variables of constraint (23) ;

[0123] Dual variables based on constraint (22) The dual variables of constraint (23) Construct a pricing subproblem; the pricing subproblem model is as follows:

[0124] (25)

[0125] st (26)

[0126] (27)

[0127] (28)

[0128] (29)

[0129] (30)

[0130] (31)

[0131] (32)

[0132] (33)

[0133] The other steps and parameters are the same as those in any of the specific implementation methods one to nine.

[0134] This invention may have other embodiments. Without departing from the spirit and essence of this invention, those skilled in the art can make various corresponding changes and modifications according to this invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A method for collaborative scheduling of electric buses under a time-sharing charging facility model, characterized in that: The specific process of the method is as follows: Step 1: Construct expressions for the daily charging cost Z1 of electric bus routes and the estimated value Z2 of the daily revenue that bus companies receive from sharing charging stations with electric cars. Step 2: Based on the expressions for the daily charging cost Z1 of electric bus routes and the estimated value Z2 of the revenue obtained by bus companies from sharing charging piles with electric cars, construct a collaborative optimization model for bus dispatching schemes and charging facility sharing schemes. Step 3: Solve the collaborative optimization model of the bus dispatching scheme and the charging facility sharing scheme to obtain the optimal feasible solution and the corresponding objective function value; the optimal feasible solution includes the electric bus dispatching scheme and the charging facility sharing scheme. The specific process of step 2 is as follows: Step 2.1: The optimization objective Z is to minimize the daily charging cost Z1 of the electric bus route and the estimated revenue Z2 of the bus company from sharing charging stations with electric cars, as shown in formula (10): minZ=Z1-Z2(10) Step 2.2: Set the constraints of the optimization model, as shown in formulas (11)-(20): In the formula: x k,i To determine whether electric bus k operates on route i, if electric bus k operates on route i, then x k,i =1, otherwise x k,i =0; Let K be the operating state of electric bus K at time t. This indicates that bus k is running on the route at time t; otherwise... The charging state of electric bus k at time t. This indicates that bus k is charging at time t; otherwise... For train number i, the departure time is... T represents the departure time of train number i′; k,i Let k be the travel time of electric bus i on route i; Let ξ be the start time of the time window; e represents the end time of the time window ξ; k Let δ1 and δ2 be the lower and upper bounds of the allowable variation of the state of charge of the electric bus k, respectively, with the interval [δ1, δ2]. Let be the remaining battery power of electric bus k at time t; This represents the minimum charging time. The charging time of electric bus k during its charging journey r; The energy consumption of electric bus k for trip i; The remaining battery power of electric bus k at time 0; The remaining battery power of electric bus k after 1439 minutes; ε n,i Let ε represent the relationship between bus number i and electric bus route n. If bus number i belongs to electric bus route n, then ε n,i =1, otherwise ε n,i =0; ε′ n,k Let ε′ indicate whether electric bus k operates on route n. If electric bus k operates on route n, then let ε′ n,k =1, otherwise ε′ n,k =0; x k,i′ To determine whether electric bus k operates on route i′, if electric bus k operates on route i′, then x k,i′ =1, otherwise x k,i′ =0; The specific process of step 3 is as follows: Step 3.1: Develop an electric bus dispatching plan as the initial vehicle dispatching plan, and use the minimum number of remaining charging piles at each time window in the initial vehicle dispatching plan as the number of charging piles shared with electric cars to construct the initial charging facility sharing plan. The initial vehicle scheduling scheme and the initial charging facility sharing scheme are combined to form the initial feasible solution. The initial feasible solution serves as the root node in the branch and bound algorithm and is also the node to be branched at. Let the current optimal feasible solution be X. * The objective function value corresponding to the current optimal feasible solution is Z. * ; Step 3.2: Set the iteration count τ = 0, set the initial lower bound LB of the branch and bound algorithm to 0, and use the objective function value Z of the initial feasible solution. init UB serves as the initial upper bound; Step 3.3: Take the collaborative optimization model of vehicle scheduling scheme and charging facility sharing scheme as the original problem, and decompose the original problem into a restricted master problem; Step 3.4: Solve the restricted master problem, and construct the pricing subproblem based on the solution to the restricted master problem; Step 3.5: Solve the pricing subproblem and determine whether the test number of the optimal solution to the pricing subproblem is negative; If the test number of the optimal solution to the pricing subproblem is negative, add all vehicle scheduling schemes with negative test numbers from the process of solving the pricing subproblem to the restricted main problem, and return to step 3.4; If the test number of the optimal solution to the pricing subproblem is positive, then the test numbers of all vehicle scheduling schemes generated during the solution of the pricing subproblem are positive, and proceed to step 3.6; Step 3.6: Denote the optimal solution to the restricted master problem at the node where the τth iteration occurs as X. (τ) X (τ) The corresponding objective function value is denoted as Z. (τ) ; Step 3.7, Judgment: If Z (τ) <UB, and ψ k If it is not an integer, proceed to step 3.8; If Z (τ) <UB, and ψ k If it is 0 or 1, then update X. * =X (τ) UB=Z * =Z (τ) Trim the current node and proceed to step 3.10; If Z (τ) If ≥UB, prune the current node and proceed to step 3.10; ψ k =1 indicates the selection of electric bus k; ψ k =0 indicates that electric bus k is not selected; Step 3.8, Calculate η k.i =∑ k x k,i ψ k Select the η value closest to 0.5 k.i Find the η closest to 0.5 k.i The corresponding bus k and bus number i; where η k.i As an intermediate variable; Then, by adding constraints x to the pricing subproblem of the current node. k,i =1 to construct the left child node, by adding constraints x to the pricing subproblem of the current node. k,i = 0 to construct the right child node; let LB = Z for both the left and right child nodes. (τ) ; Step 3.9: Set the left child node as the new current node, add the right child node to the list of nodes to be processed, update the iteration count τ = τ + 1, and return to step 3.4; Step 3.10: Determine if the current node list still contains nodes to be processed. If the list of nodes to be processed is not empty, select the node with the smallest LB value in the list as the new current node, update the iteration count τ = τ + 1, and return to step 3.4; if the list of nodes to be processed is empty, proceed to step 3.

11. Step 3.11: Record the optimal feasible solution X * and the corresponding objective function value Z * ;Optimal feasible solution X * This includes electric bus dispatching schemes and charging facility sharing schemes; In step 3.3, the collaborative optimization model of vehicle scheduling scheme and charging facility sharing scheme is taken as the original problem, and the original problem is decomposed into a restricted master problem; The model for the restricted principal problem is as follows: in, Let $\frac{k}{k}$ be the charging cost of the electric bus $k$; $\frac{st}{k}$ be a constraint. In step 3.4, the restricted master problem is solved, and a pricing subproblem is constructed based on the solution; the specific process is as follows: Solve the restricted master problem to obtain the dual variable π of constraint (22). i The dual variable π of constraint (23) t ′; The dual variable π based on constraint (22) i The dual variable π of constraint (23) t Construct a pricing subproblem, the model of which is as follows:

2. The method for coordinated scheduling of electric buses under the time-sharing mode of charging facilities according to claim 1, characterized in that: The daily charging cost Z1 of the electric bus route is expressed as: In the formula: ε″ r,i,k =1 indicates that the charging trip r is the charging trip after the electric bus k completes its trip i; otherwise, ε″ r,i,k =0; r = 1, 2, ..., R, where R is the total number of charging trips; k = 1, 2, ..., K, where K is the total number of electric buses on N electric bus routes; i = 1, 2, ..., I, where I is the total number of daily operating trips on N electric bus routes, and N is the total number of electric bus routes served by a bus charging station. The charging cost for electric bus k during its charging journey r.

3. The method for coordinated scheduling of electric buses under the time-sharing mode of charging facilities according to claim 2, characterized in that: The estimated value Z2 of the revenue that the public transport company receives daily from the shared charging stations for electric vehicles is expressed as follows: In the formula: ξ represents the estimated revenue that public transport companies can obtain from sharing charging stations with electric vehicles within the time window ξ, where Λ is the maximum value of the time window number when the analysis period is divided into time window intervals of a certain length.

4. The method for coordinated scheduling of electric buses under the time-sharing mode of charging facilities according to claim 3, characterized in that: The process of obtaining the estimated value Z2 expression for the daily revenue that the public transport company receives from shared charging stations for electric vehicles is as follows: 1) Share Q charging stations within the time window ξ ξ The service intensity of a single charging station is denoted as ρ. ξ (Q ξ ), ρ ξ (Q ξ )=λ ξ / μ ξ Q ξ ;λ ξ ξ represents the average arrival rate of electric cars within the time window; μ ξ For charging service rate; Q ξ The number of charging piles at a charging station open to the public within each time window, 0 ≤ Q ξ ≤Q; Q is the number of charging piles configured in the charging station; Define blocking probability To calculate the probability that an electric car driver will be refused service due to a full charging station when there is no waiting queue in the service system, formula (3) is used. Alternatively, the blocking probability can be calculated using a recursive method. In the formula: Within the time window ξ, the number of charging piles opened at the charging station is Q. ξ The probability of blocking at that time; and ρ ξ (Q ξ -1) represents the number of charging piles that are open at the charging station within the time window ξ, which is Q. ξ Blocking probability and service strength at -1; Let ξ be the congestion probability when the number of open charging piles at a charging station is 0 within the time window ξ; ! represents the factorial symbol; j is the j-th charging pile, j = 0, 1, ..., Q ξ ; 2) Calculate the probability that the waiting time for an electric car arriving at the charging station is greater than 0. In the formula: A(,) is a user-defined operator; θ is the driver's impatience parameter; The actual waiting time for drivers of electric cars; In the formula: A(x,y) and γ(x,y) are both user-defined operators; 3) Calculate the probability that an electric car with a waiting time greater than 0 will forgo charging service. 4) Calculate the probability P of an electric vehicle abandoning the charging service based on 2) and 3). ξ {Ab}; 5) Calculate the estimated revenue that the public transport company will receive from sharing charging stations for electric vehicles within the time window ξ. 6) Calculate the estimated revenue Z2 that the public transport company will receive daily from sharing charging stations for electric vehicles.

5. The method for coordinated scheduling of electric buses under the time-sharing mode of charging facilities according to claim 4, characterized in that: In step 4), the probability P of an electric vehicle abandoning charging service is calculated based on steps 2) and 3). ξ {Ab}; is represented as:

6. The method for coordinated scheduling of electric buses under the time-sharing mode of charging facilities according to claim 5, characterized in that: The estimated value of the revenue obtained by the public transport company from the shared charging stations for electric vehicles within the time window ξ in step 5) is calculated in section 5). The expression is: In the formula: c′ ξ b1 represents the service fee charged by the public transport company within the time window ξ; b2 represents the charging power of the electric car. Let ξ be the duration of the time window; Let ξ be the expected value of the total charging amount of the electric cars arriving within the time window ξ; The maximum amount of electricity that a charging station can provide for electric vehicles within the time window ξ.

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