A dynamic operation adjustment method for an electric bus system based on a fast charging facility

Abstract

The application provides a kind of electric bus system dynamic operation adjustment method based on fast charging facilities, to cope with the real-time changes of operating environment. The dynamic adjustment problem of electric bus system is reconstructed as a mixed integer nonlinear programming model. At the same time, the factors such as bus operation dynamics, dynamic passenger loading, vehicle overtaking, capacity limitation and charging and discharging process are considered comprehensively. Based on the dynamic adjustment model of rolling optimization, the prediction time domain and control time domain of the current decision stage are constructed. The electric bus dynamic adjustment optimization model is constructed. The space branch and bound method combined with acceleration technology is designed to solve the current stage decision. Through this strategy, the scheduling and charging arrangement of the bus can be optimized, the overall operation efficiency and service quality of the system can be significantly improved, the energy consumption and operating cost can be reduced, so as to promote the wide application and sustainable development of electric bus system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of road transport organization, and particularly relates to a dynamic operation adjustment method for an electric bus system based on a fast charging facility. BACKGROUND

[0002] With the continuous improvement of the electrification level of bus fleets, fast charging technology is considered as an emerging solution that can effectively solve the high cost problem caused by large vehicle-mounted batteries. Compared with traditional diesel buses, electric buses, as an environmentally friendly means of transportation, show significant environmental and economic advantages. Although electric buses have many advantages, their driving range is limited and the charging time is long. 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 conveniently charge batteries through chargers along the route during operation, thereby reducing the number of batteries required and reducing battery costs, which has obvious cost advantages compared to buses that rely on slow charging at night.

[0003] In an uncontrolled electric bus system, due to the uncertainty of road traffic conditions, the daily operation of buses is often disturbed by many factors, such as bus delays and disruptions to scheduled charging plans, which inevitably lead to a significant decline in system reliability and service quality. Therefore, in a disturbed operating environment, public 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 operating efficiency and sustainable development of electric bus systems. SUMMARY

[0004] Therefore, the application proposes a dynamic operation adjustment method for an electric bus system based on a fast charging facility 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 constraints, and charging and discharging processes are considered. Through this strategy, the scheduling and charging arrangement of buses can be optimized, significantly improving the overall operating efficiency and service quality of the system, reducing energy consumption and operating costs, and thus promoting the widespread application and sustainable development of electric bus systems.

[0005] To achieve the above purpose, the technical solution adopted by the application reconstructs the dynamic adjustment problem of the electric bus system of interest into a mixed integer nonlinear programming model.

[0006] In a first aspect, a dynamic operation adjustment method for an electric bus system based on a fast charging facility includes the following steps:

[0007] Obtain bus line operation data, including bus line station quantity, station spacing, charging facility location, charging power, bus battery capacity and other information.

[0008] 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 stage are constructed, and real-time information of road environment, bus vehicles and passenger demand is monitored and collected as input, including: environmental disturbance information, actual arrival and departure time, bus passenger number, passenger arrival rate.

[0009] Construct bus operation, passenger loading and power consumption constraints;

[0010] Take the minimum of bus schedule deviation, passenger waiting time, power consumption and adjustment measure cost as the objective function, and construct an electric bus dynamic adjustment optimization model;

[0011] Combine the space branch and bound method with acceleration technology to solve the current stage decision until a feasible and high-quality adjustment optimization strategy is obtained;

[0012] Fix the electric bus adjustment and charging strategy in the control time domain of the current decision stage, then construct the prediction time domain and control time domain of the next decision stage, and solve until the operation ends.

[0013] Specifically, the bus operation constraints include bus arrival and departure time, stop time, inter-station running time and overtaking constraints;

[0014] The passenger loading constraints include waiting passengers, boarding passengers, alighting passengers, on-board passengers and stranded passengers;

[0015] The power consumption constraints include fast charging constraints and battery power constraints.

[0016] Specifically, the acceleration technology includes boundary contraction and bilinear special branch.

[0017] Specifically, the dynamic adjustment model includes:

[0018] Model the topology of the electric bus line: construct the node set of the electric bus system, including the start and end station nodes, intermediate station nodes and station nodes equipped with fast charging infrastructure;

[0019] Based on the node set and bus operation direction, construct the bus operation arc: the electric bus departs from the start station node, arrives at and stays at the intermediate station node to load and unload passengers, and charges at the station node equipped with fast charging infrastructure, each operation arc corresponds to a different discharging process;

[0020] Based on the given electric bus route 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-making stage involves the prediction time domain and the control time domain. Based on bus operation, passenger demand and disturbance information, bus adjustment strategies for a relatively long period of time in the prediction time domain are formulated and solved. The adjustment strategies for a relatively short period of time in the control time domain are then distributed to the buses for execution.

[0021] Specifically, the bus operation constraints include:

[0022] Arrival time constraint for electric buses: The arrival time of electric buses at each station is the sum of the departure time of the electric bus at the previous station and the travel time between the two stations, as shown in formula (1):

[0023] ;

[0024] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Represents electric buses At the bus stop Actual arrival time; variables Represents electric buses At the bus stop Actual departure time; variables Represents electric buses At the bus stop and the station The actual inter-station travel time between them;

[0025] Electric bus running time constraint: The running time of an electric bus between stations is the sum of the planned running time between stations, the running adjustment time, and the interference time, as shown in formula (2):

[0026] ;

[0027] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Represents electric buses At bus stops and stations The actual running time between; variables Represents electric buses At the bus stop and the station Planned runtime between; variables Represents electric buses At the bus stop and the station Adjustment time between parameters; Represents electric buses At the bus stop and the station Interference time between operations;

[0028] Electric bus dwell time constraint: The dwell time of an electric bus at a station is the sum of the bus door opening and closing time, passenger boarding and alighting time, and interference time, as shown in formula (3):

[0029] ;

[0030] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Represents electric buses At the bus stop Actual stay time; parameters 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 disembarking at that time; variables Represents electric buses Arrive at the bus station Number of passengers boarding at any given time; parameters Represents electric buses At the bus stop Interference time during station stops;

[0031] Electric bus departure time constraint: The departure time of an electric bus at each station is the sum of its arrival time, dwell time, and adjustment time at that station, as shown in formula (4):

[0032] ;

[0033] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Represents electric buses At the bus stop Actual departure time; variables Represents electric buses At the bus stop Actual arrival time; variables Represents electric buses At the bus stop Actual stay time; variables Represents electric buses At the bus stop Adjustment timeframe;

[0034] Electric bus departure interval constraint: The departure interval of electric buses 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):

[0035] ;

[0036] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Represents electric buses At the bus stop Departure interval with the preceding train; variable Represents electric buses At the bus stop Actual departure time; variables Represents electric buses The car in front of me is at the bus stop. Due to the possibility of overtaking, the bus departure time is subject to change. Not necessarily equal to ;

[0037] Electric bus overtaking constraint: binary variables are introduced to represent the departure sequence of buses to determine whether an electric bus overtakes another bus, as shown in equations (6)-(11):

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] wherein index represents a bus stop; index represents an electric bus; set represents the set of bus stops ; set represents the set of bus stops visited by buses at the th decision stage; variable represents the actual departure time of an electric bus at a bus stop ; variable represents the departure time of the immediately preceding bus at a bus stop ; variable , if the departure time of an electric bus at a bus stop is later than that of a bus , otherwise ; variable , if a bus is the immediately preceding bus of an electric bus at a bus stop , otherwise ; parameter is a large positive number;

[0045] Electric bus operation constraint: the adjustment time of an electric bus for staying at a bus stop and for running between bus stops must be within the predetermined maximum and minimum limits, and the departure interval should be greater than the predetermined minimum limit; as shown in equations (12)-(14):

[0046] ;

[0047] ​ ;

[0048] ;

[0049] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Represents electric buses At the bus stop Adjustment time taken; variables Represents electric buses At the bus stop and the station Adjustment time between parameters; This represents electric buses at the station. Maximum adjustment time that can be taken during the stay; parameters and This represents electric buses in and the station The minimum and maximum adjustment times that can be taken during runtime; variables Represents electric buses At the bus stop Actual departure time; variables The representative immediately followed the electric bus The bus ahead is at the bus stop. Departure time; parameters Representative at the station Minimum departure interval between electric buses.

[0050] The passenger loading constraints include:

[0051] Passenger waiting constraints: The number of passengers waiting at the bus stop includes newly arrived passengers and passengers remaining from the previous bus, as shown in formula (15):

[0052] ;

[0053] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations set of transit vehicles; variable representing passengers waiting for an electric bus at a bus stop waiting for an electric bus at a bus stop set of waiting passengers; parameter representing passengers waiting for an electric bus at a bus stop waiting for an electric bus at a bus stop arrival rate of electric buses; variable representing an electric bus at a bus stop at a bus stop departure interval of electric buses at a bus stop; variable representing electric buses at a bus stop immediately preceding a bus at a bus stop number of passengers stranded by an electric bus immediately preceding a bus at a bus stop

[0054] stranded passengers constraint for electric buses: 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 equation (16):

[0055] ;

[0056] where: index represents a bus stop; index represents an electric bus; set represents the set of bus stops represents the set of bus stops represents the set of transit vehicles passing through bus stops at the th decision stage; variable representing electric buses at a bus stop stranded by an electric bus at a bus stop; variable representing passengers waiting for an electric bus at a bus stop waiting for an electric bus at a bus stop; variable representing passengers boarding an electric bus at a bus stop waiting for an electric bus at a bus stop set of waiting passengers; variable representing passengers boarding an electric bus at a bus stop an electric bus at a bus stop ;

[0057] stranded passengers constraint for electric buses immediately preceding a bus at a bus stop as shown in equation (17):

[0058] ;

[0059] where: index represents a bus stop; index represents an electric bus; set represents the set of bus stops represents the set of bus stops represents the set of transit vehicles passing through bus stops at the Each decision-making stage, passing through stations A set of public transport vehicles; variables This represents at the bus stop electric bus The number of passengers stranded in the adjacent vehicle; if the bus It's a bus. exist If the car in front is the one immediately in front, then the variable... ,otherwise, ;variable This represents at the bus stop electric bus The number of stranded passengers;

[0060] Passenger boarding constraint: The number of passengers boarding involves the smaller of the number of waiting passengers and the remaining capacity of the bus, as shown in formula (18):

[0061] ;

[0062] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Representative at the bus stop Boarding the electric bus Number of passengers; variables Representative at the bus stop Waiting for the electric bus Number of waiting passengers; parameters Represents electric buses capacity; variable Represents electric buses Arrive at the bus station The number of passengers disembarking at that time; variables Represents electric buses At the bus stop The number of passengers on board at the station;

[0063] The passenger load constraint is as follows: the number of passengers on the electric bus involves the number of passengers on board at the previous station plus the number of passengers boarding and subtracting the number of passengers alighting. As shown in formula (19):

[0064] ;

[0065] Where: index Represents bus stops; index set of electric buses set of bus stops set of bus stops set of buses passing through the bus stop at the th decision stage; variable set of buses passing through the bus stop at the number of passengers on board the electric bus at the bus stop number of passengers on board the electric bus at the bus stop number of passengers boarding the electric bus at the bus stop; variable number of passengers boarding the electric bus at the bus stop; variable number of passengers boarding the electric bus at the bus stop; variable number of passengers alighting from the electric bus at the bus stop. alighting passengers constraint: the number of passengers alighting from the electric bus at the bus stop is linearly related to the number of passengers on board the bus at the previous bus stop and the alighting rate, as shown in equation (20):

[0066]

[0067] ;

[0068] wherein index represents a bus stop; index represents an electric bus; set represents a set of bus stops represents a set of bus stops represents a set of buses passing through the bus stop at the th decision stage; variable represents the number of passengers alighting from the electric bus at the bus stop; variable represents the number of passengers on board the electric bus at the bus stop; parameter represents the number of passengers boarding the electric bus at the bus stop; parameter represents the number of passengers boarding the electric bus at the bus stop; parameter represents the proportion of passengers alighting from the electric bus at the bus stop. The power consumption constraint condition comprises: electric bus energy consumption constraint: the energy consumption of the electric bus is related to the changes in air resistance, gravitational potential energy and rolling resistance power consumption, as well as auxiliary equipment power consumption, as shown in equation (21):

[0069]

[0070]

[0071] ;

[0072] wherein index represents a bus stop; index represents an electric bus; set represents a set of bus stops​​​​​​ set of collection; collection represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable represent the set of buses passing through the station at the decision stage; set of collection; variable

[0073] The energy consumption of the electric bus is nonlinear with the running speed, which is approximated by piecewise linearization, as shown in equations (22)-(23):

[0074] ;

[0075] ;

[0076] wherein: represents an approximation function; represents a breakpoint dividing interval represents the slope of a straight line segment connecting and

[0077] Linearize equation (23) into equations (24)-(26):

[0078]

[0079]

[0080]

[0081] wherein variable denotes the operating time of an electric bus between a bus stop and the bus stop , if is greater than , then ; otherwise,

[0082] Electric bus charging amount constraint: the charging amount of an electric bus should take the minimum value of the remaining battery amount and the amount of charging at a bus stop, as shown in equation (27):

[0083]

[0084] wherein index represents a bus stop; index represents an electric bus; set represents a set of bus stops ; set represents a set of bus vehicles passing through bus stop at the th decision stage; set represents a set of bus stops provided with fast charging facilities; variable represents the charging amount of an electric bus at bus stop ; parameter represents the charging power of an electric bus at bus stop ; parameter represents the time of charger connection and disconnection; parameter represents the battery amount of an electric bus at the first bus stop; variable represents the battery amount of an electric bus ​​​​​​​​At the bus stop Actual stay time; variables Represents electric buses With bus At the bus stop Departure interval; variable Represents electric buses At the bus stop The amount of electricity;

[0085] Electric bus power constraint: The current power of the electric bus is calculated as shown in formula (28):

[0086] ;

[0087] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Represents electric buses In all sites with fast charging facilities The battery level at the next station; variable Represents electric buses At the bus stop Electricity; variable Represents electric buses At the station Energy consumption during station parking and inter-station operation; variables Represents electric buses At the station The amount of charge at the location;

[0088] Electric bus power constraints at each station: The electric bus power at each station should be greater than the minimum power requirement, as shown in formula (29):

[0089] ;

[0090] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Represents electric buses At the bus stop Battery level; parameters Electric buses at bus stops The minimum charge.

[0091] The objective function includes:

[0092] The penalty for deviation in departure time of electric buses is shown in formula (30):

[0093] ;

[0094] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A collection of public transport vehicles; parameters Weighting coefficients representing the deviation in departure time of electric buses; variables Represents electric buses At the bus stop Actual departure time; parameters Represents electric buses At the bus stop The scheduled departure time;

[0095] The penalty for deviation in departure interval of electric buses is shown in formula (31):

[0096] ;

[0097] Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A collection of public transport vehicles; parameters Weighting coefficients representing the deviation in departure intervals of electric buses; variables Represents electric buses At the bus stop Actual departure time; variables Represents electric buses The car in front of me is at the bus stop. Departure time; parameters Represents electric buses At the bus stop the headway between the previous bus;

[0098] The passenger waiting time penalty of the electric bus is shown in equation (32):

[0099] ;

[0100] wherein index represents a bus stop; index represents an electric bus; set represents the set of bus stops ; set represents the set of bus stops visited by the bus vehicles at the th decision stage; parameter represents the weight coefficient of the passenger waiting time at the bus stop; parameter represents the arrival rate of the passengers waiting for the electric bus at the bus stop ; variable represents the headway between the electric bus and the bus at the bus stop ; variable represents the number of passengers at the bus stop who are waiting for the electric bus ;

[0101] The energy consumption penalty of the electric bus is shown in equation (33):

[0102] ;

[0103] wherein index represents a bus stop; index represents an electric bus; set represents the set of bus stops ; set represents the set of bus stops visited by the bus vehicles at the th decision stage; parameter represents the weight coefficient of the energy consumption of the electric bus; variable represents the energy consumption of the electric bus at the bus stop .

[0104] The adjustment cost penalty of the electric bus is shown in equation (34):

[0105] ;

[0106] wherein: index represents a bus station; index represents an electric bus; set represents a set of bus stations ; set represents a set of bus stations visited by the electric bus at the th decision stage; parameter represents a weight coefficient of adjustment measures taken for the electric bus operation; variable represents an electric bus at a bus station ; variable represents an electric bus at a bus station ; and a set of stations ; variable

[0107] The spatial branch and bound method comprises:

[0108] Step 1: a dynamic adjustment optimization mathematical model of an electric bus system with fast charging infrastructure is abbreviated as model M1, and formulas (17), (18) and (27) are linearized, so that the mixed integer nonlinear programming model (MNILP) M1 can be reconstructed as a mixed integer bilinear programming model (MIBLP) M2, and a spatial branch and bound algorithm is used to solve the MIBLP model.

[0109] Step 2: for formula (32), a McCormick inequality is used to relax the bilinear term , so that the original MIBLP model is relaxed into a mixed integer convex quadratic programming (MIQP) model, and a solution obtained by the MIQP model is a lower bound solution of the original problem.

[0110] The McCormick inequality bilinear relaxation: a bilinear term in the objective function is converted into a linear form through a continuous variable, as shown in formula (35):

[0111] ;

[0112] wherein: index represents a bus station; index represents an electric bus; set represents a set of bus stations ; set represents a set of bus stations visited by the electric bus at the th decision stage; variable represents a set of bus stations visited by the electric bus at the th decision stage; variable an approximation of the product of the variables represent the number of passengers waiting at the bus stop is served by the electric bus represent the number of passengers waiting at the bus stop and represent the lower and upper bounds of , respectively; variables represent the electric bus and the bus at the bus stop ; parameters and represent the lower and upper bounds of , respectively. By McCormick inequality bilinear relaxation, the original problem is transformed into a relaxed problem, and the solution obtained is the lower bound solution of the original problem.

[0113] Step 3: According to the bilinear branching strategy, identify and select the relaxation problem and bilinear variable that needs 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 the bilinear variable whose error exceeds the predetermined tolerance as the branching candidate variable; sorting these candidate variables in descending order according to the error size, and selecting the first n largest error variable as the branching variable for the relaxation problem.

[0114] Step 4: For each selected candidate bilinear variable, determine the split threshold and generate child nodes. The specific steps include: determining a reasonable threshold for each candidate variable, and dividing its continuous variable domain into two new intervals; based on the split threshold, generate two new child nodes, each corresponding to a new variable domain.

[0115] 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.

[0116] Step 6: When the stop condition is met, terminate the space branching and bounding algorithm and output the result. Specifically, it includes: checking whether the predetermined maximum branching time limit is reached or the predetermined upper and lower bound gap is reached; if the stop condition is met, output the optimal adjustment scheme at the current decision stage and the corresponding upper bound value; otherwise, continue the branching operation.

[0117] In the second aspect, a dynamic operation adjustment device for an electric bus system based on a fast charging facility is provided, which includes:

[0118] First module: used for obtaining bus line operation data;

[0119] Based on the dynamic adjustment model of rolling optimization and the rolling horizon framework, the prediction horizon and control horizon of the current decision stage are constructed, and the real-time information of road environment, bus vehicle and passenger demand is monitored and collected as input;

[0120] The second module is used for constructing bus operation, passenger loading and power consumption constraints;

[0121] The third module is used for constructing an electric bus dynamic adjustment optimization model with the minimum bus schedule deviation, passenger waiting time, power consumption and adjustment measure cost as the objective function;

[0122] The fourth module is used for solving the current stage decision by combining the space branch and bound method of acceleration technology until a feasible and high-quality adjustment optimization strategy is obtained;

[0123] The fifth module is used for fixing the electric bus adjustment and charging strategy in the control horizon of the current decision stage, and controlling the implementation of constructing the prediction horizon and control horizon of the next decision stage, and solving until the operation ends.

[0124] The beneficial effects of the present application are as follows:

[0125] The present application establishes the dynamic operation adjustment problem of the electric bus system based on the fast charging facility as a mixed integer nonlinear programming problem, and designs a rolling horizon method to realize the real-time application of the adjustment strategy of the electric bus in each decision stage. The whole research time domain is divided into multiple decision stages, each decision stage involves a prediction and control horizon, and the original problem is decomposed into smaller scale problems for sequential repeated solving. In particular, for the optimization problem formed in the prediction horizon, a space branch and bound algorithm for bilinear terms is designed for solving. By applying the above mathematical model and solving algorithm, the present application generates high-quality electric bus adjustment and charging schemes, effectively improving the operation efficiency, energy utilization efficiency and service quality of the electric bus system. BRIEF DESCRIPTION OF DRAWINGS

[0126] The present application has the following drawings:

[0127] Figure 1 is an electric bus route schematic diagram;

[0128] Figure 2 is a rolling horizon method schematic diagram;

[0129] Figure 3 is a Beijing express bus line 3 schematic diagram. DETAILED DESCRIPTION

[0130] In order to make the purpose, advantages and characteristics of the present application more apparent, the present application will be further described in detail below in combination with the drawings and specific embodiments.

[0131] The application analyzes the coupling relationship of electric bus traffic dynamics, vehicle overtaking, passenger load, capacity limit and charging and discharging, and proposes a dynamic operation adjustment method of an electric bus system based on a fast charging facility, as shown in the following formula: Figure 1 The specific implementation steps are described in detail as follows:

[0132] Step 1: Taking the number of bus stops on the bus line, the distance between stops, the location of charging facilities, the charging power, the capacity of the bus battery and the like as the input of the control system; based on the dynamic adjustment model of the rolling optimization, the prediction time domain and the control time domain of the current decision stage are constructed based on the rolling time domain framework, the real-time information of the road environment, the bus and the passenger demand is monitored and collected as the input, including: environmental disturbance information, actual arrival and departure time, number of passengers on the bus, passenger arrival rate.

[0133] Step 2: Constructing the bus operation, passenger loading and power consumption constraints:

[0134] The bus operation constraints include: bus arrival and departure time, stop time, inter-station running time and overtaking constraints;

[0135] The passenger loading constraints include: waiting passengers, boarding passengers, alighting passengers, on-board passengers and stranded passengers constraints;

[0136] The power consumption constraints include: fast charging constraints and battery power constraints.

[0137] Step 3: Taking the minimum of the bus timetable deviation, the passenger waiting time, the power consumption and the adjustment measure cost as the objective function, and constructing the dynamic adjustment optimization model of the electric bus;

[0138] Step 4: For the dynamic operation adjustment method optimization model of the electric bus system based on the fast charging facility obtained in step 3, a spatial branch and bound method combined with acceleration techniques is designed to solve the current stage decision until a feasible and high-quality adjustment optimization strategy is obtained. The acceleration techniques include: boundary contraction and bilinear specific branch.

[0139] Step 5: According to the obtained optimization scheme, the electric bus adjustment and charging strategy in the control time domain of the current decision stage is fixed, and then the prediction time domain and the control time domain of the next decision stage are constructed in step 1, and the solution is performed until the operation ends. (As shown in the following formula: Figure 2

[0140] In this embodiment, the following necessary parameters and data need to be determined in advance:

[0141] (1) Electric bus line, including bus station sites and the like;

[0142] ​(2) Basic parameters of the bus, including the passenger capacity of the electric bus, the battery capacity, etc.

[0143] (3) Bus operation parameters, including the door opening and closing time, the minimum departure interval time, the limit value of the stop and operation adjustment, etc.

[0144] (4) Disturbance time parameters, including the station or section where the disturbance event occurs, the train stop or section operation delay time caused by the disturbance event, etc.

[0145] In the case where the above conditions are given, codes are written in Python or Matlab according to the mathematical model and the constraint conditions described in the disclosure content, the model framework proposed by the method is constructed, and the corresponding electric bus operation scheme is obtained.

[0146] As shown in a single line containing 22 stations, and given the following known conditions: Figure 3

[0147] (1) Electric bus line, considering a single electric bus line, as shown in Figure 2 The line has 22 bus stations, of which there are fast charging infrastructure at station 6, station 11 and station 16.

[0148] (2) The basic parameters of the electric bus, operation and disturbance are shown in Table 1.

[0149] Table 1

[0150] Parameter Value Unit Bus passenger capacity 150 People Efficiency parameter 0.9 - Air density 1.2 Kilogram / meter cubed Drag coefficient 0.29 - Front area 2.27 Square meters Friction coefficient 0.01 - Bus body weight 5000 Kilograms Average passenger mass 60 Kilograms Gravitational constant 9.81 Meters / squared seconds Road slope 0 - Power of accessory load 2 Kilowatts Power of auxiliary system 10 Kilowatts Initial station electric quantity 12 Kilowatt-hours Battery capacity 20 Kilowatt-hours Charging power of station 6 100 Kilowatts Charging power of station 11 100 Kilowatts Charging power of station 16 80 Kilowatts Minimum electric quantity to station 3 Kilowatt-hours Control stay time maximum value 30 Seconds Control operation time interval [-30,30] Seconds Operation and stay time disturbance interval [0，180] Seconds Departure deviation weight coefficient 0.1 - Departure interval weight coefficient 1 - Passenger waiting time weight coefficient 1 - Electric bus energy consumption weight coefficient 1.5·10 4 ]]> - Control measure weight coefficient 1 -

[0151] In order to illustrate the effectiveness of the electric bus system dynamic adjustment strategy proposed in the disclosure, the calculation results of the electric bus system dynamic adjustment strategy based on the spatial branch and bound method are compared with the rule-based station control strategy, and the following results are 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 3.16·10^6s, and the energy consumption is 1.26·10^2kW·h; under the bus adjustment strategy proposed in the disclosure, 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 application are all better than those of the rule-based bus adjustment strategy: the departure deviation and the schedule deviation are reduced by 61.54% and 69.43% respectively, the waiting time is reduced by 19.95%, and the 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 the present application not only supports the adjustment of bus running time, can actively recover delays, improve punctuality, reduce waiting time and energy consumption, but also can consider future disturbances and passenger flow changes in real time, so that the decision maker can dynamically adjust the strategy, thereby enhancing the robustness of the system.

[0152] More example solving results are shown in Table 2 (calculation results of the rule-based bus adjustment strategy) and Table 3 (calculation results of the dynamic operation adjustment of the electric bus system based on the fast charging facility).

[0153] Table 2

[0154] Stage Departure deviation Departure interval deviation Waiting time Energy consumption 1 2.25·10 4 ]]> 2.24·10 4 ]] 1.77·10 5 ]] 1.13·10 1 ]] 2 6.88·10 4 ]]> 5.24·10 4 ]]> 1.17·10 5 ]] 7.97·10 0 ]] 3 3.21 · 10 5 ]] 1.76·10 5 ]]> 2.44·10 5 ]] 1.33·10 1 ]]> 4 7.74·10 5 ]] 5.49·10 5 ]]> 3.56·10 5 ]]> 1.43·10 1 ]]> 5 2.64·10 6 ]]> 8.91 · 10 5 ]] 8.91 · 10 5 ]] 1.54·10 1 ]]> 6 1.28·10 6 ]]> 6.56·10 5 ]]> 3.26·10 5 ]]> 1.12·10 1 ]]> 7 5.61·10 6 ]]> 1.27·10 6 ]]> 3.18·10 5 ]]> 1.59·10 1 ]]> 8 3.81·10 6 ]]> 8.13·10 5 ]]> 3.05·10 5 ]]> 1.13·10 1 ]]> 9 9.08·10 6 ]]> 8.55·10 5 ]]> 4.54·10 5 ]]> 1.31·10 1 ]]> 10 7.94·10 6 ]]> 1.63·10 6 ]]> 4.78·10 5 ]]> 1.22·10 1 ]]> Total 3.16·10 7 ]]> 6.92·10 6 ]]> 3.16·10 6 ]]> 1.26·10 2 ]]>

[0155] Table 3

[0156] Stage Departure deviation Departure interval deviation Waiting time Energy consumption 1 2.93·10 4 ]]> 2.88·10 4 ]]> 1.78·10 5 ]]> 1.15·10 1 ]]> 2 6.78·10 4 ]]> 3.15·10 4 ]]> 1.16·10 5 ]]> 8.11 · 10 0 ]] 3 2.02·10 5 ]] 1.15·10 5 ]]> 2.33·10 5 ]]> 1.29·10 1 ]]> 4 3.74·10 5 ]]> 1.84·10 5 ]]> 3.00·10 5 ]]> 1.40·10 1 <!-- 12 -->]]> 5 1.18·10 6 ]]> 2.36·10 5 ]]> 3.08·10 5 ]]> 1.48·10 1 ]]> 6 5.07*10 6 ]]> 2.18·10 5 ]]> 2.45·10 5 ]]> 1.07·10 1 ]]> 7 2.28·10 6 ]]> 2.61·10 5 ]]> 2.53·10 5 ]]> 1.51·10 1 ]]> 8 1.52·10 6 ]]> 2.11 · 10 5 ]] 2.48·10 5 ]]> 1.05·10 1 ]]> 9 3.65·10 6 ]]> 4.83·10 5 ]]> 3.81·10 5 ]]> 1.18·10 1 ]]> 10 2.34·10 6 ]]> 3.47·10 5 ]]> 2.66·10 5 ]]> 1.07·10 1 ]]> Total 1.21·10 7 ]]> 2.12·10 6 ]]> 2.53·10 6 ]]> 1.20·10 2 ]]>

[0157] In the present example, the Gurobi solver and the spatial branch and bound algorithm proposed in the present application are used to solve the problem and the mathematical model. In order to meet the real-time requirement, the following solving termination conditions are set: (a) when the Gurobi solver finds a feasible solution with an optimality gap less than 3%, the solving is stopped; (b) when the solving time exceeds 180 seconds, the search process is terminated. The average calculation time of the spatial branch and bound algorithm proposed in the present application in each stage is 2.26 seconds, and the maximum calculation time is 4.07 seconds, which meets the real-time requirement. In addition, the average optimality gap is 2.44%, and the maximum optimality gap is 2.87%, which shows that the method can obtain high-quality solutions in a short time. In contrast, the average calculation 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, especially in the 3rd, 7th and 8th stages, the Gurobi takes more than 180 seconds and still cannot find a solution with the same or better objective function value as the spatial branch and bound method, which further demonstrates the effectiveness of the spatial branch and bound algorithm proposed in the present application. More example solving results are shown in Table 4.

[0158] Table 4

[0159] Performance index Objective function value Optimality gap of space branch and bound method Space branch and bound method running time Gurobi running time Stage 1 1.72·10 6 ]]> 2.17% 2.22 10.11 Stage 2 2.05·10 6 ]]> 2.78% 2.15 57.96 Stage 3 2.39·10 6 ]]> 2.49% 3.84 180+ Stage 4 3.06·10 6 ]]> 2.67% 4.07 64.12 Stage 5 3.59·10 6 ]] 2.18% 2.61 30.18 Stage 6 3.04·10 6 ]]> 2.47% 1.43 31.04 Stage 7 4.27*10 6 ]] 2.10% 2.27 180+ Stage 8 4.44·10 6 ]]> 2.66% 0.82 180+ Stage 9 4.94·10 6 ]]> 1.98% 2.08 178.93 Stage 10 3.95·10 6 ]]> 2.87% 1.12 90.79

[0160] In conclusion, the application proposes a dynamic operation adjustment method for electric bus system based on fast charging facilities based on the spatial branch and bound method, which can provide fast and effective adjustment and charging scheme for electric bus operators under interference, and help to improve the operation efficiency of electric bus, passenger satisfaction and reduce energy consumption.

[0161] It should be noted that any process or method descriptions in the embodiments can be understood as representing modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions or steps, and that the scope of preferred embodiments of the application includes alternatives where the functions can be performed out of the same order as shown or discussed, including substantially concurrently or in reverse order, as appropriate, which will be appreciated by those skilled in the art of the embodiments to which this application pertains.

[0162] It should be noted that for the logic and / or steps of embodiments, such as can be conceived to be a sequenced list of executable instructions for implementing logic functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination of both. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical apparatus), a portable computer diskette (magnetic apparatus), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical apparatus), and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, by optically scanning the paper or other suitable medium, then electronically converted into a form that is suitable for use in a computer storage medium.

[0163] It should be understood that each part of the present application can be realized by hardware, software, firmware or their combination. In the above-mentioned embodiments, a plurality of steps or methods can be realized by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if realized by hardware, it can be realized by any one or their combination of the following technologies known in the art: discrete logic circuit with logic gate circuit for implementing logic function on data signal, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA) and the like.

[0164] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiment methods can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable storage medium, and when executed, include one or a combination of steps of the method embodiments.

[0165] In addition, each functional module in the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above-mentioned integrated module can be realized in the form of hardware or in the form of software functional module. The integrated module, if realized in the form of software functional module and sold or used as an independent product, can also be stored in a computer readable storage medium.

[0166] The above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disk and the like.

[0167] The above-mentioned embodiments have described the technical solutions of the present application in detail. Apparently, the present application is not limited to the described embodiments. Based on the embodiments in the present application, those skilled in the art can make various changes, but any change equivalent or similar to the present application belongs to the protection scope of the present application. The contents not described in detail in the specification belong to the prior art known by those skilled in the art.

Claims

1. A method for dynamic operation adjustment of an electric bus system based on fast charging facilities, characterized in that, The method comprises the following steps: acquiring bus line operation data; based on a rolling optimization dynamic adjustment model and a rolling time domain framework, constructing a prediction time domain and a control time domain of a current decision stage, monitoring and collecting real-time information of road environment, bus vehicles and passenger demand as input; constructing bus operation, passenger loading and power consumption constraints; constructing an electric bus dynamic adjustment optimization model with a minimum target function of bus schedule deviation, passenger waiting time, power consumption and adjustment measure cost; solving the current stage decision by combining a spatial branch and bound method with acceleration technology until a feasible and high-quality adjustment optimization strategy is obtained; the acceleration technology comprises boundary contraction and bilinear specific branch; fixing the electric bus adjustment and charging strategy in the control time domain of the current decision stage, then constructing a prediction time domain and a control time domain of the next decision stage and solving until the operation ends; the spatial branch and bound method comprises: step 1: simplifying the dynamic adjustment optimization mathematical model of the electric bus system with fast charging infrastructure into model M1, linearizing the passenger constraints, then reconstructing the mixed integer nonlinear programming model M1 into a mixed integer bilinear programming model M2, and solving the model by using a spatial branch and bound algorithm; step 2: relaxing the bilinear term by using McCormick inequality for passenger waiting time, so as to relax the original mixed integer bilinear programming model into a mixed integer convex quadratic programming model, and obtaining a lower bound solution of the original problem; step 3: identifying and selecting the relaxed problem and bilinear variables that need to be branched according to the bilinear branch strategy; specifically, calculating the error of each bilinear term in the optimal solution of the relaxed problem with the minimum lower bound, selecting the bilinear variables with error exceeding a predetermined tolerance as branch candidate variables, and selecting the first n variables with the maximum error as branch variables for the relaxed problem in descending order of error; step 4: for each selected candidate bilinear variable, determining a split threshold and generating a child node; the specific steps comprise: determining a reasonable threshold for each candidate variable to divide its continuous variable domain into two new intervals, and generating two new child nodes based on the split threshold, each corresponding to a new variable domain; step 5: solving the generated child nodes in parallel and calculating the lower bound solution, and recalculating the target function value according to the current bound to obtain the upper bound; step 6: when the stop condition is met, terminating the spatial branch and bound algorithm and outputting the result; the specific steps comprise: checking whether the predetermined maximum branch time limit is reached or the predetermined upper and lower bound gap is reached; if the stop condition is met, outputting the optimal adjustment scheme of the current decision stage and the corresponding upper bound value; otherwise, continuing the branch operation.

2. The method of claim 1, wherein, the bus operation constraints comprise bus arrival and departure time, stop time, inter-station running time and overtaking constraints; the passenger loading constraints comprise waiting passengers, boarding passengers, alighting passengers, on-board passengers and stranded passengers; the power consumption constraints comprise fast charging constraints and battery power constraints.

3. The method of claim 1, wherein, The dynamic adjustment model based on rolling optimization and the rolling time domain framework include: Modeling the topology of the electric bus line: 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 set of nodes and the direction of bus operation, constructing bus operation arcs: the electric bus departs from the start station node, arrives at and stops at the intermediate station node to pick up and drop off passengers, and charges at the station node equipped with fast charging infrastructure, each operation arc corresponds to a different discharging process; Based on the given topology of the electric bus line, the complete electric bus operation adjustment time domain is divided into multiple decision stages, and a rolling time domain decision framework is constructed; each decision stage involves a prediction time domain and a control time domain; according to the bus vehicle operation, passenger demand and interference information, the bus adjustment strategy in the future for a long period of time, i.e. the prediction time domain, is formulated and solved; the adjustment strategy in the future for a short period of time, i.e. the control time domain, is issued to the bus vehicle for execution.

4. The method of claim 2, wherein, The bus operation constraints include: Electric bus arrival time constraint: the arrival time of the 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): ； wherein: index represents a bus stop; index represents an electric bus; set, represents a bus stop ; set represents the set of bus vehicles passing by the bus stop at the decision stage; variable represents an electric bus at a bus stop ; variable represents an electric bus at a bus stop ; variable represents an electric bus at a bus stop ; variable represents the actual inter-station travel time of an electric bus between the bus stop Electric bus running time constraint: the running time of the electric bus between stations is the sum of the planned running time between stations and the adjustment time and interference time, as shown in formula (2): ； where: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles passing by the bus stop at the th decision stage; variable represents an electric bus at a bus stop ; variable represents the actual travel time between bus stop and bus stop ; variable represents the planned travel time between bus stop and bus stop ; variable represents the travel adjustment time between bus stop and bus stop ; parameter represents the disturbance time of an electric bus traveling between bus stop and bus stop ; Electric bus dwell time constraint: the dwell time of the electric bus at the station is the sum of the opening and closing door time of the bus at the station and the time of passengers getting on and off the bus and the interference time, as shown in formula (3): ； where: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles passing by the bus stop at the th decision stage; variable represents an electric bus at the bus stop ; parameter 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 an electric bus arriving at the bus stop ; variable represents an electric bus arriving at the bus stop ; parameter represents an electric bus at the bus stop ; Electric bus departure time constraint: the departure time of the electric bus at each station is the sum of the arrival time at the station and the dwell time and adjustment time, as shown in formula (4): ； Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Represents electric buses At the bus stop Actual departure time; variables Represents electric buses At the bus stop Actual arrival time; variables Represents electric buses At the bus stop Actual stay time; variables Represents electric buses At the bus stop Adjustment timeframe; Electric bus headway constraint: the headway of the electric bus at each station is the difference between the departure time of the bus at the station and the departure time of the immediately preceding bus, as shown in formula (5): ； where: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles that pass by the bus stop at the th decision stage; variable represents an electric bus at a bus stop ; variable represents the headway of the preceding bus at a bus stop; variable represents an electric bus at a bus stop ; variable represents the departure time of the preceding bus at a bus stop ; since overtaking can occur, the bus is not necessarily equal to ; Electric bus overtaking constraint: the departure order of the bus is represented by introducing a binary variable to determine whether the electric bus overtakes, as shown in formulas (6)-(11): ; ； ； ； ； ； Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Represents electric buses At the bus stop Actual departure time; variables Represents electric buses The car in front of me is at the bus stop. Departure time; if at the bus stop electric buses The departure time is later than that of public buses. Then the variable ,otherwise, If the bus It's a bus. exist If the car in front is the one immediately in front, then the variable... ,otherwise, ;parameter It is a large positive number; Electric bus operation constraint: the adjustment time of the electric bus staying at the station and running between stations must be within the predetermined maximum and minimum limits, and the headway should be greater than the predetermined minimum limit; as shown in formulas (12)-(14): ； ； ； where: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles passing the bus stop at the th decision stage; variable represents an electric bus at a bus stop ; variable represents the adjustment time taken by an electric bus at a bus stop ; parameter represents the running adjustment time between an electric bus at a bus stop ; parameter represents the maximum adjustment time that an electric bus can take when stopping at a bus stop ; variable represents the minimum and maximum adjustment times that an electric bus can take when running between and a bus stop ; variable represents the departure time of the bus immediately preceding an electric bus at a bus stop ; parameter represents the minimum departure interval between electric buses at a bus stop .

5. The method of claim 4, wherein, The passenger loading constraints include: Waiting passenger constraint: the number of waiting passengers at the bus station includes newly arrived passengers and stranded passengers from the previous bus, as shown in formula (15): ； wherein: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles passing by the bus stop at the th decision stage; variable represents the number of waiting passengers at the bus stop waiting for an electric bus ; parameter represents the arrival rate of passengers at the bus stop waiting for an electric bus ; variable represents the headway of an electric bus and a bus at the bus stop ; variable represents the number of passengers at the bus stop immediately before the preceding vehicle of an electric bus ; Electric bus stranded passenger constraint: the number of passengers stranded by the electric bus is equal to the number of waiting passengers minus the number of boarding passengers, as shown in formula (16): ； wherein: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles that pass by the bus stop at the th decision stage; variable represents the number of passengers that are stranded at the bus stop by the electric bus ; variable represents the number of waiting passengers at the bus stop for the electric bus ; variable represents the number of passengers that board the electric bus at the bus stop ; Passenger constraint stranded by the immediately preceding bus as shown in formula (17): ； where: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles that pass through the bus stop at the th decision stage; variable represents the number of passengers that are stranded at the bus stop by the electric bus that immediately precedes it; if the bus is the bus at the th decision stage, then the variable , otherwise, ; variable represents the number of passengers that are stranded at the bus stop by the electric bus that immediately precedes it; Boarding passenger constraint: the number of boarding passengers is the minimum of the number of waiting passengers and the remaining capacity of the bus, as shown in equation (18): ; Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A set of public transport vehicles; variables Representative at the bus stop Boarding the electric bus Number of passengers; variables Representative at the bus stop Waiting for the electric bus The number of waiting passengers; Represents electric buses Capacity; Variable Represents electric buses Arrive at the bus station The number of passengers disembarking at that time; variables Represents electric buses At the bus stop The number of passengers on board at the station; On-board passenger constraint: the number of on-board passengers of the electric bus involves the number of on-board passengers at the previous station plus the number of boarding passengers minus the number of alighting passengers; as shown in formula (19): ； where: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles passing by the bus stop at the th decision stage; variable represents the set of bus vehicles passing by the bus stop at the th decision stage; variable represents the number of on-board passengers of an electric bus at a bus stop ; variable represents the number of on-board passengers of an electric bus at a bus stop ; variable represents the number of on-board passengers of an electric bus at a bus stop ; variable represents the number of on-board passengers of an electric bus at a bus stop Off-board passenger constraint: the off-board passenger number of the electric bus at the bus stop is linearly related to the on-board passenger number at the previous bus stop and the off-board rate, as shown in equation (20): ； where: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles passing by the bus stop at the th decision stage; variable represents the set of bus vehicles passing by the bus stop at the th decision stage; variable represents the number of passengers getting off an electric bus at a bus stop; variable represents an electric bus at a bus stop ; parameter represents an electric bus at a bus stop ; parameter 6. The method of claim 5, wherein, The power consumption constraint includes: Electric bus energy consumption constraint: the energy consumption of the electric bus is related to the changes in air resistance, gravitational potential energy, and rolling resistance power consumption, as well as auxiliary equipment power consumption, as shown in equation (21): ； Where: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles passing through the bus stop at the th decision stage; variable represents an electric bus at a bus stop ; parameter represents the energy consumption of the electric bus at a bus stop and during the interval operation; parameter represents the distance between the bus stop and ; parameter represents the efficiency coefficient; parameter represents the air density; parameter represents the air resistance coefficient; parameter represents the frontal area of the electric bus; parameter represents the friction coefficient of the electric bus; parameter represents the slope of the road on which the electric bus is driving; parameter and respectively represent the body weight of the electric bus when empty and the average weight of the passengers; parameter represents the acceleration due to gravity; parameter and respectively represent the power of the electric bus running accessories and auxiliary systems; variable represents the actual running time of the electric bus between the bus stop and the bus stop ; variable represents the number of on-board passengers of the electric bus at the bus stop ; parameter represents the disturbance time of the electric bus when stopping at the bus stop ; variable represents the actual dwell time of the electric bus at the bus stop ; variable represents the adjustment time taken by the electric bus at the bus stop ; The energy consumption of the electric bus is nonlinearly related to the running speed, as shown in equation (21). The piecewise linearization method is used to approximate the energy consumption of the electric bus, as shown in equations (22)-(23): ； ； wherein: represents an approximation function; represents a breakpoint dividing the interval represents a slope of a straight line segment connecting and ​​​ Linearize equation (23) to equations (24)-(26): ； ； ； wherein: variable represents an electric bus at a bus stop with the bus stop between the bus stop and the bus stop, if is greater than then ; otherwise, ; Electric bus charging amount constraint: The charging amount of the electric bus should take the minimum value of the remaining battery amount and the charging amount at the station, as shown in equation (27): ; where: index represents a bus stop; index represents an electric bus; set represents the set of bus stops in the decision stage; set represents the set of bus vehicles passing by the bus stops in the decision stage; set represents the set of electric buses with fast charging facilities; variable represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter represents the charging power of an electric bus at a bus stop; parameter Electric bus power constraint: the current power of the electric bus is calculated as shown in equation (28): ； where: index represents a bus stop; index represents an electric bus; set represents the set of bus vehicles that pass through the bus stop at the th decision stage; variable represents an electric bus at the next bus stop among all bus stops with fast charging facilities; variable represents an electric bus at the bus stop ; variable represents an electric bus at the bus stop ; variable represents the energy consumption of the bus stop parking and the inter-vehicle operation; variable represents an electric bus at the bus stop ; Electric bus power constraint at each bus stop: the power of the electric bus at each bus stop should be greater than the minimum power, as shown in equation (29): ； where: index represents a bus stop; index represents an electric bus; set represents the set of bus vehicles that pass through the bus stop at the decision stage; variable represents the set of electric buses that pass through the bus stop at the decision stage; variable represents the electric power at the bus stop; parameter represents the minimum electric power at the bus stop for the electric bus; parameter represents the minimum electric power at the bus stop for the electric bus; parameter represents the minimum electric power at the bus stop for the electric bus; parameter 7. The method of claim 1, wherein, The objective function includes: Electric bus departure time deviation penalty, as shown in equation (30): ； Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A collection of public transport vehicles; parameters Weighting coefficients representing the deviation in departure time of electric buses; variables Represents electric buses At the bus stop Actual departure time; parameters Represents electric buses At the bus stop The scheduled departure time; Electric bus departure interval deviation penalty, as shown in equation (31): ； wherein: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles passing by the bus stop at the th decision stage; parameter represents the set of bus vehicles passing by the bus stop at the th decision stage; parameter represents the weight coefficient of the deviation of the electric bus headway; variable represents the actual departure time of the electric bus at the bus stop; variable represents the actual departure time of the electric bus at the bus stop; variable represents the departure time of the immediately preceding bus at the bus stop; parameter represents the departure time of the immediately preceding bus at the bus stop; parameter represents the departure time of the immediately preceding bus at the bus stop; parameter represents the departure time of the immediately preceding bus at the bus stop; parameter represents the departure time of the immediately preceding bus at the bus stop; parameter represents the departure time of the immediately preceding bus at the bus stop; parameter Electric bus passenger waiting time penalty, as shown in equation (32): ； Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A collection of public transport vehicles; parameters Weighting coefficients representing passenger waiting time at the station; parameters\ Representing passengers at the bus stop Waiting for the electric bus Arrival rate; variable Represents electric buses With bus At the bus stop Departure interval; variable This represents the next step after electric buses The bus ahead is at the bus stop. The number of stranded passengers; Electric bus energy consumption penalty, as shown in equation (33): ； Where: index Represents bus stops; index Represents electric buses; collection Represents bus station A set; a set Representative at the Each decision-making stage, passing through stations A collection of public transport vehicles; parameters Weighting coefficients representing the energy consumption of electric buses; variables Represents electric buses At the bus stop Energy consumption for parking and inter-section operation; Electric bus adjustment cost penalty, as shown in equation (34): ； wherein: index represents a bus stop; index represents an electric bus; set represents a bus stop ; set represents the set of bus vehicles passing through the bus stop at the th decision stage; parameter represents the set of bus vehicles passing through the bus stop at the th decision stage; parameter represents the weight coefficient for taking adjustment measures on the operation of the electric bus; variable represents the adjustment time taken by the electric bus at the bus stop; variable represents the adjustment time taken by the electric bus between the bus stop and the bus stop .

8. A dynamic operation adjustment device for a fast-charging facility-based electric bus system, characterized by, It includes: First module: for obtaining 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 stage are constructed, and the real-time information of road environment, bus and passenger demand is monitored and collected as input; Second module, for constructing bus operation, passenger loading and power consumption constraint conditions; Third module, for constructing the electric bus dynamic adjustment optimization model with the minimum bus timetable deviation, passenger waiting time, power consumption and adjustment measure cost as the objective function; Fourth module, for solving 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 acceleration technology includes: boundary contraction and bilinear specific branch; Fifth module, for fixing the electric bus adjustment and charging strategy in the control time domain of the current decision stage, and controlling the implementation to construct the prediction time domain and control time domain of the next decision stage, and solving until the end of operation; The spatial branch and bound method includes: Step 1: the dynamic adjustment optimization mathematical model of the electric bus system with fast charging infrastructure is written as model M1, and the passenger constraint is linearized, then the mixed integer nonlinear programming model M1 can be reconstructed as a mixed integer bilinear programming model M2, which is solved by the spatial branch and bound algorithm; Step 2: for passenger waiting time, McCormick inequality is used to relax the bilinear term, so that the original mixed integer bilinear programming model is relaxed into 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 including: calculating the error of each bilinear term in the optimal solution of the lower bound minimum relaxation problem; Selecting the bilinear variables whose error exceeds the predetermined tolerance as the branching candidate variables; Sort these candidate variables in descending order according to the error size, and select the first n variables with the largest error as the branching variables for the relaxation problem; Step 4: For each selected candidate bilinear variable, determine the split threshold and generate child nodes; The specific steps include: determining a reasonable threshold for each candidate variable, and dividing its continuous variable domain into two new intervals; Based on the split threshold, generate two new child nodes, each corresponding to a new variable domain; Step 5: Solve the generated child nodes in parallel and calculate their lower bound solutions, and calculate the objective function value according to the current bound to obtain the upper bound; Step 6: When the stop condition is met, terminate the space branch and bound algorithm and output the result; The specific steps include: checking whether the predetermined maximum branching time limit is reached or the predetermined upper and lower bound gap is reached; If the stop condition is met, output the optimal adjustment scheme of the current decision stage and the corresponding upper bound value; Otherwise, continue the branching operation.

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