Electric bus scheduling method considering delay time cost under opportunity charging strategy

Abstract

This invention provides an electric bus scheduling method that considers delay time costs under an opportunistic charging strategy. The method includes: acquiring basic data related to electric bus scheduling; formulating an opportunistic charging strategy for electric buses based on the basic data; establishing an electric bus scheduling optimization model that considers delay time costs under the opportunistic charging strategy; and solving the electric bus scheduling optimization model using an algorithm to obtain the optimal scheduling scheme for electric buses. This invention helps bus companies formulate more realistic electric bus operation and scheduling plans, which is of great significance for improving the operational reliability and rational allocation of resources in the public transportation system. It also helps buses to recharge their batteries rationally and efficiently and improve vehicle utilization.

Description

Technical Field

[0001] This invention relates to the field of urban traffic optimization technology, and in particular to an electric bus scheduling method that considers the cost of delay time under an opportunity charging strategy. Background Technology

[0002] In recent years, urban traffic congestion, energy crises, and environmental pollution have become increasingly severe, making the principles of prioritizing public transportation and green development a consensus in the transportation industry. Electric buses, with their advantages of zero emissions, low noise, and high energy efficiency, are being vigorously promoted in the process of optimizing urban transportation structures. Compared to fuel-powered buses, electric buses face challenges such as shorter driving range and longer charging times due to limited battery capacity. This necessitates the development of reasonable charging strategies to ensure timely recharging during operation, which further complicates their scheduling.

[0003] With the development and popularization of smart charging pantograph technology, equipping bus routes with smart charging pantographs at their terminals and utilizing departure intervals for opportunistic charging has become feasible. By allowing vehicles to flexibly utilize opportunistic charging and return charging during daytime operation, not only can the driving range be increased and vehicle utilization improved, but deep battery discharge can also be reduced, extending battery life. However, smart charging pantographs are more expensive to purchase than traditional charging piles, and their reasonable allocation needs to be considered. Researching electric bus scheduling under the opportunistic charging strategy can collaboratively optimize the resource allocation of traditional charging piles at depots and smart charging pantographs at stations. Furthermore, the opportunistic charging mode helps improve vehicle utilization, thereby reducing fleet size and lowering costs for bus companies.

[0004] Furthermore, during operation, bus journey times are uncertain due to factors such as road traffic conditions, intersection signal timing schemes, and passenger boarding and alighting. This can lead to delays for some bus routes and even affect the smooth execution of scheduling plans. By incorporating the cost of delay time into the objective function of the scheduling optimization model and using the reserved travel time at a certain reliability level as input, it will help to develop more realistic scheduling plans and ensure their successful implementation.

[0005] Currently, there is no effective optimization method for electric bus scheduling that takes into account the introduction of opportunistic charging strategies and the cost of delay time. Summary of the Invention

[0006] The embodiments of the present invention provide an electric bus scheduling method that considers the cost of delay time under an opportunistic charging strategy, so as to effectively improve the operational reliability of the public transportation system.

[0007] To achieve the above objectives, the present invention adopts the following technical solution.

[0008] An electric bus scheduling method considering delay time costs under an opportunistic charging strategy includes: Obtain basic data related to electric bus dispatching; Based on the aforementioned fundamental data, an opportunity charging strategy for electric buses is developed. Establish an electric bus scheduling optimization model that considers delay time costs under the aforementioned opportunity charging strategy; The optimal scheduling scheme for electric buses is obtained by solving the electric bus scheduling optimization model using an algorithm.

[0009] Preferably, the basic data related to the electric bus dispatching includes: Bus route and depot data, including the length of each bus route within the area, the distance from each route's starting and ending points to each bus depot, the name of each route, the name of each starting and ending point, the name of each bus depot, and the information of each bus depot. Maximum capacity for parking buses Various bus stations Maximum number of standard charging stations that can be installed and each starting and ending station Maximum number of smart charging bows installed ; Bus vehicle parameters, including maximum passenger capacity Energy consumption rate Rated battery capacity and unit purchase cost ; Charging station parameters, including the charging power of the smart pantograph used for opportunistic charging. Unit purchase cost Charging power of ordinary charging piles in the station and unit purchase cost ; The bus timetable is generated by obtaining all scheduled bus trips from the operating timetables of each route and assigning them numbers. One departure time corresponds to one task train. For the total number of mission trains, each mission train... The departure time of the timetable is recorded as Each task train The length of the line from the originating station to the terminal station is denoted as . ; The mean and variance of the travel time for each bus route in both directions over the past 30 days are calculated, and then assigned to the task bus number according to the correspondence between the task bus number and the route. average travel time , mission train number Travel time variance ; Other relevant data, including industrial electricity prices. and passenger unit time value .

[0010] Preferably, the method further includes: Define the cost symbol for charging electric buses: definition Vehicle purchase cost, unit: yuan; definition Cost of purchasing charging infrastructure, unit: yuan; definition Cost of bus delay time, unit: yuan; Define the travel time symbol for electric buses: definition For the mission train number i Travel time, in minutes; definition For vehicles from train number i Destination to Train Number j Travel time from the starting point, in minutes; Define set notation: definition For vehicle assembly, ; definition For the station assembly, ; definition This is a collection of all bus routes' daily schedules.

[0011] definition This is the set of all starting and ending stations of all routes. ; definition For station A collection of ordinary charging stations, ; definition For the site A collection of smart charging bows, ; Define optimization variable symbols Define 0-1 variables If the vehicle If used, then ,otherwise ; Define 0-1 variables If the station charging stations If it has been used within one day, then ,otherwise ; Define 0-1 variables If the site Smart charging bow If it has been used within one day, then ,otherwise ; Define 0-1 variables If the station charging stations In the mission train Later If the time slot is occupied, then ,otherwise ; Define 0-1 variables If the site Smart charging bow In the mission train Later If the time slot is occupied, then ,otherwise ; Define 0-1 variables If the vehicle Completed the mission Execute the task after ,but ,otherwise ; Define 0-1 variables If the vehicle Completed the mission Then proceed to the station ,but ,otherwise ; Define 0-1 variables If the vehicle From the station After setting off, proceed to carry out the mission. ,but ,otherwise ; Define 0-1 variables If the vehicle From the station Departure, ,otherwise ; Define 0-1 variables If the vehicle Return to the station after completing all tasks. ,but ,otherwise .

[0012] Preferably, the opportunity charging strategy for the electric bus includes: From the start of operation, the bus departs from a bus depot with its battery fully charged, and the state of charge (SOC) of the battery is at the set upper limit. ,in λ 2 This represents the upper limit ratio coefficient of SOC.

[0013] Before each mission, the vehicle is idle from its arrival at the originating station until departure time, during which it uses the smart charging pantographs provided at the station for opportunistic charging. The specific process is as follows: (1) Calculate the time available for opportunity charging of the vehicle. On the train Originating Station Opportunity charging time is expressed as As shown in equation (1): (1) in, Indicates vehicle On the train The time during which the smart charging pantograph is not occupied during the waiting period at the starting station is determined by the method shown in equation (2), which sets the shortest opportunity charging time. , Indicates vehicle Charge to The required time is shown in equation (3). Indicates the train number of the arrival vehicle. Remaining battery power at the originating station; (2) (3) (2) If the vehicle is performing a task If an opportunity charge was performed previously, then when the vehicle begins performing a task... At that time, the battery level was restored to [value missing]. level, The calculation formula is shown in equation (4): (4) After a vehicle completes a mission, it is necessary to determine whether the remaining battery level after completing the next mission and returning to the nearest depot is greater than [a certain value]. ,in This is the set lower limit SOC ratio coefficient. If the above conditions are met, the vehicle continues to perform subsequent tasks; otherwise, the vehicle travels from the current task's endpoint to the nearest charging station to restore the battery level to the upper limit SOC. Then continue with the subsequent tasks; After completing all the daily tasks, the vehicles eventually return to their respective depots.

[0014] Preferably, the establishment of the electric bus scheduling optimization model considering delay time costs under the opportunity charging strategy includes: The actual travel time of a bus on a specific route Follow the mean The variance is Normal distribution, train number Reserved travel time The calculation is shown in formula (5): (5) The value of determines the reliability of the reserved travel time; An electric bus scheduling optimization model considering delay time costs under the opportunistic charging strategy is established. The objective function of this electric bus scheduling optimization model is as follows: (6) In the formula, Total cost, including vehicle purchase cost Cost of purchasing charging infrastructure and the time cost of bus departure delays , This includes the purchase cost of smart charging pantographs and the purchase cost of ordinary charging piles at the charging station. c v This indicates the purchase cost of a single electric bus. c p This indicates the purchase cost of a single smart charging bow. c c This represents the purchase cost of a single standard charging station. This is the daily depreciation rate for fixed assets.

[0015] Bus departure delay time cost The calculation process is as follows: Taking a time granularity of 1 minute and assuming that the fluctuation range of travel time for each bus route in all directions is 1 minute... ,Right now ,in , ,but The number of possible values ​​is ; Train number Subject to the preceding train number The departure delay time caused by the impact is Then the train number The actual departure times are shown in equation (7), and the train numbers are as follows. The actual end time is shown in equation (8).

[0016] (7) (8) In equation (7), For train number Departure times on the timetable ,and Among them, train numbers For train number The preceding train number, if the train number If it is the first mission performed by the vehicle on that day, or if it is a charging mission, then This assumes that the departure time of the first train service is guaranteed to be without delay and that the train's return for charging can interrupt the propagation of delays. Therefore, the train service... The minimum and maximum actual end times are shown in equations (9) and (10), respectively: (9) (10) like Train number The departure time was not affected by the preceding train. When affected by this, it will not cause delays, therefore: (11) like Train number If the departure time is delayed, the probability calculation method for each possible delay time is shown in equations (12) and (13): (12) (13) in, For train number The number of possible travel times for preceding trains assuming no delays. . For the preceding train number The delay probability matrix is ​​shown in equation (14), where the number of non-zero elements in each row of the matrix is ​​the number of trains. Number of possible values ​​for travel time The first row contains non-zero elements. The non-zero elements in the second row are from the 1st to the th. One, and so on. The matrix has rows. The number of rows and columns is A column, where each non-zero element is a train number. The probability of the corresponding delay time. For the preceding train number The travel time probability matrix is ​​shown in Equation (15). The distribution of the elements is the same as that of the delay probability matrix. The two are identical matrices, and each element is the probability of the corresponding travel time.

[0017] (14) (15) Therefore, train number The formula for calculating the expected delay time is shown in equation (16): (16) For vehicles For the train schedule to be executed within a day, the total expected delay can be expressed as: (17) in, For vehicles A collection of train schedules to be executed within a single day; Total delay time cost of all public transport vehicles in a day for: (18) The constraints of the electric bus dispatch optimization model include: (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) (29) (30) Equations (19) to (21) are train connection constraints. Equations (19) and (20) indicate that each task train can only be executed by one train, and each train can only be connected with one preceding train and one following train. Equation (21) indicates that a train departing from a certain station must return to that station after completing a series of tasks. Equations (22) and (23) are time constraints. Equation (22) indicates that for each train, the difference between the departure time of the latter train and the end time of the former train must not be less than the empty running time of the train between the two task origin and destination stations. Equation (23) indicates that the expected average delay time of each train should be less than the maximum allowable deviation. Equations (24) and (25) are power constraints. Equation (24) means that the remaining power of a vehicle after completing a trip must ensure that it can return to the nearest depot and that the remaining power after returning is not lower than the SOC lower limit. Equation (25) means that the remaining power of a vehicle after completing a task trip. Equations (26) to (27) are charging pile occupancy constraints. Equation (26) means that the smart charging bow can only be occupied by one vehicle at a certain time. Equation (27) means that the charging pile of the depot can only be occupied by one vehicle at a certain time. Equations (28) to (30) are capacity constraints. Equation (28) means that the number of vehicles allocated to each depot must meet the quantity limit. Equations (29) and (30) mean that the charging facilities of the starting station or depot must meet the quantity limit.

[0018] Preferably, the step of solving the electric bus scheduling optimization model using an algorithm to obtain the optimal scheduling scheme for electric buses includes: Step 3.1: Read the basic data related to electric bus dispatching; Step 3.2: Set the relevant parameters of the genetic algorithm as follows: population size is 100, retention rate of superior individuals in the selection operation is 0.2, crossover probability is 0.7, mutation probability is 0.1, and the maximum number of iterations of the genetic algorithm is [not specified]. ; Step 3.3: Generate the initial population The task number is used as a gene for encoding, 100 chromosomes are randomly generated, and the iteration number is set to iter=1; Step 3.4: Calculate the individual fitness value The chromosomes are decoded one by one. A greedy algorithm is introduced to transform the generation of scheduling schemes into a "binding" problem of task bus numbers. A satisfactory bus scheduling scheme under the task order of each chromosome bus number is obtained, and the fitness value of the individual is calculated, which is the objective function value. Step 3.5: Sort the population according to its fitness value. After obtaining the fitness values ​​of all individuals in the population, sort them in descending order of fitness values ​​to obtain the optimal fitness value, which is the smallest individual fitness value. Step 3.6: Determine if the termination condition is met. Check in turn whether one of the following conditions is met: (1) The number of iterations of the genetic algorithm, iter, reaches the set maximum value: ; (2) The change in the minimum fitness value obtained in two adjacent iterations is less than ,set up ,Right now:

[0019] in, This represents the minimum fitness value obtained in the iter-th iteration; (3) The minimum fitness value obtained during the iteration process did not change during 50 consecutive iterations; If any of the above conditions are met, proceed to step 3.11; Step 3.7, Select Operation Following the strategy of retaining elite individuals, 20% of the best individuals will be retained.

[0020] Step 3.8, Cross Operation

[0021] A crossover operator with partial mapping is used. Chromosomal segments of the same length are selected from two parent individuals and a mapping relationship is established. The two chromosome segments with the mapping relationship are interchanged to initially form offspring individuals. Then, the duplicated genes are processed according to the previously established mapping relationship to ensure that the offspring individuals are still feasible after crossover. The crossover probability in this operation is 70%. Step 3.9, Mutation Operation The reverse mutation operator is used to reverse the order of genes on a specific chromosomal segment, with a mutation probability of 10%.

[0022] Step 3.10, Population Update

[0023] After steps 3.7 to 3.9 are completed in sequence, the population is updated, and the process jumps to step 3.4 and updates the iteration count iter = iter + 1.

[0024] Step 3.11: Output the optimal scheduling scheme and the values ​​of each index in the current population; The optimal scheduling scheme includes the sequence of all bus trips, the required number of buses, the number of charging piles at the depot, the number of smart charging pantographs at each starting and ending point, the number of times vehicles return to the depot for charging, and the departure delay time.

[0025] Preferably, step 3.4, calculating the individual fitness value, includes: Step 3.4.1: Decode the chromosome and extract all task train numbers according to the mapping relationship; Step 3.4.2: Initialize bus vehicle number ; Step 3.4.3: Update vehicle number ; Step 3.4.4: According to the task train sequence after chromosome decoding, traverse all currently unexecuted task trains and determine whether to add the current task train to the current vehicle. If the train number chain is satisfied with the train number connection constraint and time constraint, i.e. formulas (19) to (23), if it is satisfied, it is added to the train number chain of the current vehicle; if it is not satisfied, it jumps to the next task train number to make the above judgment until the traversal ends. Steps 3, 4, and 5: Determine the current vehicle. Whether the train chain meets the power constraints, i.e. formulas (24) and (25), if it does, the vehicle is assigned to a station based on the principle of "the total travel distance between the outgoing train and the returning train is the shortest". If it does not meet the constraints, a returning charging train is inserted at the train where the power constraints are not met, and subsequent conflicting trains are eliminated according to the time constraints and included in the set of unexecuted task trains. Finally, the vehicle is assigned to a station. Step 3.4.6: Determine whether all current task trains have been executed. If all have been executed, proceed to step 3.4.7; otherwise, proceed to step 3.4.3. Step 3.4.7: Store the current scheduling scheme and output the individual fitness value, i.e. the objective function value.

[0026] As can be seen from the technical solutions provided by the embodiments of the present invention above, the present invention proposes an electric bus scheduling method that considers the cost of delay time under the opportunity charging strategy, which helps bus companies to formulate more realistic electric bus operation scheduling plans, and is of great significance to improving the operational reliability of the bus system and the rationality of the allocation of bus system resources.

[0027] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments 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 these drawings without creative effort.

[0029] Figure 1This is a flowchart of an electric bus scheduling method that considers delay time costs under an opportunistic charging strategy according to the present invention. Detailed Implementation

[0030] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0031] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0032] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0033] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0034] The processing flow of an electric bus scheduling method considering delay time costs under an opportunistic charging strategy provided by an embodiment of the present invention is as follows: Figure 1 As shown, the processing steps include the following: Step S1: Obtain basic data related to electric bus dispatching; Step S2: Define the relevant symbols for electric bus dispatch optimization; Step S3: Develop a charging strategy for electric buses based on the above basic data and related symbols; Step S4: Calculate travel time; Step S5: Establish an electric bus scheduling optimization model that considers delay time costs under the opportunistic charging strategy; Step S6: Solve the optimization model established in step S5 and output the optimal scheduling scheme.

[0035] The basic data related to electric bus dispatching in step S1 includes: (1) Bus route and station data, including the length of each bus route in the area, the distance of each route's starting and ending points from each bus station, the name of each route, the name of the starting and ending points, the name of each bus station, and the information of each bus station. Maximum capacity for parking buses (vehicles), various bus stations Maximum number of standard charging stations that can be installed (Taiwan), each starting and ending station Maximum number of smart charging bows installed (tower); (2) Relevant parameters of public transport vehicles, including maximum passenger capacity (Personnel), Energy Consumption Rate (kWh / km), rated battery capacity (kWh), unit purchase cost (vehicles / yuan); (3) Charging pile parameters, including the charging power of the smart charging pantograph used for opportunistic charging. (kW), Unit purchase cost (per unit / yuan), charging power of ordinary charging piles in the station (kW), Unit purchase cost (each / yuan); (4) Bus timetable: Obtain all the assigned bus routes from the operating timetables of each route and assign them numbers. One departure time corresponds to one task train. For the total number of mission trains, each mission train... The departure time of the timetable is recorded as Each task train The length of the line from the originating station to the terminal station is denoted as . (km); (5) The mean and variance of the travel time in both directions for each bus route over the past 30 days, and assign values ​​to the task bus numbers according to the correspondence between task bus numbers and routes. average travel time , mission train number Travel time variance ; (6) Other relevant data, including industrial electricity prices (Yuan / kWh), Passenger unit time value (yuan / min).

[0036] The specific process for defining the relevant symbols for electric bus dispatch optimization in step S2 is as follows: Step 2.1, Define cost symbols: definition Vehicle purchase cost, unit: yuan; definition Cost of purchasing charging infrastructure, unit: yuan; definition Cost of bus delay time, unit: yuan; Step 2.2, Define the travel time symbol: definition For the mission train number i Travel time, in minutes; definition For vehicles from train number i Destination to Train Number j Travel time from the starting point, in minutes; Step 2.3, Define set notation: definition For vehicle assembly, ; definition For the station assembly, ; definition This is a collection of all bus routes' daily schedules.

[0037] definition This is the set of all starting and ending stations of all routes. ; definition For station A collection of ordinary charging stations,

[0038] definition For the site A collection of smart charging bows, ; Step 2.4: Define optimization variable symbols Define 0-1 variables If the vehicle If used, then ,otherwise ; Define 0-1 variables If the station charging stations If it has been used within one day, then ,otherwise ; Define 0-1 variables If the site Smart charging bow If it has been used within one day, then ,otherwise ; Define 0-1 variables If the station charging stations In the mission train Later If the time slot is occupied, then ,otherwise ; Define 0-1 variables If the site Smart charging bow In the mission train Later If the time slot is occupied, then ,otherwise ; Define 0-1 variables If the vehicle Completed the mission Execute the task after ,but ,otherwise ; Define 0-1 variables If the vehicle Completed the mission Then proceed to the station ,but ,otherwise ; Define 0-1 variables If the vehicle From the station After setting off, proceed to carry out the mission. ,but ,otherwise ; Define 0-1 variables If the vehicle From the station Departure, ,otherwise ; Define 0-1 variables If the vehicle Return to the station after completing all tasks. ,but ,otherwise .

[0039] The specific process of formulating the charging strategy for electric buses based on the above-mentioned basic data and related symbols in step S3 is as follows: The buses depart from a bus depot at the start of operation, at which point the vehicles are fully charged. A fully charged state means that the battery's SOC (State of Charge) is at the set upper limit level. ,set up Afterwards, the vehicles will arrive at the departure station of their first task of the day according to the train number chain specified in the scheduling plan, and must arrive at the departure station before the departure time specified in the timetable.

[0040] Before each mission, the vehicle is idle from its arrival at the originating station until departure time. During this period, the vehicle can use the smart charging pantographs provided at the station for opportunistic charging, as detailed below: (1) Calculate the time available for opportunity charging of the vehicle. On the train Originating Station Opportunity charging time is expressed as As shown in equation (1): (1) in, Indicates vehicle On the train The time during which the smart charging pantograph is not occupied during the waiting period at the starting station is determined by the method shown in equation (2). The shortest opportunity charging time is set. , and when Greater than Charging can only be performed when the time is right; otherwise, it cannot be done. Indicates vehicle Charge to The required time is shown in equation (3). Indicates the train number of the arrival vehicle. The remaining power at the originating station, in kWh.

[0041] (2) (3) (2) If the vehicle is performing a task If an opportunity charge was performed previously, then when the vehicle begins performing a task... At that time, its power can be restored to level, The calculation formula is shown in equation (4): (4) After a vehicle completes a mission, it needs to be determined whether the remaining battery power after the vehicle's opportunity charging at the starting station of the next mission is sufficient to support it in completing the next mission and returning to the nearest depot. In other words, it needs to be determined whether the current battery level, after completing the next mission and returning to the nearest depot, will be greater than [a certain value]. ,in The set SOC (State of Charge) lower limit is 10%, which aims to prevent deep battery discharge and extend battery life. If the above conditions are met, the vehicle can continue to perform subsequent tasks; otherwise, the vehicle should travel from the current task's endpoint to the nearest charging station for charging. Return charging refers to returning to the charging station to use a regular charging station to restore the battery level to the upper SOC limit. Then, continue with the subsequent tasks.

[0042] After completing all the daily tasks, the vehicles eventually return to their respective depots.

[0043] The specific process for calculating the travel time in step S4 is as follows: The actual travel time of a bus on a specific route Follow the mean The variance is It follows a normal distribution. (Based on train number) Reserved travel time The scheduling method is generated as input for step S6. The calculation is shown in formula (5): (5) in, The value of this parameter determines the reliability of the reserved travel time. That is, the reliability of the travel time is 90%.

[0044] The objective function constructed in the electric bus scheduling optimization model considering delay time costs under the opportunity charging strategy in step S5 is as follows: (6) In the formula, Total cost, including vehicle purchase cost Cost of purchasing charging infrastructure and the time cost of bus departure delays , This includes the purchase cost of smart charging pantographs and the purchase cost of ordinary charging piles at the charging station. c v This indicates the purchase cost of a single electric bus. c p This indicates the purchase cost of a single smart charging bow. cc This represents the purchase cost of a single standard charging station. This is the daily depreciation rate for fixed assets.

[0045] The calculation process for the cost of delay time is as follows: Taking a time granularity of 1 minute and assuming that the fluctuation range of travel time in each direction for each route is... ,Right now ,in , ,but The number of possible values ​​is Train number Subject to the preceding train number The departure delay time caused by the impact is Then the train number The actual departure times are shown in equation (7), and the train numbers are as follows. The actual end time is shown in equation (8).

[0046] (7) (8) In equation (7), For train number Departure times on the timetable , Among them, train numbers For train number The preceding train number, if the train number If it is the first mission performed by the vehicle on that day, or if it is a charging mission, then This assumes that the departure time of the first train service operated by the vehicle is guaranteed to be without delay and that the vehicle's return for charging can interrupt the propagation of delays. The minimum and maximum actual end times are shown in equations (9) and (10), respectively: (9) (10) like Train number The departure time was not affected by the preceding train. When affected by this, there will be no delay, therefore: (11) like Train number If the departure time is delayed, the probability calculation method for each possible delay time is shown in equations (12) and (13): (12) (13) in, For train number The number of possible travel times for preceding trains assuming no delays. , For the preceding train number The delay probability matrix is ​​shown in equation (14), where the number of non-zero elements in each row of the matrix is ​​the number of trains. Number of possible values ​​for travel time The first row contains non-zero elements. The non-zero elements in the second row are from the 1st to the th. One, and so on, the number of rows in the matrix is... The number of rows and columns is A column, where each non-zero element is a train number. The probability of the corresponding delay time. For the preceding train number The travel time probability matrix is ​​shown in Equation (15). The distribution of the elements is the same as that of the delay probability matrix. The two are matrices of the same type, and each element is the probability of the corresponding travel time. (14) (15) Train number The formula for calculating the expected delay time is shown in equation (16): (16) For vehicles For the train schedule to be executed within a day, the total expected delay is expressed as: (17) in, For vehicles A collection of train schedules to be executed within a single day; Total delay time cost of all public transport vehicles in a day for: (18) The constraints of the electric bus dispatch optimization model include: (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) (29) (30) Equations (19) to (21) are train connection constraints. Equations (19) and (20) indicate that each task train can only be executed by one train, and each train can only be connected with one preceding train and one following train. Equation (21) indicates that a train departing from a certain depot must return to that depot after completing a series of tasks. Equations (22) and (23) are time constraints. Equation (22) indicates that for each train, the difference between the departure time of the subsequent train and the end time of the preceding train must not be less than the empty running time of the train between the two task origin and destination stations. Equation (23) indicates that the expected average delay time for each train must be less than the maximum allowable deviation. Equations (24) and (25) represent the power constraints. Equation (24) states that the remaining power of a vehicle after completing its trip must ensure that it can return to the nearest depot and that the remaining power after returning is not lower than the SOC lower limit. Equation (25) states the remaining power of a vehicle after completing its mission. Equations (26) to (27) represent the charging pile occupancy constraints. Equation (26) states that the smart charging pantograph can only be occupied by one vehicle at a given time. Equation (27) states that the charging pile at a depot can only be occupied by one vehicle at a given time. Equations (28) to (30) represent the capacity constraints. Equation (28) states that the number of vehicles allocated to each depot must meet the quantity limit. Equations (29) and (30) state that the number of charging facilities at the originating and terminal stations or depots must meet the quantity limit.

[0047] The specific process of solving the optimization model established in step S5 and outputting the optimal scheduling scheme in step S6 is as follows: Step 6.1: Basic Data Reading Read the basic parameters of the bus, including the purchase cost of the bus. Energy consumption rate of public transport vehicles Maximum passenger capacity of the vehicle Bus battery capacity Read relevant parameters of the charging facilities, including the purchase cost of the smart charging pantograph. and charging power The purchase cost of ordinary charging piles at the station and charging power Read the average travel time for each route in each direction. With variance Read the relevant parameters of the train numbers for each route and number all the train numbers. The information for each mission train includes the train number. Route name, timetable, and designated departure time. Task completion time calculated based on 90% reliability Origin station name, destination station name; read other basic parameters, including electricity price. Passenger unit time value .

[0048] Step 6.2: Input the relevant parameters for the genetic algorithm.

[0049] The relevant parameters for the genetic algorithm are set as follows: population size is 100, the retention rate of superior individuals in the selection operation is 0.2, the crossover probability is 0.7, the mutation probability is 0.1, and the maximum number of iterations of the genetic algorithm is [not specified]. .

[0050] Step 6.3: Generate the initial population

[0051] The task number is used as a gene for encoding, and 100 chromosomes are randomly generated, with the iteration number iter=1.

[0052] Step 6.4: Calculate the individual fitness value

[0053] The chromosomes are decoded one by one, and a greedy algorithm is introduced to transform the generation of scheduling schemes into a "binding" problem of task bus numbers. This yields a satisfactory bus scheduling scheme under the task ordering of each chromosome, and the fitness value of the individual is calculated, which is the objective function value.

[0054] Step 6.5: Sort the population according to its fitness value.

[0055] After obtaining the fitness values ​​of all individuals in the population, they are sorted from largest to smallest fitness value to obtain the optimal fitness value, which is the smallest individual fitness value.

[0056] Step 6.6: Determine if the termination condition is met.

[0057] Check in turn whether one of the following conditions is met: (1) The number of iterations of the genetic algorithm, iter, reaches the set maximum value: ; (2) The change in the minimum fitness value (objective function value) obtained in two adjacent iterations is less than ,set up ,Right now:

[0058] in, This represents the minimum fitness value obtained in the iter-th iteration.

[0059] (3) The minimum fitness value (objective function value) obtained during the iteration process did not change during 50 consecutive iterations.

[0060] If any of the above conditions are met, proceed to step 6.11.

[0061] Step 6.7: Select Operation

[0062] Following the strategy of retaining elite individuals, 20% of the best individuals will be retained.

[0063] Step 6.8, Cross Operation

[0064] A partially mapped crossover operator is used. Chromosomal segments of the same length are selected from two parent individuals and a mapping relationship is established. The two mapped chromosome segments are then interchanged to initially form offspring individuals. The duplicated genes are then processed according to the previously established mapping relationship to ensure that the offspring individuals remain viable after crossover. The crossover probability in this operation is 70%.

[0065] Step 6.9, Mutation Operation

[0066] The reverse mutation operator is used to reverse the order of genes on a specific chromosomal segment, with a mutation probability of 10%.

[0067] Step 6.10, Population Update

[0068] After steps 6.7 to 6.9 are completed in sequence, the population is updated, and the process jumps to step 6.4 and updates the iteration count iter = iter + 1.

[0069] Step 6.11: Output the optimal scheduling scheme and the values ​​of each indicator.

[0070] Output the optimal scheduling scheme for the current population, which is the bus number chain (sequence of task bus numbers) for all buses. Output the objective function value, required number of buses, number of charging piles at the depot, number of smart charging pantographs at each starting and ending point, number of times vehicles need to return to the depot for charging, and departure delay time for this optimal scheduling scheme.

[0071] The specific process for calculating the individual fitness value in step 6.4 is as follows: Step 6.4.1: Decode the chromosome and extract all task train numbers according to the mapping relationship; Step 6.4.2: Initialize bus vehicle number ; Step 6.4.3: Update vehicle number ; Step 6.4.4: According to the task train sequence after chromosome decoding, traverse all currently unexecuted task trains and determine whether to add the current task train to the current vehicle. If the train number chain is satisfied with the train number connection constraint and time constraint, i.e. formulas (19) to (23), if it is satisfied, it is added to the train number chain of the current vehicle; if it is not satisfied, it jumps to the next task train number to make the above judgment until the traversal ends. Step 6.4.5: Determine the current vehicle Whether the train chain meets the power constraints, i.e. formulas (24) and (25), if it does, the vehicle is assigned to a station based on the principle of "the total travel distance between the outgoing train and the returning train is the shortest". If it does not meet the constraints, a returning charging train is inserted at the train where the power constraints are not met, and subsequent conflicting trains are eliminated according to the time constraints and included in the set of unexecuted task trains. Finally, the vehicle is assigned to a station. Step 6.4.6: Determine whether all current task trains have been executed. If all have been executed, proceed to step 6.4.7; otherwise, proceed to step 6.4.3. Step 6.4.7: Store the current scheduling scheme and output the individual fitness value, i.e., the objective function value.

[0072] In summary, the electric bus scheduling method proposed in this invention, which considers delay time costs under an opportunistic charging strategy, can help bus companies develop economically reasonable regional electric bus scheduling plans and optimize the resource allocation of vehicles and charging facilities in the bus system. Specifically, step 3, formulating an electric bus charging strategy, helps buses to recharge rationally and efficiently and improve vehicle utilization. Step 4, calculating travel time, can take into account the uncertainty of bus travel time, improving the reliability of the scheduling plan. In conclusion, this method can intelligently adjust the tightness of bus connections in the scheduling plan, not only facilitating opportunistic charging and improving vehicle utilization, but also reducing departure delays caused by travel time uncertainty, thus improving the reliability of the bus system.

[0073] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0074] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0075] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0076] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for scheduling electric buses considering delay time costs under an opportunistic charging strategy, characterized in that, include: Obtain basic data related to electric bus dispatching; Based on the aforementioned fundamental data, an opportunity charging strategy for electric buses is developed. Establish an electric bus scheduling optimization model that considers delay time costs under the aforementioned opportunity charging strategy; The optimal scheduling scheme for electric buses is obtained by solving the electric bus scheduling optimization model using an algorithm. The basic data related to electric bus dispatching includes: Bus route and depot data, including the length of each bus route within the area, the distance from each route's starting and ending points to each bus depot, the name of each route, the name of each starting and ending point, the name of each bus depot, and the information of each bus depot. Maximum capacity for parking buses Various bus stations Maximum number of standard charging stations that can be installed and each starting and ending station Maximum number of smart charging bows installed ; Bus vehicle parameters, including maximum passenger capacity Energy consumption rate Rated battery capacity and unit purchase cost ; Charging station parameters, including the charging power of the smart pantograph used for opportunistic charging. Unit purchase cost Charging power of ordinary charging piles in the station and unit purchase cost ; The bus timetable is generated by obtaining all scheduled bus trips from the operating timetables of each route and assigning them numbers. One departure time corresponds to one task train. For the total number of mission trains, each mission train... The departure time of the timetable is recorded as Each task train The length of the line from the originating station to the terminal station is denoted as . ; The mean and variance of the travel time for each bus route in both directions over the past 30 days are calculated, and then assigned to the task bus number according to the correspondence between the task bus number and the route. average travel time , mission train number Travel time variance ; Other relevant data, including industrial electricity prices. and passenger unit time value ; The method further includes: Define the cost symbols related to electric buses: definition Vehicle purchase cost, unit: yuan; definition Cost of purchasing charging infrastructure, unit: yuan; definition Cost of bus delay time, unit: yuan; Define the travel time symbol for electric buses: definition For the mission train number i Travel time, in minutes; definition For vehicles from train number i Destination to Train Number j Travel time from the starting point, in minutes; define set notation: definition For vehicle assembly, ; definition For the station assembly, ; definition This is a collection of all bus routes' daily schedules. definition This is the set of all starting and ending stations of all routes. ; definition For station A collection of ordinary charging stations, ; definition For the site A collection of smart charging bows, ; Define optimization variable symbols Define 0-1 variables If the vehicle If used, then ,otherwise ; Define 0-1 variables If the station charging stations If it has been used within one day, then ,otherwise ; Define 0-1 variables If the site Smart charging bow If it has been used within one day, then ,otherwise ; Define 0-1 variables If the station charging stations In the mission train Later If the time slot is occupied, then ,otherwise ; Define 0-1 variables If the site Smart charging bow In the mission train Later If the time slot is occupied, then ,otherwise ; Define 0-1 variables If the vehicle Completed the mission Execute the task after ,but ,otherwise ; Define 0-1 variables If the vehicle Completed the mission Then proceed to the station ,but ,otherwise ; Define 0-1 variables If the vehicle From the station After setting off, proceed to carry out the mission. ,but ,otherwise ; Define 0-1 variables If the vehicle From the station Departure, ,otherwise ; Define 0-1 variables If the vehicle Return to the station after completing all tasks. ,but ,otherwise ; The establishment of the electric bus scheduling optimization model considering delay time costs under the opportunity charging strategy includes: The actual travel time of a bus on a specific route Follow the mean The variance is Normal distribution, train number Reserved travel time The calculation is shown in formula (1): (1) The value of determines the reliability of the reserved travel time; An electric bus scheduling optimization model considering delay time costs under the opportunistic charging strategy is established. The objective function of this electric bus scheduling optimization model is as follows: (2) In the formula, Total cost, including vehicle purchase cost Cost of purchasing charging infrastructure and the time cost of bus departure delays , This includes the purchase cost of smart charging pantographs and the purchase cost of ordinary charging piles at the charging station. c v This indicates the purchase cost of a single electric bus. c p This indicates the purchase cost of a single smart charging bow. c c This represents the purchase cost of a single standard charging station. This refers to the daily depreciation rate for fixed assets. Bus departure delay time cost The calculation process is as follows: Taking a time granularity of 1 minute and assuming that the fluctuation range of travel time for each bus route in all directions is 1 minute... ,Right now ,in , ,but The number of possible values ​​is ; Train number Subject to the preceding train number The departure delay time caused by the impact is Then the train number The actual departure times are shown in equation (3), and the train numbers are as follows. The actual end time is shown in equation (4); (3) (4) In equation (3), For train number Departure times on the timetable ,and Among them, train numbers For train number The preceding train number, if the train number If it is the first mission performed by the vehicle on that day, or if it is a charging mission, then This assumes that the departure time of the first train service is guaranteed to be without delay and that the train's return for charging can interrupt the propagation of delays. Therefore, the train service... The minimum and maximum actual end times are shown in equations (5) and (6), respectively: (5) (6) like Train number The departure time was not affected by the preceding train. When affected by this, it will not cause delays, therefore: (7) like Train number If the departure time is delayed, the probability calculation method for each possible delay time is shown in equations (8) and (9): (8) (9) in, For train number The number of possible travel times for preceding trains assuming no delays. , For the preceding train number The delay probability matrix is ​​shown in Equation (10), where the number of non-zero elements in each row of the matrix is ​​the number of trains. Number of possible values ​​for travel time The first row contains non-zero elements. The non-zero elements in the second row are from the 1st to the th. One, and so on, the number of rows in the matrix is... The number of rows and columns is A column, where each non-zero element is a train number. The probability of the corresponding delay time. For the preceding train number The travel time probability matrix is ​​shown in Equation (11). The distribution of the elements is the same as that of the delay probability matrix. The two are matrices of the same type, and each element is the probability of the corresponding travel time. (10) (11) Therefore, train number The formula for calculating the expected delay time is shown in equation (12): (12) For vehicles For a train schedule to be executed within a day, the total expected delay can be expressed as: (13) in, For vehicles A collection of train schedules to be executed within a single day; Total delay time cost of all public transport vehicles in a day for: (14) The constraints of the electric bus dispatch optimization model include: (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) Equations (15) to (17) are train connection constraints. Equations (15) and (16) indicate that each task train can only be executed by one train, and each train can only be connected with one preceding train and one following train. Equation (17) indicates that a train departing from a certain station must return to that station after completing a series of tasks. Equations (18) and (19) are time constraints. Equation (18) indicates that for each train, the difference between the departure time of the latter train and the end time of the former train must not be less than the empty running time of the train between the two task origin and destination stations. Equation (19) indicates that the expected average delay time of each train should be less than the maximum allowable deviation. Equations (20) and (21) are power constraints. Equation (20) means that the remaining power of a vehicle after completing a trip must ensure that it can return to the nearest depot and that the remaining power after returning is not lower than the SOC lower limit. Equation (21) means that the remaining power of a vehicle after completing a task trip. Equations (22) to (23) are charging pile occupancy constraints. Equation (22) means that the smart charging pantograph can only be occupied by one vehicle at a certain time. Equation (23) means that the charging pile of the depot can only be occupied by one vehicle at a certain time. Equations (24) to (26) are capacity constraints. Equation (24) means that the number of vehicles allocated to each depot must meet the quantity limit. Equations (25) and (26) mean that the charging facilities of the starting station or depot must meet the quantity limit.

2. The method according to claim 1, characterized in that, The aforementioned opportunity charging strategy for electric buses includes: From the start of operation, the bus departs from a bus depot with its battery fully charged, and the state of charge (SOC) of the battery is at the set upper limit. ,in λ 2 This represents the upper limit ratio coefficient of SOC; Before each mission, the vehicle is idle from its arrival at the originating station until departure time, during which it uses the smart charging pantographs provided at the station for opportunistic charging. The specific process is as follows: (1) Calculate the time available for opportunity charging of the vehicle. On the train Originating station Opportunity charging time is expressed as As shown in equation (27): (27) in, Indicates vehicle On the train The time during which the smart charging pantograph is not occupied during the waiting period at the starting station is determined by the method shown in equation (28), which sets the shortest opportunity charging time. , Indicates vehicle Charge to The required time is shown in equation (29). Indicates the train number of the arrival vehicle. Remaining battery power at the originating station; (28) (29) (2) If the vehicle is performing a task If an opportunity charge was performed previously, then when the vehicle begins performing a task... At that time, the battery level was restored to [value missing]. level, The calculation formula is shown in equation (30): (30) After a vehicle completes a mission, it is necessary to determine whether the remaining battery level after completing the next mission and returning to the nearest depot is greater than [a certain value]. ,in This is the set lower limit SOC ratio coefficient. If the above conditions are met, the vehicle continues to perform subsequent tasks; otherwise, the vehicle travels from the current task's endpoint to the nearest charging station to restore the battery level to the upper limit SOC. Then continue with the subsequent tasks; After completing all the daily tasks, the vehicles eventually return to their respective depots.

3. The method according to claim 1, characterized in that, The method of solving the electric bus scheduling optimization model using an algorithm to obtain the optimal scheduling scheme for electric buses includes: Step 3.1: Read the basic data related to electric bus dispatching; Step 3.2: Set the relevant parameters of the genetic algorithm as follows: population size is 100, retention rate of superior individuals in the selection operation is 0.2, crossover probability is 0.7, mutation probability is 0.1, and the maximum number of iterations of the genetic algorithm is [not specified]. ; Step 3.3: Generate the initial population The task number is used as a gene for encoding, 100 chromosomes are randomly generated, and the iteration number is set to iter=1; Step 3.4: Calculate the individual fitness value The chromosomes are decoded one by one. A greedy algorithm is introduced to transform the generation of scheduling schemes into a "binding" problem of task bus numbers. A satisfactory bus scheduling scheme under the task order of each chromosome bus number is obtained, and the fitness value of the individual is calculated, which is the objective function value. Step 3.5: Sort the population according to its fitness value. After obtaining the fitness values ​​of all individuals in the population, sort them in descending order of fitness values ​​to obtain the optimal fitness value, which is the smallest individual fitness value. Step 3.6: Determine if the termination condition is met. Check in turn whether one of the following conditions is met: (1) The number of iterations of the genetic algorithm, iter, reaches the set maximum value: ; (2) The change in the minimum fitness value obtained in two adjacent iterations is less than ,set up ,Right now: (31) in, This represents the minimum fitness value obtained in the iter-th iteration; (3) The minimum fitness value obtained during the iteration process did not change during 50 consecutive iterations; If any of the above conditions are met, proceed to step 6.11; Step 3.7, Select Operation Following the strategy of retaining elite individuals, 20% of the best individuals will be retained. Step 3.8, Cross Operation A crossover operator with partial mapping is used. Chromosomal segments of the same length are selected from two parent individuals and a mapping relationship is established. The two chromosome segments with the mapping relationship are interchanged to initially form offspring individuals. Then, the duplicated genes are processed according to the previously established mapping relationship to ensure that the offspring individuals are still feasible after crossover. The crossover probability in this operation is 70%. Step 3.9, Mutation Operation The reverse mutation operator is used to reverse the order of genes on a specific chromosomal segment, with a mutation probability of 10%. Step 3.10, Population Update After completing steps 3.7 to 3.9, update the population, jump to step 3.4, and update the iteration count iter = iter + 1. Step 3.11: Output the optimal scheduling scheme and the values ​​of each index in the current population; The optimal scheduling scheme includes the sequence of all bus trips, the required number of buses, the number of charging piles at the depot, the number of smart charging pantographs at each starting and ending point, the number of times vehicles return to the depot for charging, and the departure delay time.

4. The method according to claim 3, characterized in that, Step 3.4, calculating the individual fitness value, includes: Step 3.4.1: Decode the chromosome and extract all task train numbers according to the mapping relationship; Step 3.4.2: Initialize bus vehicle number ; Step 3.4.3: Update vehicle number ; Step 3.4.4: According to the task train sequence after chromosome decoding, traverse all currently unexecuted task trains and determine whether to add the current task train to the current vehicle. If the train number chain is satisfied with the train number connection constraint and time constraint, i.e. formulas (15) to (19), if it is satisfied, it is added to the train number chain of the current vehicle; if it is not satisfied, it jumps to the next task train number to make the above judgment until the traversal ends. Steps 3, 4, and 5: Determine the current vehicle. If the train chain meets the power constraints, i.e. formulas (20) and (21), if it does, the train is assigned to a station based on the principle of "the total travel distance between the outgoing train and the returning train is the shortest". If it does not meet the constraints, a returning train is inserted at the train where the power constraints are not met, and subsequent conflicting trains are eliminated according to the time constraints and included in the set of unexecuted task trains. Finally, the train is assigned to a station. Step 3.4.6: Determine whether all current task trains have been executed. If all have been executed, proceed to step 3.4.7; otherwise, proceed to step 3.4.

3. Step 3.4.7: Store the current scheduling scheme and output the individual fitness value, i.e. the objective function value.

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