A single-line modular bus vehicle scheduling method considering the influence of battery capacity and charging pile number in cooperation

Abstract

The application relates to a single-line modular public transport vehicle scheduling method considering the influences of battery capacity and charging pile quantity in cooperation, and relates to a single-line modular public transport vehicle scheduling method. The application aims to solve the problem that the scheduling methods of traditional fuel public transport vehicles and electric public transport vehicles are not applicable to modular public transport vehicle scheduling. The process is as follows: step one, collecting modular public transport line basic data; step two, defining decision variables based on step one; step three, calculating the state of charge of the modular public transport vehicle battery based on step two; step four, constructing a modular public transport vehicle scheduling and resource allocation comprehensive optimization model based on step three; and step five, solving the optimization model by using a hybrid intelligent algorithm to obtain an optimal modular public transport vehicle scheduling scheme and a charging scheduling scheme.

Description

Technical fields:

[0001] This invention belongs to the field of urban public transportation operation and management, specifically a modular bus dispatching method for a single route that considers the impact of battery capacity and the number of charging piles. Technical background:

[0002] Modular buses are a new type of urban public transportation vehicle, characterized by their small size, autonomous driving, and electric drive. Each modular bus serves as a basic unit and can operate independently on a route; bus companies can also combine or separate multiple basic units as needed. However, due to limitations in battery capacity, this type of vehicle has a limited driving range and requires charging during daytime operation.

[0003] When multiple modular buses operate as a convoy, the convoy is towed by the lead bus, so only the lead bus consumes energy, while the other buses do not. When the lead bus's battery is low, the convoy can continue operating by adjusting the order of the buses or replacing the lead bus. Therefore, modular bus vehicle scheduling not only requires allocating operating shifts on the timetable to the vehicles but also arranging the vehicle sequence when multiple modular buses are performing the same shift.

[0004] As described above, traditional scheduling methods for fuel-powered and electric buses are not suitable for modular buses. Firstly, modular buses have smaller battery capacities, requiring more frequent charging during the day. Secondly, multiple modular buses can form a fleet, and the energy consumption of each bus within the fleet varies significantly. How to schedule modular buses under the constraints of battery capacity and the number of charging stations is a crucial problem that urgently needs to be solved. To address these issues, this paper proposes a single-route modular bus scheduling method that collaboratively considers the impact of battery capacity and the number of charging stations. Summary of the Invention:

[0005] The purpose of this invention is to solve the problem that traditional scheduling methods for fuel-powered buses and electric buses are not applicable to the scheduling of modular buses, and to propose a single-route modular bus scheduling method that considers the impact of battery capacity and the number of charging stations.

[0006] The specific process of a modular bus dispatching method for a single route that collaboratively considers the impact of battery capacity and the number of charging stations is as follows:

[0007] Step 1: Modular bus route basic data collection;

[0008] Step Two: Define the decision variables based on Step One;

[0009] Step 3: Calculate the state of charge of the modular bus battery based on Step 2;

[0010] Step 4: Construct a modular integrated optimization model for bus vehicle scheduling and resource allocation based on Step 3;

[0011] Step 5: Use a hybrid intelligent algorithm to solve the optimization model and obtain the optimal modular bus dispatching scheme and charging dispatching scheme.

[0012] The beneficial results of this invention are:

[0013] The single-line modular bus dispatching method proposed in this invention takes into account the impact of battery capacity and the number of charging piles on the operation of modular buses. The generated route operation plan can reduce the charging cost of bus routes, reduce the number of charging piles, improve the operating efficiency of modular buses, and achieve the sustainable development of the public transportation system. Attached Figure Description

[0014] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0015] Specific Implementation Method 1: The specific process of this implementation method for single-line modular bus dispatching, which considers the combined impact of battery capacity and the number of charging stations, is as follows:

[0016] Step 1: Modular bus route basic data collection;

[0017] Step Two: Define the decision variables based on Step One;

[0018] Step 3: Calculate the state of charge of the modular bus battery based on Step 2;

[0019] Step 4: Construct a modular integrated optimization model for bus vehicle scheduling and resource allocation based on Step 3;

[0020] Step 5: Use a hybrid intelligent algorithm to solve the optimization model and obtain the optimal modular bus dispatching scheme and charging dispatching scheme.

[0021] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that the modular bus route basic data collection in step one is as follows:

[0022] 1.1. Define a modular vehicle's journey from the starting station to the terminal station and back to the starting station as one shift;

[0023] The total number of planned daily trips I is determined based on the bus route's departure timetable, and the trips are numbered from morning to evening according to their departure time, with i (i = 1, 2, ..., I) representing the trip number of the route;

[0024] 1.2. Define the number of modular buses equipped on the route as U, and use u (u=1,2,...,U) to represent the number of the modular buses.

[0025] The other steps and parameters are the same as in Specific Implementation Method 1.

[0026] Specific Implementation Method Three: This implementation method differs from Specific Implementation Method One or Two in that: in step two, the decision variable definition is based on step one; the specific process is as follows:

[0027] 2.1. Define the number of charging piles configured in the charging station as R, and use r (r=1,2,...,R) to represent the number of the charging piles;

[0028] 2.2. Define the battery capacity configured on each modular vehicle as B, in kWh;

[0029] 2.3. Define 0-1 variables and If modular vehicle u is on shift i and u is the lead vehicle, then If modular vehicle u performs shift i but u is not the lead vehicle, then otherwise,

[0030] 2.4. Define 0-1 variables If modular vehicle u completes shift i and then continues with shift j (j = 1, 2, ..., I), then the 0-1 variable... otherwise, If shift j is the first shift executed by modular vehicle u, then otherwise,

[0031] 2.5. Divide the entire day into K time periods with 1-minute intervals, and use k (k = 1, 2, ..., K) to represent the time period number;

[0032] 2.5.1 Define 0-1 variables If the modular vehicle u is charged after completing shift i, then otherwise,

[0033] 2.5.2 Defining 0-1 variables If modular vehicle u starts charging within time period k after completing shift i, then otherwise,

[0034] 2.5.3 Define 0-1 variables If modular vehicle u is charged during time period k after completing shift i, then otherwise,

[0035] 2.5.4 Define 0-1 variables If modular vehicle u finishes its charging within time period k after completing shift i, then otherwise,

[0036] Other steps and parameters are the same as in specific implementation method one or two.

[0037] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that: in step three, the state of charge of the modular bus battery is calculated based on step two; the specific process is as follows:

[0038] Modular vehicle u's battery state of charge at the start of shift j The calculation is divided into the following three cases:

[0039] Scenario 1: If shift j is the first shift performed by modular bus u, and bus u is the lead bus, i.e. and The calculation method is shown in equation (1):

[0040]

[0041] Where: SOC max This represents the upper limit of the battery's state of charge, %.

[0042] Scenario 2: If modular bus u completes its shift i and then goes on to shift j, and bus u was the lead bus when it was on shift i, i.e. and The calculation method is shown in equation (2):

[0043]

[0044] In the formula: W i B represents the power consumption of shift i, in kWh; B represents the battery capacity of the modular bus, in kWh. The state of charge of the battery for modular vehicle u at the start of shift i is %.

[0045] W i The calculation method is shown in equation (3):

[0046]

[0047] In the formula: L is the mileage of one trip, in km; M i The curb weight (kg) and weight (T) of the modular bus for route i are specified. iLet i be the travel time for train i, in min; Let be the average ambient temperature during the time period of shift i, in °C.

[0048] M i The calculation method is shown in equation (4):

[0049]

[0050] M B =1000(B / η) (5)

[0051] hour, It must be 0; hour, It can be 0 or 1;

[0052] In the formula: τ i M represents the average number of passengers on flight i, in people. pas Average passenger mass, kg; M bus The weight of a modular bus body, in kg; M B η is the weight of a battery, in kg; η is the battery energy density, in Wh / kg.

[0053] Scenario 3: If modular bus u completes its shift i and then moves on to shift j, but bus u was not the lead bus when it was on shift i, i.e. and hour, The calculation method is shown in equation (6):

[0054]

[0055] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0056] Specific Implementation Method Five: This implementation method differs from Specific Implementation Methods One through Four in that: in step four, a modular integrated optimization model for bus vehicle scheduling and resource allocation is constructed based on step three; the specific process is as follows:

[0057] 4.1. Construction of the objective function

[0058] The optimization objective of the model is to minimize the sum of the charging pile construction cost Z1, the modular bus battery purchase cost Z2, and the modular bus charging cost Z3. The objective function is calculated as follows:

[0059] min Z=Z1+Z2+Z3 (7)

[0060] 4.1.1. Calculation of charging pile construction cost Z1

[0061] The construction cost Z1 of charging piles is determined by the average daily purchase cost c of the charging piles. pile The amount (in yuan) is determined by the number of charging piles R deployed in the station, and the calculation method is shown in equation (8):

[0062] Z1 = c pile R (8)

[0063] 4.1.2. Calculation of Battery Purchase Cost Z2 for Modular Buses

[0064] The modular bus purchase cost Z2 is composed of the average daily battery purchase cost c. battery The value of the battery (B, kWh) on the modular bus and the number of modular buses (U) are jointly determined, and the calculation method is shown in equation (9):

[0065] Z2 = c battery UB (9)

[0066] 4.1.3. Calculation of Z3 Charging Cost for Modular Buses

[0067] The calculation method for the charging cost Z3 of modular buses is shown in Equation (10):

[0068]

[0069] In the formula: c k denoted as k, where k is the electricity price in yuan / kWh; P is the charging power of the charging pile in kW.

[0070] 4.2. Constraint Construction

[0071] (1) Service constraints

[0072]

[0073]

[0074] Where: n bus pax is the rated passenger capacity of a modular vehicle. Let pax be the number of passengers required to board train i.

[0075] (2) Time feasibility constraints

[0076]

[0077] In the formula: The end time of shift i; This is the start time of train number j.

[0078] (3) Power Constraint

[0079]

[0080] In the formula: The state of charge (SOC) of the battery for modular vehicle u at the start of shift i is %. min This represents the minimum state of charge of the battery, %.

[0081] (4) Charging service constraints

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] In the formula: t min Minimum charging time, min; SOC max This represents the upper limit of the battery's state of charge, %.

[0088] (5) Optimize variable value constraints

[0089]

[0090]

[0091]

[0092] B min ≤B≤B max (twenty three)

[0093]

[0094] In the formula: B min B represents the lower limit of battery capacity for modular buses, in kWh. max The upper limit for battery capacity in modular buses is kWh; Let be a 0-1 variable. If modular vehicle u executes shift j and u is the lead vehicle, then otherwise, For a 0-1 variable, if modular vehicle u executes shift j but u is not the lead vehicle, then otherwise, It is a set of positive integers.

[0095] The other steps and parameters are the same as those in specific implementation methods one through four.

[0096] Specific Implementation Method Six: This implementation method differs from Specific Implementation Methods One through Five in that: in step five, a hybrid intelligent algorithm is used to solve the optimization model to obtain the optimal modular bus dispatching scheme and charging dispatching scheme; the specific process is as follows:

[0097] This invention employs a hybrid intelligent algorithm to solve the model. First, an implicit enumeration method is used to provide feasible resource allocation schemes for the number of charging piles and the battery capacity of modular buses. Then, a hybrid intelligent algorithm combining the cloning algorithm and the particle swarm optimization algorithm is used to solve the corresponding modular bus scheduling and charging scheduling schemes for each resource allocation scheme, calculating the line operating cost under different numbers of charging piles and battery capacities. Finally, the algorithm that minimizes the cost of charging piles, battery capacity, modular bus scheduling, and charging scheduling is output.

[0098] 5.1. Determine the minimum feasible number of charging stations R using implicit enumeration. fea,min With the maximum value R fea,max Give the number R of each feasible charging pile. fea (R fea =R fea,min ,R fea,min +1,...,R fea,max -1,R fea,max ) value;

[0099] 5.2. Determine R using implicit enumeration method fea Minimum feasible battery capacity for modular buses With the maximum value Provide the battery capacity for each feasible modular bus.

[0100] 5.3. For each R fea and The resource allocation scheme under the combination uses a hybrid intelligent algorithm that combines the cloning algorithm and the particle swarm algorithm to solve the modular bus scheduling scheme and charging scheduling scheme;

[0101] 5.4. Compare the route operation costs (objective function values) of all feasible solutions; output the optimal solution, i.e., the solution with the minimum operation cost, including the number of charging piles (R). fea Modular bus battery capacity Modular bus dispatching scheme and charging scheduling scheme

[0102] The other steps and parameters are the same as those in one of the specific implementation methods one to five.

[0103] Specific Implementation Method Seven: This implementation method differs from Specific Implementation Methods One through Six in that: in section 5.3, for each R... fea and The resource allocation scheme under the combination utilizes a hybrid intelligent algorithm combining the cloning algorithm and the particle swarm optimization algorithm to solve the modular bus dispatching scheme and charging dispatching scheme; the process is as follows:

[0104] 5.3.1. Initialize basic algorithm parameters:

[0105] Let the number of iterations s = 0, and set the population size to E and the maximum number of iterations to S;

[0106] 5.3.2. Generating the initial population:

[0107] The population contains E particles (feasible solutions), which are randomly generated in the parameter space (initially the population is randomly generated); each particle e (e = 1, 2, ..., E) has a velocity V. e and position F e Two attributes;

[0108] Optimize variables Both are 0-1 matrices of I×U; therefore, the velocity space and position space of the particle swarm, which are both G=3I×U, are S×G matrices.

[0109] Where I is the total number of scheduled daily bus trips on the bus route; U is the number of modular buses on the bus route; G is the search space of the particle swarm optimization algorithm; and S is the maximum number of iterations of the algorithm.

[0110] variable It can be indirectly obtained through the above optimization variables, and its value can be discussed in the following five cases:

[0111] (1) After modular vehicle u serves as the lead vehicle for shift i, it continues to serve as the lead vehicle for shift j, i.e. at this time,

[0112] (2) After modular vehicle u serves as the lead vehicle for shift i, vehicle r2 does not serve as the lead vehicle for shift j. at this time,

[0113] (3) Modular vehicle u, after not serving as the lead vehicle for shift i, continues to serve as the lead vehicle for shift j, i.e. At this time,

[0114] (4) After modular vehicle u is no longer the lead vehicle for shift i, it will not continue to be the lead vehicle for shift j. hour,

[0115] (5) In other cases,

[0116] Therefore, not It is solved as a direct optimization variable.

[0117] 5.3.3. Calculate the affinity θ of particle e. e The calculation method is as follows:

[0118] θ e =1 / (Z) e,1 +Z e,2 +Z e,3 (25)

[0119] In the formula: Z e,1 Z e,2 Z e,3 The figures are the construction cost of the charging pile, the purchase cost of the modular bus battery, and the charging cost for particle e (feasible solution), respectively, in yuan;

[0120] 5.3.4. Record the best position F found so far for particle e (e = 1, 2, ..., E). e,best Record the optimal position F found so far for the entire particle swarm. best,all ;

[0121] The lower the affinity of the particles, the better their positions. The optimal position F for each particle... e,best And the optimal position F of the entire particle swarm best,all F may change after each iteration, therefore F needs to be updated after each iteration. e,best With F best,all .

[0122] 5.3.5. Determine if the number of iterations s is greater than the maximum number of iterations S; if yes, proceed to 5.3.11; otherwise, proceed to 5.3.6;

[0123] 5.3.6. Update the velocity and position of all particles according to equations (26) and (27), and limit the velocity of all particles to not exceed the boundary [-V max V max ];

[0124] v e,g (s+1)=ωv e,g (s)+c1r1(F e,best -f e,g(s)+c2r2(F best,all -f e,g (s)) (26)

[0125] f e,g (s+1)=f e,g (s)+v e,g (s+1) (27)

[0126] In the formula: v e,g (s+1) represents the velocity of particle e in the (s+1)th generation in the g-th dimension; f e,g (s+1) represents the position of the g-th dimension of particle e in the (s+1)-th generation; ω is the inertial weight; c1 and c2 are the acceleration constants; r1 and r2 are random numbers between [0,1]; V max V is the maximum value of the velocity boundary. max This represents the minimum value of the velocity boundary.

[0127] 5.3.7. Sort the affinity of each particle from high to low, and select the particles corresponding to the top τ affinities to write into set A. m The remaining E-τ particles are written into set A. r ;

[0128] 5.3.8. Cloning:

[0129] Set A m The affinity of each particle is sorted from high to low, and the top [particles] are selected. Cloning particles corresponding to each affinity;

[0130] The particles are sorted from highest to lowest affinity; the higher the affinity, the more particle clones there are.

[0131] 5.3.9. Mutation:

[0132] The cloned particles were also sorted from high to low affinity, and the sorted particles were then mutated.

[0133] The mutation rate decreases as affinity increases.

[0134] 5.3.10. Recalculate the affinity of each particle after mutation according to equation (25). Select τ particles with high affinity and write them into set A. m Return to version 5.3.3;

[0135] 5.3.11. Output R fea and The optimal modular bus scheduling scheme and charging scheduling scheme under resource allocation conditions, i.e., the optimal position F found by the entire particle swarm so far after the algorithm iterations. best,all The corresponding solutions include variables The values ​​of represent the modular bus dispatching scheme and variables The value of represents the charging scheduling scheme.

[0136] The other steps and parameters are the same as those in one of the specific implementation methods one to six.

[0137] 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 modular bus dispatching method for a single route that collaboratively considers the impact of battery capacity and the number of charging stations, characterized in that: The specific process of the method is as follows: Step 1: Modular bus route basic data collection; Step Two: Define the decision variables based on Step One; Step 3: Calculate the state of charge of the modular bus battery based on Step 2; Step 4: Construct a modular integrated optimization model for bus vehicle scheduling and resource allocation based on Step 3; Step 5: Use a hybrid intelligent algorithm to solve the optimization model and obtain the optimal modular bus dispatching scheme and charging dispatching scheme; In step four, a modular integrated optimization model for bus vehicle scheduling and resource allocation is constructed based on step three; the specific process is as follows: 4.

1. Construction of the objective function Based on the construction cost of charging piles Modular bus battery purchase cost and the charging cost of modular buses Minimizing the sum is the optimization objective of the model. The objective function is calculated as follows: (7) 4.1.

1. Construction cost of charging stations calculate Charging pile construction cost The average daily purchase cost of charging piles The number of charging piles deployed within the station The decision is made jointly, and the calculation method is shown in equation (8): (8) 4.1.

2. Battery Purchase Cost for Modular Buses calculate Modular bus purchase cost The average daily purchase cost of batteries The capacity of the batteries equipped on modular buses and modular bus number The decision is made jointly, and the calculation method is shown in equation (9): (9) 4.1.

3. Charging Costs of Modular Buses calculate Modular bus charging costs The calculation method is shown in equation (10): (10) In the formula: The electricity price for time period k is expressed in yuan / kWh. The charging power of the charging station, in kW; This represents the total number of scheduled daily trips. Indicates the task number for the line shift; The serial number indicating the modular bus; The total number of time periods divided into the entire day; Indicates the time period number; Let be a 0-1 variable. If the modular vehicle u charges within time period k after completing shift i, then... ; otherwise, ; 4.

2. Constraint Construction (1) Service constraints of shifts (11) (12) In the formula: pax is the rated passenger capacity of a modular vehicle. For train schedule The number of passengers demanding boarding, pax; For 0-1 variables, For 0-1 variables, if modular vehicles Shift schedule and As the lead car, then If modular vehicles Shift schedule but If it's not the lead car, then... ; otherwise, ; (2) Time feasibility constraints (13) In the formula: For train schedule The end time; For train schedule The beginning moment; For 0-1 variables, if modular vehicles Completed shift The schedule will continue to be operated. , Then the 0-1 variable ; otherwise, ; For 0-1 variables, if modular vehicles Completed shift If charging ends within time period k, then ; otherwise, ; For 0-1 variables, if modular vehicles Completed shift If charging begins within time period k, then... ;otherwise, ; (3) Power Constraint (14) In the formula: For modular vehicles In the schedule The battery state of charge at the start, % This represents the minimum state of charge of the battery, % For train schedule Power consumption, kWh; The capacity of the battery equipped on the modular bus, in kWh; (4) Charging service constraints (15) (16) (17) (18) (19) In the formula: The minimum charging time, min; This represents the upper limit of the battery's state of charge, % variable For 0-1 variables, if modular vehicles Completed shift After charging, ; otherwise, ; The number of charging piles to be configured in the charging station; (5) Optimize variable value constraints (20) (21) (22) (23) (24) In the formula: The lower limit for the battery capacity of modular buses is given in kWh. The upper limit for battery capacity in modular buses is given in kWh. For 0-1 variables, if modular vehicles Shift schedule and As the lead car, then ,otherwise, ; For 0-1 variables, if modular vehicles Shift schedule but If it's not the lead car, then... ; otherwise, ; It is a set of positive integers; and For 0-1 variables, if modular vehicles Shift schedule and As the lead car, then If modular vehicles Shift schedule but If it's not the lead car, then... ; otherwise, .

2. The method for scheduling modular buses on a single route, considering the combined effects of battery capacity and the number of charging stations, as described in claim 1, is characterized in that: The modular bus route basic data collection in step one is as follows: 1.

1. Define a modular vehicle's journey from the starting station to the terminal station and back to the starting station as one shift; The total number of planned daily bus trips (I) is determined based on the bus route's departure timetable, and these trips are numbered from morning to evening according to their departure time. Indicates the task number for the line shift; 1.

2. Define the number of modular buses equipped on the route as U, and use... This indicates the number of the modular bus.

3. The method for scheduling modular buses on a single route, considering the combined effects of battery capacity and the number of charging stations, as described in claim 2, is characterized in that: In step two, the decision variables are defined based on step one; the specific process is as follows: 2.

1. Define the number of charging piles configured within the charging station as follows: ,use Indicates the number of the charging station; 2.

2. Define the battery capacity configured on each modular vehicle as follows: The unit is kWh; 2.

3. Define 0-1 variables and If modular vehicles Shift schedule and As the lead car, then If modular vehicles Shift schedule but If it's not the lead car, then... ; otherwise, ; 2.

4. Define 0-1 variables If modular vehicles Completed shift The schedule will continue to be operated. Then the 0-1 variable ; otherwise, If the schedule It is a modular vehicle The first shift executed, ; otherwise, ; 2.

5. Divide the day into segments with 1-minute intervals. A time period, using Indicates the time period number; 2.5.1 Defining 0-1 variables If modular vehicles Completed shift After charging, ; otherwise, ; 2.5.2 Defining 0-1 variables If modular vehicles Completed shift Later in the period Once charging begins, ; otherwise, ; 2.5.3 Defining 0-1 variables If modular vehicles Completed shift Later in the period Internal charging, then ; otherwise, ; 2.5.4 Defining 0-1 variables If modular vehicles Completed shift Later in the period If charging ends within the specified time, then ; otherwise, .

4. A single-line modular bus dispatching method that collaboratively considers the impact of battery capacity and the number of charging piles, as described in claim 3, is characterized in that: In step three, the state of charge of the modular bus battery is calculated based on step two. The specific process is as follows: Modular vehicles In the schedule Battery state of charge at the start The calculation is divided into the following three cases: Scenario 1: If the shift Modular buses The first shift executed, and the vehicle It is the lead car, that is and , The calculation method is shown in equation (1): (1) In the formula: This represents the upper limit of the battery's state of charge, % Scenario 2: If modular buses Completed shift Go to carry out the shift And vehicles During the shift It was the lead car, that is... and , The calculation method is shown in equation (2): (2) In the formula: B represents the power consumption of shift i, in kWh; B represents the battery capacity of the modular bus, in kWh. For modular vehicles At the start of shift i, the battery state of charge is % The calculation method is shown in equation (3): (3) In the formula: L is the mileage of one trip, in km; The curb weight of the modular bus for shift i is kg; Let i be the travel time for train i, in min; The average ambient temperature during the time period of shift i, in °C; The calculation method is shown in equation (4): (4) (5) In the formula: Let i be the average number of passengers on flight i, in people. The average passenger weight is expressed in kg. The weight of a modular bus body, in kg; The weight of a battery is expressed in kg. Battery energy density, Wh / kg; Scenario 3: If modular bus u completes its shift i and then moves on to shift j, but bus u was not the lead bus when it was on shift i, i.e. and hour, The calculation method is shown in equation (6): (6)。 5. A single-line modular bus dispatching method that collaboratively considers the influence of battery capacity and the number of charging piles according to claim 4, characterized in that: In step five, a hybrid intelligent algorithm is used to solve the optimization model to obtain the optimal modular bus dispatching scheme and charging dispatching scheme; the specific process is as follows: 5.

1. Determine the minimum feasible number of charging stations using implicit enumeration. With the maximum value Give the number of each feasible charging station. Values; 5.

2. Determined by implicit enumeration method Minimum feasible battery capacity for modular buses With the maximum value Provide the battery capacity for each feasible modular bus. ; 5.

3. For each and The resource allocation scheme under the combination uses a hybrid intelligent algorithm that combines the cloning algorithm and the particle swarm algorithm to solve the modular bus scheduling scheme and charging scheduling scheme; 5.

4. Compare the route operation costs of all feasible options; output the optimal option, i.e. the option with the lowest operating cost, including the number of charging piles, the battery capacity of modular buses, the modular bus dispatching scheme, and the charging dispatching scheme.

6. A single-line modular bus dispatching method that collaboratively considers the impact of battery capacity and the number of charging piles, as described in claim 5, is characterized in that: For each of the terms in 5.3 and The resource allocation scheme under the combination utilizes a hybrid intelligent algorithm combining the cloning algorithm and the particle swarm optimization algorithm to solve the modular bus dispatching scheme and charging dispatching scheme; the process is as follows: 5.3.

1. Initialize basic algorithm parameters: Let the number of iterations Set the population size to The maximum number of iterations is ; 5.3.

2. Generating the initial population: The population contains E particles, which are randomly generated in the parameter space; each particle Speed and location Two attributes; Optimize variables , , All A 0-1 matrix; therefore, the search space Both the velocity space and position space of the particle swarm are Matrix; in, This represents the total number of scheduled bus trips per day for a given bus route. The number of modular buses deployed on bus routes; This represents the search space for the particle swarm optimization algorithm. This represents the maximum number of iterations for the algorithm. variable It can be indirectly obtained through the above optimization variables, and its value is discussed in the following five cases: (1) After modular vehicle u serves as the lead vehicle for shift i, it continues to serve as the lead vehicle for shift j, i.e. , ,at this time, ; (2) After modular vehicle u serves as the lead vehicle for shift i, it does not continue to serve as the lead vehicle for shift j, i.e. , ,at this time, ; (3) Modular vehicle u does not serve as the lead vehicle for shift i, but continues to serve as the lead vehicle for shift j, i.e. , At this time, ; (4) After modular vehicle u is no longer the lead vehicle for shift i, it will not continue to be the lead vehicle for shift j. , hour, ; (5) In other cases, ; Therefore, not It is used as a direct optimization variable for solving; 5.3.

3. Calculate the affinity of particle e. The calculation method is as follows: In the formula: , , The figures are the construction cost of the charging pile, the purchase cost of the modular bus battery, and the charging cost for particle e (feasible solution), respectively, in yuan; 5.3.

4. Recording Particles The best location found so far Record the best position found so far for the entire particle swarm. ; 5.3.

5. Determine if the number of iterations s is greater than the maximum number of iterations S; if yes, proceed to 5.3.11; otherwise, proceed to 5.3.6; 5.3.

6. Update the velocity and position of all particles according to equations (26) and (27), and restrict the velocity of all particles to not exceed the boundary. ; In the formula: This represents the velocity of the g-th dimension of particle e in the (s+1)-th generation; This indicates the position of the g-th dimension of particle e in the (s+1)-th generation; Inertial weights; , Here is the acceleration constant; , for Random numbers between; The maximum value of the velocity boundary. This represents the minimum value of the velocity boundary. 5.3.

7. Sort the affinity of each particle from high to low, and select the top... Each affinity corresponds to a particle written into the set. The rest Each particle is written into the set. ; 5.3.

8. Cloning: set The affinity of each particle is sorted from high to low, and the top [particles] are selected. Cloning particles corresponding to each affinity; 5.3.

9. Variation: The cloned particles were also sorted from high to low affinity, and the sorted particles were then mutated. 5.3.

10. According to the formula Recalculate the affinity of each particle after mutation; select Write particles with high affinity into the set. Return to version 5.3.3; 5.3.

11. Output and The optimal modular bus scheduling and charging scheduling schemes under resource allocation conditions represent the optimal positions found by the entire particle swarm so far after the algorithm iterations. The corresponding solutions include variables , The values ​​represent the modular bus dispatching scheme and variables. The value of represents the charging scheduling scheme.

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