Electric public transportation system dynamic operation adjusting method based on rapid charging facility

Abstract

The invention provides a dynamic operation adjusting method for an electric public transportation system based on a quick charging facility so as to cope with the real-time change of an operation environment. And reconstructing the dynamic adjustment problem of the electric public transportation system into a mixed integer nonlinear programming model. Meanwhile, factors such as bus operation dynamics, dynamic passenger loading, vehicle overtaking, capacity limitation and charging and discharging processes are comprehensively considered. Constructing a prediction time domain and a control time domain of the current decision-making stage based on a dynamic adjustment model of rolling optimization; constructing an electric bus dynamic adjustment optimization model; designing a spatial branch and bound method combined with an acceleration technology to solve the decision at the current stage; through the strategy, the scheduling and charging arrangement of the buses can be optimized, the overall operation efficiency and service quality of the system are remarkably improved, energy consumption and operation cost are reduced, and therefore wide application and sustainable development of the electric public transportation system are promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of road transport organization, and in particular relates to a dynamic operation adjustment method of an electric bus system based on a fast charging facility. Background Art

[0002] As the electrification of bus fleets continues to increase, fast charging technology is seen as an emerging solution that can effectively address the high cost of large on-board batteries. Compared with traditional diesel buses, electric buses, as an environmentally friendly means of transportation, show significant environmental and economic advantages. Despite their many advantages, electric buses have limited driving range and long charging times. The emergence of fast charging technology provides greater potential for the development of electric bus systems. Based on the concept of opportunity charging, this technology allows electric buses to easily charge their batteries through chargers along the route during operation, thereby reducing the number of batteries required and reducing battery costs, which has a clear cost advantage over buses that rely on slow charging at night.

[0003] In an uncontrolled electric bus system, due to the inherent uncertainty of road traffic conditions, the daily operation of buses is often disturbed by many factors, such as bus delays, disruptions to scheduled charging plans, etc. These factors inevitably lead to a significant decline in the reliability and service quality of the system. Therefore, in a disturbed operating environment, bus operators need to respond quickly and provide effective adjustment strategies to ensure high operating performance and service quality while maintaining low energy consumption, which is of great significance to the overall operational efficiency and sustainable development of the electric bus system. Summary of the invention

[0004] In view of this, the present invention proposes a dynamic operation adjustment method for an electric bus system based on fast charging facilities to cope with real-time changes in the operating environment. At the same time, factors such as bus operation dynamics, dynamic passenger loading, vehicle overtaking, capacity limitations, and charging and discharging processes are comprehensively considered. Through this strategy, the scheduling and charging arrangements of buses can be optimized, the overall operating efficiency and service quality of the system can be significantly improved, energy consumption and operating costs can be reduced, and the widespread application and sustainable development of electric bus systems can be promoted.

[0005] In order to achieve the above objectives, the technical solution adopted by the present invention reconstructs the dynamic adjustment problem of the electric bus system concerned into a mixed integer nonlinear programming model.

[0006] In a first aspect, a method for dynamic operation adjustment of an electric bus system based on a fast charging facility comprises the following steps: Obtain bus line operation data; including the number of bus line stations, station spacing, charging facility locations, charging power, bus battery capacity and other information.

[0007] Based on the dynamic adjustment model of rolling optimization and the rolling time domain framework, the prediction time domain and control time domain of the current decision-making stage are constructed to monitor and collect real-time information on road environment, public transportation vehicles and passenger demand as input; including: environmental disturbance information, actual arrival and departure time, number of bus passengers, and passenger arrival rate.

[0008] Construct bus operation, passenger loading, and power consumption constraints; Taking bus schedule deviation, passenger waiting time, power consumption and adjustment measure cost minimization as the objective function, a dynamic adjustment optimization model for electric buses is constructed. The spatial branch-and-bound method combined with acceleration technology is used to solve the current stage decision until a feasible and high-quality adjustment optimization strategy is obtained; The electric bus adjustment and charging strategies in the control time domain of the current decision stage are fixed, and then the prediction time domain and control time domain of the next decision stage are constructed and solved until the end of the operation.

[0009] Specifically, the bus operation constraints include: bus arrival and departure time, stop time, inter-stop operation time and overtaking constraints; The passenger loading constraints include: waiting passengers, boarding passengers, getting off passengers, onboard passengers and stranded passengers constraints; The power consumption constraint conditions include: fast charging constraint and battery power constraint.

[0010] Specifically, the acceleration technology includes: boundary shrinkage and bilinear specific branching.

[0011] Specifically, the dynamic adjustment model includes: Modeling the topology of electric bus routes: Constructing a set of nodes of the electric bus system, including the start and end station nodes, intermediate station nodes, and station nodes equipped with fast charging infrastructure; Based on the node set and the bus running direction, the bus running arc is constructed: the electric bus starts from the starting station node, arrives and stays at the intermediate station node to pick up and drop off passengers, and is charged at the station node equipped with fast charging infrastructure. Each running arc corresponds to a different discharge process; Based on the given electric bus line topology, the complete electric bus operation adjustment time domain is divided into multiple decision-making stages, and a rolling time domain decision-making framework is constructed; each decision stage involves the prediction time domain and the control time domain; according to the bus operation, passenger demand and interference information, the bus adjustment strategy for a longer period of time in the future, namely the prediction time domain, is formulated and solved; the adjustment strategy for a shorter period of time in the future, namely the control time domain, is sent to the bus for execution.

[0012] Specifically, the bus operation constraints include: Electric bus arrival time constraint: The arrival time of an electric bus at each station is the sum of the departure time of the electric bus at the previous station and the running time between the two stations, as shown in formula (1): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The actual arrival time of Represents electric buses At the bus stop The actual departure time of Represents electric buses At the bus stop and Station The actual inter-station running time between Electric bus operation time constraint: The operation time of an electric bus between stations is the sum of the planned operation time between stations, the operation adjustment time and the interference time, as shown in formula (2): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At bus stops and stations The actual running time between Represents electric buses At the bus stop and Station The planned running time between Represents electric buses At the bus stop and Station The running adjustment time between parameters Represents electric buses At the bus stop and Station Interference time between operations; Electric bus stay time constraint: The stay time of an electric bus at a station is the sum of the door opening and closing time of the bus at the station, the time for passengers to get on and off the bus, and the interference time, as shown in formula (3): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The actual residence time of Represents the bus door opening and closing time; parameter Represents the average time for each passenger to get on and off the bus; variable Represents electric buses Arrive at the bus station The number of passengers getting off at time; variable Represents electric buses Arrive at the bus station The number of passengers boarding at time ; parameter Represents electric buses At the bus stop Disruption time during stops; Departure time constraint of electric buses: The departure time of electric buses at each station is the sum of the arrival time, stay time and adjustment time at the station, as shown in formula (4): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The actual departure time of Represents electric buses At the bus stop The actual arrival time of Represents electric buses At the bus stop The actual residence time of the variable Represents electric buses At the bus stop The time taken for adjustments; Electric bus departure interval constraint: The departure interval of an electric bus at each station is the difference between the departure time of the bus at the station and the departure time of the bus immediately preceding it, as shown in formula (5): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The interval between the vehicles in front; variable Represents electric buses At the bus stop The actual departure time of Represents electric buses The car in front of you is at the bus stop The departure time of the bus is due to the possibility of overtaking. Not necessarily equal to ; Electric bus overtaking constraint: By introducing binary variables to represent the departure order of buses, it is possible to determine whether the electric bus overtakes, as shown in formulas (6)-(11): ; ; ; ; ; ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The actual departure time of Represents electric buses The car in front of you is at the bus stop departure time; if at the bus station Electric Bus The departure time is later than the bus , then the variable ,otherwise, If the bus It's a bus exist If the vehicle in front of the vehicle is standing next to it, then the variable ,otherwise, ;parameter is a large positive number; Electric bus operation constraints: The adjustment time of electric buses staying at stations and running between stations must be between the predetermined maximum and minimum limits, and the departure interval should be greater than the predetermined minimum limit; as shown in formulas (12)-(14): ; ; ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop Adjustment time taken; variable Represents electric buses At the bus stop and Station The running adjustment time between parameters Represents an electric bus at the station The maximum adjustment time that can be taken during the stop; parameter and Represents the electric bus and Station The minimum and maximum adjustment times that can be taken when running between Represents electric buses At the bus stop The actual departure time of Representatives immediately followed in the electric bus The bus ahead is at the bus stop Departure time; parameters Representatives at the station Minimum departure intervals between electric buses.

[0013] The passenger loading constraints include: Waiting passenger constraint: The number of passengers waiting at the bus station includes newly arrived passengers and stranded passengers from the previous bus, as shown in formula (15): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Representative at the bus stop Waiting for the electric bus The number of waiting passengers; parameter Representing passengers at bus stops Waiting for the electric bus The arrival rate of Represents electric buses With the bus At the bus stop The departure interval of Represents the bus station Electric bus The number of passengers stranded in the vehicle immediately preceding it; Electric bus stranded passenger constraint: The number of passengers stranded by an electric bus is equal to the number of waiting passengers minus the number of boarding passengers, as shown in formula (16): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents the bus station Electric bus Number of stranded passengers; variable Representative at the bus stop Waiting for the electric bus The number of waiting passengers; variable Representative at the bus stop Get on an electric bus Number of passengers; The constraint on passengers stranded in the preceding vehicle immediately adjacent to the electric bus is shown in formula (17): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents the bus station Electric bus The number of passengers left behind by the bus immediately preceding it; if the bus It's a bus exist If the vehicle in front of the vehicle is standing next to it, then the variable ,otherwise, ;variable Represents the bus station Electric bus Number of stranded passengers; Boarding passenger constraint: The number of boarding passengers involves the smaller of the number of waiting passengers and the remaining capacity of the bus, as shown in formula (18): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Representative at the bus stop Boarding an electric bus Number of passengers; variable Representative at the bus stop Waiting for the electric bus The number of waiting passengers; parameter Represents electric buses Capacity; variable Represents electric buses Arrive at the bus station The number of passengers getting off at time; variable Represents electric buses At the bus stop The number of passengers on board the vehicle at the station; The passenger constraint: The number of passengers on the electric bus involves the number of passengers on the previous station plus the number of passengers on the bus minus the number of passengers on the bus. As shown in formula (19): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop Number of passengers on board; variable Represents electric buses At the bus stop The number of passengers boarding the bus; variable Representative electric bus at the bus stop The number of passengers getting off the bus.

[0014] Passenger alighting constraint: The number of passengers alighting at a bus stop is linearly related to the number of passengers on board at the previous stop and the alighting rate, as shown in formula (20): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Representative electric bus at the bus stop The number of passengers getting off the bus; variable Represents electric buses At the bus stop The number of passengers on board at the station; parameter Represents electric buses At the bus stop The percentage of passengers getting off the bus.

[0015] The power consumption constraint conditions include: Energy consumption constraints of electric buses: The energy consumption of electric buses is related to the changes in air resistance, gravitational potential energy, rolling resistance power consumption, and auxiliary equipment power consumption, as shown in formula (21): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop Energy consumption during parking and interval operation; parameters Representative bus station and The distance between stands for efficiency coefficient; parameter Represents air density; parameter Represents the air resistance coefficient; parameter Represents the front area of ​​the electric bus; parameter Represents the friction coefficient of the electric bus; parameter Represents the slope of the electric bus; parameter and They represent the body weight of the electric bus when unloaded and the average weight of passengers; Represents the acceleration due to gravity; parameter and Represent the power of the auxiliary load and auxiliary system of the electric bus respectively; Represents electric buses At the bus stop and Station The actual running time between Represents electric buses At the bus stop The number of passengers on board; parameter Represents electric buses At the bus stop Interruption time during stops; variable Represents electric buses At the bus stop The actual residence time of the variable Represents electric buses At the bus stop The time taken for adjustments; From formula (21), it can be seen that the energy consumption of electric buses has a nonlinear relationship with the running speed. The energy consumption of electric buses is approximated by the piecewise linearization method, as shown in formulas (22)-(23): ; ; in: represent Approximate function of ; Represents the division interval Breakpoints Representative connection and The slope of the straight line segment; Linearize formula (23) into formula (24)-(26): ; ; ; Among them: variable Electric bus At the bus stop With the station If the running time between Greater than ,but ;otherwise, ; Constraint on the charging capacity of electric buses: The charging capacity of electric buses should be the minimum value of the remaining capacity of the battery and the capacity charged at the station, as shown in formula (27): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station A collection of public transport vehicles; a collection represents the set of bus stops with fast charging facilities; the variable Represents electric buses At the station Charging capacity; parameters Represents electric buses At the bus stop Charging power; parameters Represents the time when the charger is connected and disconnected; parameter Represents the battery power of the electric bus at the first stop; variable Represents electric buses At the bus stop The actual residence time of the variable Represents electric buses With the bus At the bus stop The departure interval of Represents electric buses At the bus stop The amount of electricity; Electric bus power constraint: The current power of the electric bus is calculated as shown in formula (28): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses Among all stations with fast charging facilities The power of the next station; variable Represents electric buses At the bus stop The amount of electricity; variable Represents electric buses At the station Energy consumption of station parking and interval operation; variable Represents electric buses At the station The charge level at The power constraint of electric buses at each station: The power of electric buses at each station should be greater than the minimum power, as shown in formula (29): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The power of Representative electric bus at the bus stop Minimum power.

[0016] The objective function includes: The penalty for electric bus departure time deviation is shown in formula (30): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; parameters Represents the weight coefficient of the departure time deviation of electric buses; variable Represents electric buses At the bus stop The actual departure time; parameters Represents electric buses At the bus stop The planned departure time; The penalty for deviation of the departure interval of electric buses is shown in formula (31): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; parameters Represents the weight coefficient of the electric bus departure interval deviation; variable Represents electric buses At the bus stop The actual departure time of Represents electric buses The car in front of you is at the bus stop Departure time; parameters Represents electric buses At the bus stop The interval between departures of the previous bus; The passenger waiting time penalty for electric buses is shown in formula (32): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; parameters The weight coefficient representing the waiting time of passengers at the station; parameter Representing passengers at bus stops Waiting for the electric bus The arrival rate of Represents electric buses With the bus At the bus stop The departure interval of Represents the next step in electric buses The bus ahead is at the bus stop Number of stranded passengers; The energy consumption penalty for electric buses is shown in formula (33): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; parameters Represents the weight coefficient of the energy consumption of electric buses; variable Represents electric buses At the bus stop Energy consumption during parking and interval operation.

[0017] The adjustment cost penalty for electric buses is shown in formula (34): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; parameters Represents the weight coefficient of the adjustment measures taken on the operation of electric buses; variable Represents electric buses At the bus stop Adjustment time taken; variable Represents electric buses At the bus stop and Station The running adjustment time between.

[0018] The spatial branch and bound method comprises: Step 1: The dynamic adjustment optimization mathematical model of the electric bus system with fast charging infrastructure is abbreviated as model M1. Formulas (17), (18), and (27) are linearized, and the mixed integer nonlinear programming model (MNILP) M1 can be reconstructed into a mixed integer bilinear programming model (MIBLP) M2, which is solved using the spatial branch and bound algorithm.

[0019] Step 2: For formula (32), use McCormick’s inequality to solve the bilinear term The original MIBLP model is relaxed into a mixed integer convex quadratic programming (MIQP) model, and the solution obtained is the lower bound solution of the original problem.

[0020] The McCormick inequality bilinear relaxation is to convert the bilinear term in the objective function into a linear form through a continuous variable, as shown in formula (35): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the Decision stage, electric bus At the station A collection of variables Representative variables and Approximate value of the product of ; variable Represents the bus station Electric bus The number of passengers left behind by the vehicle immediately preceding it; parameter and Represented The lower and upper bounds of ; variables Represents electric buses With the bus At the bus stop The departure interval of and Represented The lower and upper bounds of . By bilinear relaxation of McCormick inequality, the original problem is transformed into a relaxed problem, and the solution obtained is the lower bound solution of the original problem.

[0021] Step 3: According to the bilinear branching strategy, identify and select the relaxation problem and bilinear variables that need to be branched. Specifically, it includes: calculating the error of each bilinear term in the optimal solution of the relaxation problem with the smallest lower bound; selecting bilinear variables whose errors exceed the predetermined allowable value as branch candidate variables; sorting these candidate variables in descending order according to the error size, and selecting the top n The variable with the largest error is taken as the branch variable for the relaxed problem.

[0022] Step 4: For each selected candidate bilinear variable, determine the segmentation threshold and generate child nodes. The specific steps include: determining a reasonable threshold for each candidate variable to split its continuous variable domain into two new intervals; based on the segmentation threshold, generate two new child nodes, each of which corresponds to a new variable domain.

[0023] Step 5: Solve the generated child nodes in parallel and calculate their lower bound solutions, and recalculate the objective function value based on the current bound to obtain the upper bound.

[0024] Step 6: When the stopping condition is met, terminate the spatial branch and bound algorithm and output the result. Specifically, check whether the predetermined maximum branch time limit is reached or the predetermined upper and lower bounds are reached; if the stopping condition is met, output the optimal adjustment plan for the current decision stage and the corresponding upper bound value; otherwise, continue the branch operation.

[0025] In a second aspect, a dynamic operation adjustment device for an electric bus system based on a fast charging facility is provided, comprising: The first module: used to obtain bus line operation data; Based on the dynamic adjustment model of rolling optimization and the rolling time domain framework, the prediction time domain and control time domain of the current decision-making stage are constructed to monitor and collect real-time information on road environment, public transportation vehicles and passenger demand as input; The second module is used to construct bus operation, passenger loading and power consumption constraints; The third module is used to build an optimization model for dynamic adjustment of electric buses with the objective function of minimizing bus schedule deviation, passenger waiting time, power consumption and adjustment measure cost; The fourth module is used to solve the current stage decision by combining the spatial branch and bound method with acceleration technology until a feasible and high-quality adjustment optimization strategy is obtained; The fifth module is used to fix the electric bus adjustment and charging strategy within the control time domain of the current decision stage, and control the implementation to build the prediction time domain and control time domain of the next decision stage, and solve them until the end of the operation.

[0026] Beneficial effects of the present invention: The present invention establishes the problem of dynamic operation adjustment of electric bus systems based on fast charging facilities as a mixed integer nonlinear programming problem, and designs a rolling time domain method to implement real-time application of adjustment strategies for electric buses at each decision stage. This method divides the entire research time domain into multiple decision stages, each decision stage involves prediction and control time domains, and decomposes the original problem into smaller-scale problems for sequential and repeated solutions. In particular, a spatial branch and bound algorithm for bilinear terms is designed to solve the optimization problem formed in the prediction time domain. The present invention generates high-quality electric bus adjustment and charging plans by applying the above-mentioned mathematical model and solution algorithm, which effectively improves the operating efficiency, energy utilization efficiency and service quality of the electric bus system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention has the following accompanying drawings: Figure 1 This is a schematic diagram of the electric bus route; Figure 2 It is a schematic diagram of the rolling time domain method; Figure 3 This is a schematic diagram of Beijing Bus Rapid Transit Line 3. DETAILED DESCRIPTION

[0028] In order to make the objects, advantages and features of the present invention more obvious, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] The present invention analyzes the coupling relationship between electric bus traffic dynamics, vehicle overtaking, passenger load, capacity limitation and charging and discharging, such as Figure 1 As shown in the figure, a dynamic operation adjustment method of an electric bus system based on fast charging facilities is proposed. The specific implementation steps are described in detail below: Step 1: The number of bus stops, the distance between stations, the location of charging facilities, the charging power, the battery capacity of buses, etc. are used as inputs to the control system; based on the dynamic adjustment model of rolling optimization and the rolling time domain framework, the prediction time domain and control time domain of the current decision-making stage are constructed to monitor and collect real-time information on road environment, buses and passenger demand as inputs, including: environmental disturbance information, actual arrival and departure times, bus loads, and passenger arrival rate.

[0030] Step 2: Construct bus operation, passenger loading and power consumption constraints: The bus operation constraints include: bus arrival and departure time, stop time, inter-stop operation time and overtaking constraints; The passenger loading constraints include: waiting passengers, boarding passengers, getting off passengers, onboard passengers and stranded passengers constraints; The power consumption constraint conditions include: fast charging constraint and battery power constraint.

[0031] Step 3: Taking bus schedule deviation, passenger waiting time, power consumption and adjustment measure cost minimization as the objective function, a dynamic adjustment optimization model for electric buses is constructed; Step 4: For the optimization model of the dynamic operation adjustment method of the electric bus system based on fast charging facilities obtained in step 3, a spatial branch and bound method combined with acceleration technology is designed to solve the current stage decision until a feasible and high-quality adjustment optimization strategy is obtained. The acceleration technology includes: boundary shrinkage and bilinear specific branching.

[0032] Step 5: According to the obtained optimization scheme, fix the electric bus adjustment and charging strategy in the control time domain of the current decision stage, then return to step 1 to construct the prediction time domain and control time domain of the next decision stage, and solve until the end of operation. Figure 2 shown) In this embodiment, the following necessary parameters and data need to be determined in advance: (1) Electric bus routes, including bus stops, etc.; (2) Basic parameters of public transport vehicles, including passenger capacity and battery capacity of electric buses; (3) Bus operation parameters, including door opening and closing times, minimum departure intervals, and limits for stopping and operation adjustments; (4) Disturbance time parameters, including the station or section where the disturbance occurred, the resulting train stop or section operation delay time, etc.

[0033] Given the above conditions, in Python or Matlab, write code according to the mathematical model and constraints described in the content of the invention, construct the model framework proposed by the method, and obtain the corresponding electric bus operation plan.

[0034] like Figure 3 A single line with 22 stations is shown, and the following known conditions are given: (1) Electric bus line, consider a single electric bus line, such as Figure 2 As shown, this line has 22 bus stops, of which Station 6, Station 11 and Station 16 are equipped with fast charging infrastructure.

[0035] (2) The basic parameters of electric bus vehicles, operation and disturbance are shown in Table 1.

[0036] Table 1 parameter Numeric unit Bus passenger capacity 150 people Efficiency parameters 0.9 - Air Density 1.2 kg / m3 Drag coefficient 0.29 - Front area 2.27 Square meter Friction coefficient 0.01 - Bus body weight 5000 kilogram Average passenger quality 60 kilogram Gravitational constant 9.81 m / s2 Road slope 0 - Power of accessory load 2 kilowatt Power of auxiliary systems 10 kilowatt First station power 12 Kilowatt-hour Battery capacity 20 Kilowatt-hour Charging power at station 6 100 kilowatt Charging power at station 11 100 kilowatt Charging power at station 16 80 kilowatt Minimum power at arrival 3 Kilowatt-hour Control the maximum residence time 30 Second Controlling the running time interval [-30,30] Second Disturbance intervals for operation and residence time [0，180] Second Departure deviation weight coefficient 0.1 - Departure interval weight coefficient 1 - Passenger waiting time weight coefficient 1 - Electric bus energy consumption weight coefficient <![CDATA[1.5·10 4 ]]> - Control measure weight coefficient 1 - In order to illustrate the effectiveness of the dynamic adjustment strategy of the electric bus system proposed in this invention, the calculation results of the dynamic adjustment strategy of the electric bus system obtained by the spatial branch and bound method were compared with the rule-based station control strategy, and the following results were obtained: under the rule-based bus adjustment strategy, the departure deviation in the entire control range is 3.16·10^7s², the departure interval deviation is 6.92·10^6s², the waiting time is 3.16·10^6 s, and the energy consumption is 1.26·10^2 kW·h; under the bus adjustment strategy proposed in this invention, the departure deviation in the entire control range is 1.21·10 7 s², the departure interval deviation is 2.12·10 6 s², the waiting time is 2.53·10 6 s, and the energy consumption is 1.20·10 2 kW·h. By comparison, it is found that the four performance indicators of the dynamic adjustment strategy of the electric bus system proposed in the present invention are better than the rule-based bus adjustment strategy: departure deviation and schedule deviation are reduced by 61.54% and 69.43% respectively, waiting time is reduced by 19.95%, and energy consumption is reduced by 4.62%. The main reason is that compared with the rule-based bus adjustment strategy, the dynamic adjustment strategy proposed in this patent not only supports the adjustment of bus operation time, but also can actively recover delays, improve punctuality, reduce waiting time and energy consumption, and can also consider future interference and passenger flow changes in real time, so that decision makers can flexibly and dynamically adjust strategies, thereby enhancing the robustness of the system.

[0037] More example solution results are shown in Table 2 (calculation results of rule-based bus adjustment strategy) and Table 3 (calculation results of dynamic operation adjustment of an electric bus system based on fast charging facilities).

[0038] Table 2 stage Departure deviation Departure interval deviation Waiting time Energy consumption 1 <![CDATA[2.25·10 4 ]]> <![CDATA[2.24·10 4 ]]> <![CDATA[1.77·10 5 ]]> <![CDATA[1.13·10 1 ]]> 2 <![CDATA[6.88·10 4 ]]> <![CDATA[5.24·10 4 ]]> <![CDATA[1.17·10 5 ]]> <![CDATA[7.97·10 0 ]]> 3 <![CDATA[3.21·10 5 ]]> <![CDATA[1.76·10 5 ]]> <![CDATA[2.44·10 5 ]]> <![CDATA[1.33·10 1 ]]> 4 <![CDATA[7.74·10 5 ]]> <![CDATA[5.49·10 5 ]]> <![CDATA[3.56·10 5 ]]> <![CDATA[1.43·10 1 ]]> 5 <![CDATA[2.64·10 6 ]]> <![CDATA[8.91·10 5 ]]> <![CDATA[8.91·10 5 ]]> <![CDATA[1.54·10 1 ]]> 6 <![CDATA[1.28·10 6 ]]> <![CDATA[6.56·10 5 ]]> <![CDATA[3.26·10 5 ]]> <![CDATA[1.12·10 1 ]]> 7 <![CDATA[5.61·10 6 ]]> <![CDATA[1.27·10 6 ]]> <![CDATA[3.18·10 5 ]]> <![CDATA[1.59·10 1 ]]> 8 <![CDATA[3.81·10 6 ]]> <![CDATA[8.13·10 5 ]]> <![CDATA[3.05·10 5 ]]> <![CDATA[1.13·10 1 ]]> 9 <![CDATA[9.08·10 6 ]]> <![CDATA[8.55·10 5 ]]> <![CDATA[4.54·10 5 ]]> <![CDATA[1.31·10 1 ]]> 10 <![CDATA[7.94·10 6 ]]> <![CDATA[1.63·10 6 ]]> <![CDATA[4.78·10 5 ]]> <![CDATA[1.22·10 1 ]]> total <![CDATA[3.16·10 7 ]]> <![CDATA[6.92·10 6 ]]> <![CDATA[3.16·10 6 ]]> <![CDATA[1.26·10 2 ]]> Table 3 stage Departure deviation Departure interval deviation Waiting time Energy consumption 1 <![CDATA[2.93·10 4 ]]> <![CDATA[2.88·10 4 ]]> <![CDATA[1.78·10 5 ]]> <![CDATA[1.15·10 1 ]]> 2 <![CDATA[6.78·10 4 ]]> <![CDATA[3.15·10 4 ]]> <![CDATA[1.16·10 5 ]]> <![CDATA[8.11·10 0 ]]> 3 <![CDATA[2.02·10 5 ]]> <![CDATA[1.15·10 5 ]]> <![CDATA[2.33·10 5 ]]> <![CDATA[1.29·10 1 ]]> 4 <![CDATA[3.74·10 5 ]]> <![CDATA[1.84·10 5 ]]> <![CDATA[3.00·10 5 ]]> <![CDATA[1.40·10 1 ]]> 5 <![CDATA[1.18·10 6 ]]> <![CDATA[2.36·10 5 ]]> <![CDATA[3.08·10 5 ]]> <![CDATA[1.48·10 1 ]]> 6 <![CDATA[5.07·10 6 ]]> <![CDATA[2.18·10 5 ]]> <![CDATA[2.45·10 5 ]]> <![CDATA[1.07·10 1 ]]> 7 <![CDATA[2.28·10 6 ]]> <![CDATA[2.61·10 5 ]]> <![CDATA[2.53·10 5 ]]> <![CDATA[1.51·10 1 ]]> 8 <![CDATA[1.52·10 6 ]]> <![CDATA[2.11·10 5 ]]> <![CDATA[2.48·10 5 ]]> <![CDATA[1.05·10 1 ]]> 9 <![CDATA[3.65·10 6 ]]> <![CDATA[4.83·10 5 ]]> <![CDATA[3.81·10 5 ]]> <![CDATA[1.18·10 1 ]]> 10 <![CDATA[2.34·10 6 ]]> <![CDATA[3.47·10 5 ]]> <![CDATA[2.66·10 5 ]]> <![CDATA[1.07·10 1 ]]> total <![CDATA[1.21·10 7 ]]> <![CDATA[2.12·10 6 ]]> <![CDATA[2.53·10 6 ]]> <![CDATA[1.20·10 2 ]]> In this example, the Gurobi solver and the spatial branch-and-bound algorithm proposed in the present invention are used simultaneously to solve the problem and the mathematical model. In order to meet the real-time requirements, the following solution termination conditions are set: (a) When the Gurobi solver finds a feasible solution with an optimality gap of less than 3%, the solution is stopped; (b) When the solution time exceeds 180 seconds, the search process is terminated. The spatial branch-and-bound algorithm proposed in this patent has an average calculation time of 2.26 seconds in each stage and a maximum calculation time of 4.07 seconds, which meets the real-time requirements. In addition, the average optimality gap is 2.44% and the maximum optimality gap is 2.87%, indicating that this method can obtain high-quality solutions in a short time. In contrast, the average computation time required by the Gurobi solver to obtain a solution with the same objective function value as the spatial branch and bound method is more than 100.31 seconds. In particular, in the 3rd, 7th and 8th stages, Gurobi took more than 180 seconds and still failed to find a solution that was the same or better than the spatial branch and bound method, which further demonstrates the effectiveness of the spatial branch and bound algorithm proposed in this invention. More example solution results are shown in Table 4.

[0039] Table 4 Performance Indicators Objective function value Optimality gap of spatial branch and bound method Spatial Branch and Bound Method Running Time Gurobi runtime Phase 1 <![CDATA[1.72·10 6 ]]> 2.17% 2.22 10.11 Phase 2 <![CDATA[2.05·10 6 ]]> 2.78% 2.15 57.96 Phase 3 <![CDATA[2.39·10 6 ]]> 2.49% 3.84 180+ Phase 4 <![CDATA[3.06·10 6 ]]> 2.67% 4.07 64.12 Phase 5 <![CDATA[3.59·10 6 ]]> 2.18% 2.61 30.18 Phase 6 <![CDATA[3.04·10 6 ]]> 2.47% 1.43 31.04 Phase 7 <![CDATA[4.27·10 6 ]]> 2.10% 2.27 180+ Phase 8 <![CDATA[4.44·10 6 ]]> 2.66% 0.82 180+ Stage 9 <![CDATA[4.94·10 6 ]]> 1.98% 2.08 178.93 Phase 10 <![CDATA[3.95·10 6 ]]> 2.87% 1.12 90.79 In summary, the present invention proposes a dynamic operation adjustment method for an electric bus system based on a fast charging facility based on a spatial branch and bound method. The method can provide electric bus operators with a fast and effective adjustment and charging solution under interference, which helps to improve the operating efficiency of electric buses, passenger satisfaction and reduce energy consumption.

[0040] It should be noted that any process or method description in the embodiments may be understood as representing a module, fragment or portion of a code including one or more executable instructions for implementing steps of a specific logical function or process, and that the scope of the preferred embodiments of the present invention includes alternative implementations in which the functions may not be performed in the order shown or discussed, including performing the functions in a substantially simultaneous manner or in the reverse order according to the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0041] It should be noted that the logic and / or steps in the embodiments, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or equipment (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or equipment and execute instructions), or in combination with these instruction execution systems, devices or equipment. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or equipment, or in combination with these instruction execution systems, devices or equipment. More specific examples of computer-readable media (non-exhaustive list) include the following: an electrical connection with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0042] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0043] A person skilled in the art may understand that all or part of the steps of implementing the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0044] In addition, each functional module in the content of the present invention can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0045] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0046] The above embodiments describe the technical solutions of the present invention in detail. Obviously, the present invention is not limited to the described embodiments. Based on the embodiments of the present invention, people familiar with the technical field can also make various changes accordingly, but any changes that are equivalent to or similar to the present invention belong to the scope of protection of the present invention. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field.

Claims

1. A method for dynamic operation adjustment of an electric bus system based on fast charging facilities, characterized in that: The steps include: Obtain bus route operation data; Based on the dynamic adjustment model of rolling optimization and the rolling time domain framework, the prediction time domain and control time domain of the current decision-making stage are constructed to monitor and collect real-time information on road environment, public transportation vehicles and passenger demand as input; Construct bus operation, passenger loading, and power consumption constraints; Taking bus schedule deviation, passenger waiting time, power consumption and adjustment measure cost minimization as the objective function, a dynamic adjustment optimization model for electric buses is constructed. The spatial branch-and-bound method combined with acceleration technology is used to solve the current stage decision until a feasible and high-quality adjustment optimization strategy is obtained; The electric bus adjustment and charging strategies in the control time domain of the current decision stage are fixed, and then the prediction time domain and control time domain of the next decision stage are constructed and solved until the end of the operation.

2. The method according to claim 1, characterized in that The bus operation constraints include: bus arrival and departure time, stop time, inter-stop operation time and overtaking constraints; The passenger loading constraints include: waiting passengers, boarding passengers, getting off passengers, onboard passengers and stranded passengers constraints; The power consumption constraint conditions include: fast charging constraint and battery power constraint.

3. The method according to claim 1, characterized in that The acceleration techniques include: boundary shrinkage and bilinear specific branches.

4. The method according to claim 1, characterized in that The dynamic adjustment model based on rolling optimization and the rolling time domain framework to construct the prediction time domain and control time domain of the current decision stage include: Modeling the topology of electric bus routes: Constructing a set of nodes of the electric bus system, including the start and end station nodes, intermediate station nodes, and station nodes equipped with fast charging infrastructure; Based on the node set and the bus running direction, the bus running arc is constructed: the electric bus starts from the starting station node, arrives and stays at the intermediate station node to pick up and drop off passengers, and is charged at the station node equipped with fast charging infrastructure. Each running arc corresponds to a different discharge process; Based on the given electric bus line topology, the complete electric bus operation adjustment time domain is divided into multiple decision-making stages, and a rolling time domain decision-making framework is constructed; each decision stage involves the prediction time domain and the control time domain; according to the bus operation, passenger demand and interference information, the bus adjustment strategy for a longer period of time in the future, namely the prediction time domain, is formulated and solved; the adjustment strategy for a shorter period of time in the future, namely the control time domain, is sent to the bus for execution.

5. The method according to claim 2, characterized in that The bus operation constraints include: Electric bus arrival time constraint: The arrival time of an electric bus at each station is the sum of the departure time of the electric bus at the previous station and the running time between the two stations, as shown in formula (1): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The actual arrival time of Represents electric buses At the bus stop The actual departure time of Represents electric buses At the bus stop and Station The actual inter-station running time between Electric bus operation time constraint: The operation time of an electric bus between stations is the sum of the planned operation time between stations, the operation adjustment time and the interference time, as shown in formula (2): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop and Station The actual running time between Represents electric buses At the bus stop and Station The planned running time between Represents electric buses At the bus stop and Station The running adjustment time between parameters Represents electric buses At the bus stop and Station Interference time between operations; Electric bus stay time constraint: The stay time of an electric bus at a station is the sum of the door opening and closing time of the bus at the station, the time for passengers to get on and off the bus, and the interference time, as shown in formula (3): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The actual residence time of Represents the bus door opening and closing time; parameter Represents the average time for each passenger to get on and off the bus; variable Represents electric buses Arrive at the bus station The number of passengers getting off at time; variable Represents electric buses Arrive at the bus station The number of passengers boarding at time ; parameter Represents electric buses At the bus stop Disruption time during stops; Departure time constraint of electric buses: The departure time of electric buses at each station is the sum of the arrival time, stay time and adjustment time at the station, as shown in formula (4): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The actual departure time of Represents electric buses At the bus stop The actual arrival time of Represents electric buses At the bus stop The actual residence time of the variable Represents electric buses At the bus stop The time taken for adjustments; Electric bus departure interval constraint: The departure interval of an electric bus at each station is the difference between the departure time of the bus at the station and the departure time of the bus immediately preceding it, as shown in formula (5): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The interval between the vehicles in front; variable Represents electric buses At the bus stop The actual departure time of Represents electric buses The car in front of you is at the bus stop The departure time of the bus is due to the possibility of overtaking. Not necessarily equal to ; Electric bus overtaking constraint: By introducing a binary variable to represent the departure order of the bus, it is determined whether the electric bus overtakes, as shown in formulas (6)-(11): ; ; ； ； ； ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The actual departure time of Represents electric buses The car in front of you is at the bus stop departure time; if at the bus station Electric Bus The departure time is later than the bus , then the variable ,otherwise, If the bus It's a bus exist If the vehicle in front of the vehicle is standing next to it, then the variable ,otherwise, ;parameter is a large positive number; Electric bus operation constraints: The adjustment time of electric buses staying at stations and running between stations must be between the predetermined maximum and minimum limits, and the departure interval should be greater than the predetermined minimum limit; as shown in formulas (12)-(14): ； ； ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop Adjustment time taken; variable Represents electric buses At the bus stop and Station The running adjustment time between parameters Represents an electric bus at the station The maximum adjustment time that can be taken during the stop; parameter and Represents the electric bus and Station The minimum and maximum adjustment times that can be taken when running between Represents electric buses At the bus stop The actual departure time of Representatives immediately followed in the electric bus The bus ahead is at the bus stop Departure time; parameters Representatives at the station Minimum departure intervals between electric buses.

6. The method according to claim 5, characterized in that The passenger loading constraints include: Waiting passenger constraint: The number of passengers waiting at the bus station includes newly arrived passengers and stranded passengers from the previous bus, as shown in formula (15): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Representatives at the bus stop Waiting for the electric bus The number of waiting passengers; parameter Representing passengers at bus stops Waiting for the electric bus The arrival rate of Represents electric buses With the bus At the bus stop The departure interval of Represents the bus station Electric bus The number of passengers stranded in the vehicle immediately preceding it; Electric bus stranded passenger constraint: The number of passengers stranded by an electric bus is equal to the number of waiting passengers minus the number of boarding passengers, as shown in formula (16): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents the bus station Electric bus Number of stranded passengers; variable Representatives at the bus stop Waiting for the electric bus The number of waiting passengers; variable Representatives at the bus stop Boarding an electric bus Number of passengers; The constraint on passengers stranded in the preceding vehicle immediately adjacent to the electric bus is shown in formula (17): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents the bus station Electric bus The number of passengers left behind by the bus immediately preceding it; if the bus It's a bus exist If the vehicle in front of the vehicle is standing next to it, then the variable ,otherwise, ;variable Represents the bus station Electric bus Number of stranded passengers; Boarding passenger constraint: The number of boarding passengers involves the smaller of the number of waiting passengers and the remaining capacity of the bus, as shown in formula (18): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stages, passing stations The set of public transport vehicles; variables Representative at the bus stop Boarding an electric bus Number of passengers; variable Representatives at the bus stop Waiting for the electric bus The number of waiting passengers; parameter Represents electric buses Capacity; variable Represents electric buses Arrive at the bus station The number of passengers getting off at time; variable Represents electric buses At the bus stop The number of passengers on board the vehicle at the station; The passenger constraint: The number of passengers on the electric bus involves the number of passengers on the previous station plus the number of passengers on the bus minus the number of passengers on the bus; as shown in formula (19): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop Number of passengers on board; variable Represents electric buses At the bus stop The number of passengers boarding the bus; variable Representative electric bus at the bus stop Number of passengers getting off the bus; Passenger alighting constraint: The number of passengers alighting at a bus stop is linearly related to the number of passengers on board at the previous stop and the alighting rate, as shown in formula (20): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Representative electric bus at the bus stop The number of passengers getting off the bus; variable Represents electric buses At the bus stop The number of passengers on board at the station; parameter Represents electric buses At the bus stop The percentage of passengers getting off the bus.

7. The method according to claim 6, characterized in that The power consumption constraint conditions include: Energy consumption constraints of electric buses: The energy consumption of electric buses is related to the changes in air resistance, gravitational potential energy, rolling resistance power consumption, and auxiliary equipment power consumption, as shown in formula (21): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stages, passing stations The set of public transport vehicles; variables Represents electric buses At the bus stop Energy consumption during parking and interval operation; parameters Representative bus station and The distance between stands for efficiency coefficient; parameter Represents air density; parameter Represents the air resistance coefficient; parameter Represents the front area of ​​the electric bus; parameter Represents the friction coefficient of the electric bus; parameter Represents the slope of the electric bus; parameter and They represent the body weight of the electric bus when unloaded and the average weight of passengers; Represents the acceleration due to gravity; parameter and Represent the power of the auxiliary load and auxiliary system of the electric bus respectively; Represents electric buses At the bus stop and Station The actual running time between Represents electric buses At the bus stop The number of passengers on board; parameter Represents electric buses At the bus stop Interruption time during stops; variable Represents electric buses At the bus stop The actual residence time of the variable Represents electric buses At the bus stop The time taken for adjustments; From formula (21), it can be seen that the energy consumption of electric buses has a nonlinear relationship with the running speed. The energy consumption of electric buses is approximated by the piecewise linearization method, as shown in formulas (22)-(23): ; ; in: represent Approximate function of ; Represents the division interval Breakpoints Representative connection and The slope of the straight line segment; Linearize formula (23) into formula (24)-(26): ; ; ； Among them: variable Indicates electric bus At the bus stop With the station If the running time between Greater than ,but ;otherwise, ; Constraint on the charging capacity of electric buses: The charging capacity of electric buses should be the minimum value of the remaining capacity of the battery and the capacity charged at the station, as shown in formula (27): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stages, passing stations A collection of public transport vehicles; a collection represents the set of bus stops with fast charging facilities; the variable Represents electric buses At the station Charging capacity; parameters Represents electric buses At the bus stop Charging power; parameters Represents the time when the charger is connected and disconnected; parameter Represents the battery power of the electric bus at the first stop; variable Represents electric buses At the bus stop The actual residence time of the variable Represents electric buses With the bus At the bus stop The departure interval of Represents electric buses At the bus stop The amount of electricity; Electric bus power constraint: The current power of the electric bus is calculated as shown in formula (28): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses Among all stations with fast charging facilities The power of the next station; variable Represents electric buses At the bus stop The amount of electricity; variable Represents electric buses At the station Energy consumption of station parking and interval operation; variable Represents electric buses At the station The charge level at The power constraint of electric buses at each station: The power of electric buses at each station should be greater than the minimum power, as shown in formula (29): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; variables Represents electric buses At the bus stop The power of Representative electric bus at the bus stop Minimum power.

8. The method according to claim 1, characterized in that The objective function includes: The penalty for electric bus departure time deviation is shown in formula (30): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; parameters Represents the weight coefficient of the departure time deviation of electric buses; variable Represents electric buses At the bus stop The actual departure time; parameters Represents electric buses At the bus stop The planned departure time; The penalty for deviation of the departure interval of electric buses is shown in formula (31): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; parameters Represents the weight coefficient of the electric bus departure interval deviation; variable Represents electric buses At the bus stop The actual departure time of Represents electric buses The car in front of you is at the bus stop Departure time; parameters Represents electric buses At the bus stop The interval between departures of the previous bus; The passenger waiting time penalty for electric buses is shown in formula (32): ; Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; parameters The weight coefficient representing the waiting time of passengers at the station; parameter Representing passengers at bus stops Waiting for the electric bus The arrival rate of Represents electric buses With the bus At the bus stop The departure interval of Represents the next step in electric buses The bus ahead is at the bus stop Number of stranded passengers; The energy consumption penalty for electric buses is shown in formula (33): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; parameters Represents the weight coefficient of the energy consumption of electric buses; variable Represents electric buses At the bus stop Energy consumption during parking and interval operation; The adjustment cost penalty for electric buses is shown in formula (34): ； Where: Index Represents a bus stop; index Stands for Electric Bus; Collection Representative bus station collection of Representative in the decision-making stage, passing through the station The set of public transport vehicles; parameters Represents the weight coefficient of the adjustment measures taken on the operation of electric buses; variable Represents electric buses At the bus stop Adjustment time taken; variable Represents electric buses At the bus stop and Station The running adjustment time between.

9. The method according to claim 7, characterized in that The spatial branch and bound method comprises: Step 1: The dynamic adjustment optimization mathematical model of the electric bus system with fast charging infrastructure is abbreviated as model M1. Formulas (17), (18), and (27) are linearized, and the mixed integer nonlinear programming model M1 can be reconstructed into a mixed integer bilinear programming model M2, which is solved using the spatial branch and bound algorithm. Step 2: For formula (32), use McCormick’s inequality to solve the bilinear term Relax the original mixed integer bilinear programming model to a mixed integer convex quadratic programming model, and the solution obtained is the lower bound solution of the original problem; Step 3: According to the bilinear branching strategy, identify and select the relaxation problem and bilinear variables that need to be branched; specifically, calculate the error of each bilinear term in the optimal solution of the relaxation problem with the smallest lower bound; select the bilinear variables whose errors exceed the predetermined allowable value as branch candidate variables; sort these candidate variables in descending order according to the error size, and select the top n The variable with the largest error is used as the branch variable for the relaxed problem; Step 4: For each selected candidate bilinear variable, determine the segmentation threshold and generate child nodes; the specific steps include: determining a reasonable threshold for each candidate variable to split its continuous variable domain into two new intervals; based on the segmentation threshold, generate two new child nodes, each child node corresponds to a new variable domain; Step 5: Solve the generated child nodes in parallel and calculate their lower bound solutions, and recalculate the objective function value according to the current bound to obtain the upper bound; Step 6: When the stopping condition is met, terminate the spatial branch and bound algorithm and output the result; the specific steps include: checking whether the predetermined maximum branch time limit is reached or the predetermined upper and lower bounds are reached; if the stopping condition is met, output the optimal adjustment plan for the current decision stage and the corresponding upper bound value; otherwise, continue the branch operation.

10. A dynamic operation adjustment device for an electric bus system based on a fast charging facility, characterized in that: include: The first module: used to obtain bus line operation data; Based on the dynamic adjustment model of rolling optimization and the rolling time domain framework, the prediction time domain and control time domain of the current decision-making stage are constructed to monitor and collect real-time information on road environment, public transportation vehicles and passenger demand as input; The second module is used to construct bus operation, passenger loading and power consumption constraints; The third module is used to build an optimization model for dynamic adjustment of electric buses with the objective function of minimizing bus schedule deviation, passenger waiting time, power consumption and adjustment measure cost; The fourth module is used to solve the current stage decision by combining the spatial branch and bound method with acceleration technology until a feasible and high-quality adjustment optimization strategy is obtained; The fifth module is used to fix the electric bus adjustment and charging strategy within the control time domain of the current decision stage, and control the implementation to build the prediction time domain and control time domain of the next decision stage, and solve them until the end of the operation.

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