CN111915150B - A planning method for electric bus system - Google Patents

Abstract

The invention relates to a planning method for an electric public transport system, comprising the following steps: acquiring public transport network information and cost information; using the public transport network information and cost information to establish an electric public transport network operation scheduling with the goal of minimizing the total cost of the electric public transport system and the optimization model of charging facility layout; using the adaptive genetic algorithm of continuously changing crossover probability and continuously changing variation probability to solve the optimization model of electric bus network operation scheduling and charging facility layout, and obtain the optimal model involving electric bus network operation scheduling and electric bus charging facility layout electric bus system. Compared with the existing technology, the electric bus operation scheduling plan and the charging facility layout plan are optimized at the same time, which can be applied to the time-of-use electricity price, improve the utilization rate of electric bus vehicles, and the charging scheduling of electric buses is more flexible, which can realize off-peak charging. Reduce electricity costs and increase the utilization of electric buses and charging facilities.

Description

A planning method for electric bus system

technical field

The invention relates to the field of public transport systems, in particular to an electric public transport system planning method.

Background technique

The bus system is one of the ideal application scenarios for pure electric vehicles due to its fixed routes and timetables. Electric buses have the characteristics of low noise, zero emissions, and high comfort. They are generally considered to replace traditional diesel buses and are the future development direction of the public transportation system.

However, due to the limitations of battery technology, electric buses have limited battery life and long charging time. The operation and scheduling of electric buses need to consider not only the scheduling of vehicles, but also the scheduling of charging vehicles. In addition, the layout of charging facilities is also a key problem in the electrification of public transport, and the layout of charging facilities and the operation and scheduling of electric buses affect each other. Therefore, optimizing these two aspects at the same time is a key technical difficulty in electrification of public transport.

There have been separate studies on electric bus operation scheduling and charging facility layout. In terms of electric bus operation scheduling, Chinese patents CN 107341563, CN 104615850 and CN 109636176 have studied the electric bus charging sequence plan of a single charging station, and patent CN 109934391 is based on the vehicle scheduling problem (Vehicle SchedulingProblem, VSP), and the number of vehicles is reduced by a heuristic algorithm. The number of usage, the patent CN 109615268 further considers the time-of-use electricity price scenario, and establishes a vehicle operation scheduling model with the goal of minimizing the charging cost of the vehicle on the day. On the other hand, patents such as CN110705745 and CN 107392360 provide experience and reference for modeling and optimizing the layout of electric bus charging facilities from the aspects of the location and capacity of the charging station and the setting of the number of charging facilities.

However, the research on the synchronous optimization technology of electric bus operation scheduling and charging facility layout is still lacking. Moreover, the current research on electric bus scheduling is mostly based on the single-line scenario of a single-park, and lacks consideration of the multi-line scenario with multiple yards. In addition, the current research mostly uses the default charging method of charging and charging, and lacks modeling optimization for more flexible partial charging methods.

Contents of the invention

The object of the present invention is to provide an electric bus system planning method in order to overcome the above-mentioned defects in the prior art.

The purpose of the present invention can be achieved through the following technical solutions:

A method for planning an electric public transport system, the method comprising the following steps:

Step S1: Obtain bus network information and cost information. The bus network information includes bus line site information, inter-stop idling time information, and line operation timetable information. The bus line site information includes each item in the network. The number of parking spaces, supporting facilities and power grid conditions at the first and last stations of the line;

Step S2: Using the bus network information and cost information, establish an electric bus network operation scheduling and charging facility layout optimization model with the goal of minimizing the total cost of the electric bus system;

Step S3: Use the adaptive genetic algorithm of continuously changing crossover probability and continuously changing mutation probability to solve the optimization model of electric bus network operation scheduling and charging facility layout, and obtain the electric bus system involving electric bus network operation scheduling and electric bus charging facility layout .

The fee information includes an electricity price charging standard, and the electricity price charging standard includes a single electricity price charging standard and a time-of-use electricity charging standard.

The objective function of the electric bus line network operation scheduling and charging facility layout optimization model is:

Among them, Z is the total cost of the electric bus system, which is composed of four parts: electric bus purchase cost, electric bus charging facility purchase cost, electricity cost, and electric bus operation cost; c _b is the purchase cost of a single electric bus, and c _c is the charging cost Single purchase cost of facility, c _e is the electricity price, c _t is the operating cost of electric bus per hour, e _i is the electricity consumption of shift i, t _i is the operation time of shift i, E _ij is the start time of shift j and the end time of shift i Compared with the change in battery power, T _ij is the time required for the vehicle to arrive at the starting point of shift j from the end of shift i; x _ij takes 1 when the electric bus operates shift j after shift i ends, and takes 0 otherwise; y _pq is 1 when the charging pile executes the charging event q after the charging event p is completed, otherwise it is 0.

The constraints of the electric bus line network operation scheduling and charging facility layout optimization model include:

Vehicle scheduling constraints:

Power Consumption Constraints:

Charging schedule constraints:

The calculation formulas of E _ij , T _ij and l _p are:

Among them, i, j are the number of the shift or the first and last station, S is the collection of all bus trips, D is the collection of all the first and last stations, U is the union of S and D, p, q are the charging event or the first and last station P is the collection of all charging events, Q is the union of P and D; a _i is the start time of shift i, l _i is the remaining power of the vehicle after the end of shift i; SOC _max and SOC _min are preset The upper and lower limits of battery power; z _ip takes 1 when the charging event p is performed after the electric bus operation shift i ends, otherwise it takes 0, z _pj takes 1 when the electric bus operates the shift j after the charging event p, otherwise it takes 0 _; a _p is the start time of the charging event p, t _p is the charging time of the charging event p, l _p is the remaining power of the electric bus before the charging event p starts; The electricity consumed by the charging station where the event p is located, e _pj is the electricity consumed by the electric bus driving to the starting point of the shift j after the charging event p is over, and t _ip is the electric bus traveling to the location of the charging event p after the operating shift i is over The time consumed by the charging station, t _pj is the time consumed by the electric bus to travel to the starting station of shift j after the charging event p is completed; t _ij is the time consumed by the electric bus traveling from the terminal station of shift i to the starting station of shift j time, e _ij is the power consumed by the electric bus traveling from the terminal station of flight i to the starting station of flight j; M is a sufficiently large number; F(l _p ,t _p ) is the charging function, with l _p and t _p is the independent variable, and the dependent variable is the remaining power of the electric bus after the charging event p ends.

The continuously changing crossover probability of the adaptive genetic algorithm is:

The continuous change mutation probability of adaptive genetic algorithm is:

Among them, P _c1 , P _c2 , P _m1 , and P _m2 are constants greater than 0 and less than 1, g is the current genetic algebra, G is the maximum genetic algebra, f′ is the fitness of the individual before the crossover operation, and f is the mutation operation The fitness of the former individual, is the average fitness of all individuals in the population, f _max is the maximum fitness of all individuals in the population, k _c is the change rate of adaptive crossover probability with the change of genetic algebra, k _m is the change rate of adaptive mutation probability with the change of genetic algebra .

The adaptive genetic algorithm is based on a feasible solution transformation method, that is, the crossover and mutation operations of the genetic algorithm are all within the range of feasible solutions.

The adaptive genetic algorithm adopts an integer coding form, the number of chromosome genes is twice the number of shifts in a day of the electric bus system, the order of odd-numbered genes from left to right represents the order of shifts from morning to night, and the number of odd-numbered genes in chromosomes The number b (b≥1) represents the number of the vehicle operating the shift, and the even-numbered digit c (c≤0) represents the number of the charging station. When c=0, it means that the shift has not been charged at the charging station after the end of the shift. When c<0 The time representative goes to the charging station numbered c to charge after the end of the shift.

The adaptive genetic algorithm fitness function is calibrated by the following formula:

Among them, TC _reci is the reciprocal of the total cost of the electric bus system calculated by chromosome individual decoding, TC _{reci_min} is the minimum value of all individual TC _reci in the population, TC _{reci_max} is the maximum value of all individual TC _reci in the population, and r is a small positive constant of .

The operation scheduling of the electric bus network includes the size of the electric bus fleet, vehicle scheduling and vehicle charging scheduling, the vehicle scheduling is multi-depot multi-line scheduling, and vehicle charging scheduling includes daytime charging scheduling and nighttime charging , and the charging method includes partial charging.

The layout of the electric bus charging facilities includes the location layout of the electric bus charging piles and the quantity layout of each location.

Compared with the prior art, the present invention has the following advantages:

(1) Solving the optimization model of electric bus network operation scheduling and charging facility layout can simultaneously optimize the electric bus schedule scheduling plan, charging scheduling plan and charging facility layout plan, making the planning of the electric bus system closer to the actual situation.

(2) It is not only applicable to a single electricity price, but also applicable to electricity price charging scenarios such as time-of-use electricity price, so that the planning of the electric bus system is closer to the actual situation.

(3) Modeling from the line network level can carry out electric bus scheduling with multiple depots and multiple lines.

(4) Consider the partial charging method that is not necessarily fully charged. Compared with the charging method that is fully charged, the charging scheduling of electric buses is more flexible, which can realize off-peak charging, reduce electricity costs, and improve the utilization of electric buses and charging facilities. Rate.

(5) Applying the improved adaptive genetic algorithm based on feasible solution transformation can avoid premature genetic algorithm and improve the optimization efficiency of genetic algorithm.

Description of drawings

Fig. 1 is a flowchart of the present invention;

Fig. 2 is the adaptive genetic algorithm flowchart of the present invention;

Fig. 3 is the schematic diagram of chromosome coding of adaptive genetic algorithm of the present invention;

Fig. 4 is the bus network figure of the embodiment of the present invention;

Fig. 5 is the time-varying diagram of the charging standard of electricity price in the embodiment of the present invention;

Fig. 6 is a graph showing the variation of the remaining battery power of the electric bus fleet according to the embodiment of the present invention;

Fig. 7 is a graph showing the change of the remaining power of an electric bus battery according to an embodiment of the present invention;

Fig. 8 is a graph showing the variation of charging power consumption in each hour of the electric bus system according to the embodiment of the present invention.

Detailed ways

The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

Example

This embodiment provides a method for planning an electric public transport system, as shown in Figure 1, comprising the following steps:

The first step is to collect bus network and operation data of each line in the network. Collect bus line site information, empty driving time between sites, and line operation timetable information by consulting the bus company or consulting relevant websites. Among them, the site information of the bus line mainly includes the number of parking spaces, supporting facilities, and power grid conditions at the first and last stations of each line in the line network, and then evaluates and screens the sites that can be equipped with charging facilities. The information on the idling time between stations can be obtained through field tests or through the online map API by using the number of crawlers, and is used for the calculation of vehicle scheduling or charging idling scheduling. The line operation timetable information includes the departure station and time, the end station and time of all shifts on the line.

The second step is the collection of information on the construction and operation and maintenance costs of the electric bus system, including the purchase and operation and maintenance costs of the proposed electric bus vehicles, the purchase and operation and maintenance costs of charging facilities, the electricity price charging standard, and the driver's wages and other operation-related labor cost information.

The third step is to calibrate the charging and discharging characteristics of the electric bus battery. The battery charging and discharging characteristics of electric bus vehicles can be specified through field operation tests or consultation with vehicle suppliers.

The fourth step is to build an optimization model for electric bus operation scheduling and charging facility layout. The model considers the constraints of vehicle scheduling, power consumption, and charging scheduling of electric buses, and builds a model with the optimization goal of minimizing the total cost of electric bus system construction and operation and maintenance.

The objective function of the model is as follows:

Among them, Z is the total cost of the electric bus system, which is composed of four parts: electric bus purchase cost, electric bus charging facility purchase cost, electricity cost, and electric bus operation cost; c _b is the purchase cost of a single electric bus, and c _c is the charging cost Single purchase cost of facility, c _e is the electricity price, c _t is the operating cost of electric bus per hour, e _i is the electricity consumption of shift i, t _i is the operation time of shift i, E _ij is the start time of shift j and the end time of shift i Compared with the change in battery power, T _ij is the time required for the vehicle to arrive at the starting point of shift j from the end of shift i; x _ij takes 1 when the electric bus operates shift j after shift i ends, and takes 0 otherwise; y _pq is 1 when the charging pile executes the charging event q after the charging event p is completed, otherwise it is 0.

The model includes the following constraints:

Vehicle scheduling constraints:

Among them, constraint (2) means that there is only one electric bus operating in each shift. Constraint (3) states that the electric bus will run the next shift after the current operating shift ends. Constraint (4) means that the operating hours of the two shifts operated by the same electric bus do not overlap. Constraint (5) defines the value range of the decision variable x _ij .

Power Consumption Constraints:

Among them, constraint (6) restricts the remaining battery power of electric bus vehicles to always be between the upper limit SOC _max and the lower limit SOC _min . Constraint (7) Initialize the remaining battery power of the electric bus before the first shift of the day to be SOC _max . Constraint (8) represents the relationship between the remaining power of the electric bus battery at the end of shift j and the remaining power at the end of shift i.

Charging schedule constraints:

Among them, constraint (9) indicates that the same charging pile will carry out the next charging event after the current charging event ends. Constraint (10) means that before the charging event starts, there is only one electric bus that comes to the charging station for charging after the operation ends. Constraint (11) states that each charging pile can charge at most one electric bus at any time. Constraint (12) states that the electric bus will operate the next shift or return to the depot after the end of the day's operation after charging. Constraint (13) means that the two charging events before and after the same charging pile do not overlap in time. Constraint (14) states that the charging time of an electric bus does not overlap with the subsequent bus time. Constraints (15) to (19) define the value range of each decision variable.

Intermediate variable calculation:

Among them, constraints (20) to (22) define the calculation formulas of the intermediate variables E _ij , T _ij and l _p .

The variables and parameter descriptions in the model are shown in Table 1. This embodiment mainly builds a model for commuting passenger demand in the morning rush hour.

Table 1 Description of model parameters

The fifth step is to improve the adaptive genetic algorithm to solve the model. The flow chart of the improved adaptive genetic algorithm is shown in Figure 2, which has the following four characteristics:

(1) The improved adaptive genetic algorithm is based on the feasible solution transformation method, that is, the crossover and mutation operations of the genetic algorithm are all within the range of feasible solutions, and no infeasible solutions will be generated.

(2) The improved adaptive genetic algorithm adopts the form of integer coding, and the schematic diagram of chromosome coding is shown in Fig. 3 . The number of chromosomal gene bits is twice the number of daily shifts of the electric bus system. The order of odd-numbered gene bits from left to right represents the order of shifts from morning to night. The odd-numbered gene bit number b (b≥1) of the chromosome represents the operation of the shift The vehicle number of the vehicle, the even-numbered digit c (c≤0) represents the charging station number, when c=0, it means that the charging station has not been charged after the end of the shift, and when c<0, it means that the number of the charging station is c after the end of the shift charging station.

(3) _The crossover probability P _c and mutation probability P _m of the improved adaptive genetic algorithm are _variable , which are related to the fitness of the population individual and the genetic algebra. The larger g(g∈[0,G]), the smaller _Pc and the larger _Pm , which can avoid premature algorithm and improve the optimization performance. The calculation formulas of adaptive crossover probability and adaptive mutation probability are shown in formulas (23) to (24).

Among them, P _c1 , P _c2 , P _m1 , and P _m2 are constants greater than 0 and less than 1, g is the current genetic algebra, G is the maximum genetic algebra, f′ is the fitness of the individual before the crossover operation, and f is the mutation operation The fitness of the former individual, is the average fitness of all individuals in the population, f _max is the maximum fitness of all individuals in the population, k _c is the change rate of adaptive crossover probability with the change of genetic algebra, k _m is the change rate of adaptive mutation probability with the change of genetic algebra . The improved self-adaptive genetic algorithm can avoid premature algorithm and improve the optimization efficiency of the algorithm.

(4) In order to improve the selection efficiency of the genetic algorithm, the fitness function of the improved adaptive genetic algorithm is calibrated by formula (25):

Among them, TC _reci is the reciprocal of the total cost of the electric bus system calculated by chromosome individual decoding, TC _{reci_min} is the minimum value of all individual TC _reci in the population, TC _{reci_max} is the maximum value of all individual TC _reci in the population, and r is a small positive constant of . The fitness function calibration method can improve the selection efficiency of genetic algorithm.

The following is a specific example:

This example takes 8 bus lines in Anting Town, Jiading District, Shanghai (the route diagram is shown in Figure 4) as the research scenario, and optimizes the replacement of diesel buses on 8 bus lines with plug-in conventional pure electric buses. total cost required. In the example, there are 867 bus trips in one day. The bus schedule is obtained from the official website of Jiading Public Transport, and the site conditions of all the first and last stations in the route are judged based on the actual map. The trip overview of each line is shown in Table 2, involving 12 starting points Terminal stations, 5 of which are bus yards and charging facilities are conditionally arranged (in this example, only charging stations are set up in bus yards). The overview of the terminal sites is shown in Table 3. Due to the inability to operate the test in the field, the driving time between the stations was obtained by crawling through the AutoNavi API.

Table 2 Overview of the frequency of each line

Table 3 Overview of all origin and destination stations in the bus network

Site name station number longitude latitude related lines Can it be used as a charging station Anting Bus Station -1 121.16 31.29 1,2 √ Hejing Road Anting Old Street Station -2 121.15 31.30 4,6 √ Anting North Railway Station -3 121.16 31.31 7 √ Shanghai Circuit -4 121.23 31.33 1,8 √ Huangdu Bus Station -5 121.21 31.27 7,8 √ Bus Changji East Road Station -6 121.20 31.29 3 Xiangyang Village -7 121.14 31.30 3 Shengxin South Road, Xiangfang Highway -8 121.26 31.31 2 Lianqun Village -9 121.25 31.28 5 lianxi village -10 121.25 31.27 4 Dengjiajiao Village -11 121.20 31.26 5,6 Dengjiajiao Village, Anzhi Road -12 121.19 31.26

In the second step, the Shanghai time-of-use electricity price charging standard used in this example is shown in Figure 5, and the parameters of other electric bus system construction and 3-year operation and maintenance cost models are shown in Table 4.

Table 4 Value table of electric bus system construction and operation and maintenance cost model parameters

Model parameters meaning value c _b Electric bus unit purchase cost 1,500,000 yuan c _c Single purchase cost of charging facilities 450,000 yuan c _t Electric bus operating cost per hour 25 yuan/hour

In the third step, this example uses the BYD K9 electric bus model to calibrate the charging and discharging characteristics of the vehicle battery. Since it is impossible to operate the test in the field, this example assumes that the battery charge and discharge functions are all linear functions. The definition of the battery charge and discharge function used in this example is shown in Table 5 and equations (26) to (29).

Table 5 Battery charge and discharge function table

function function description F(l _p ,t _p ) Charging function with l _p and t _p as arguments G(t _i ) The discharge function with t _i as the independent variable and e _i as the dependent variable G(t _ip ) Discharge function with t _ip as independent variable and e _ip as dependent variable G(t _pj ) Discharge function with t _pj as independent variable and e _pj as dependent variable

Among them, the values of the model parameters related to the characteristics of the electric bus battery and the upper and lower thresholds of the remaining battery power are shown in Table 6.

Table 6 Value table of model parameters related to electric bus battery characteristics and the upper and lower thresholds of remaining battery power

The fifth step is based on the electric bus operation scheduling and charging facility layout optimization model built in the fourth step, and the values of related parameters using the improved genetic algorithm are shown in Table 7.

Table 7 Value table of related parameters of improved genetic algorithm

Algorithm parameters meaning value P Genetic Algorithm Population Size 100 G maximum evolution algebra 2000 r small positive number in the fitness scaling function 0.01 P _c1 Adaptive Crossover Probability Initial Maximum 0.95 P _c2 Adaptive Crossover Probability Initial Minimum 0.85 k _c Rate of Change of Adaptive Crossover Probability as Genetic Algebra Changes 1 P _m1 Adaptive Mutation Probability Initial Maximum 0.1 P _m2 Adaptive Mutation Probability Initial Minimum 0.05 k _m Rate of Change of Adaptive Mutation Probability as Genetic Algebra Changes 0.2

After the completion of the fifth step, the optimal solution for the construction of the electric bus system and the total cost of 3-year operation and maintenance is 154,421,053.88 yuan, including the purchase cost of electric buses of 109,500,000 yuan (accounting for 70.9%) and the purchase cost of electric bus charging facilities of 103,500,000 yuan (accounting for ratio 6.7%), electricity cost 14086341.38 (accounting for 9.1%) and electric bus operating cost 20484712.5 yuan (accounting for 13.3%). Among them, the electric bus system has a total of 73 electric buses and 21 charging piles. An electric bus operates an average of 11.88 shifts per day, serves 5.18 lines, and travels 221.18 kilometers.

Among them, the change of the remaining battery power of 73 electric buses in a 24-hour operation cycle is shown in Figure 6. Specifically, the change of the remaining battery power of one of the electric buses in a 24-hour operation cycle is shown in Figure 7 shown. It can be seen that the partial charging method makes the charging scheduling plan of electric buses very flexible. A large amount of charging time is completed during non-operating hours at night, which reduces the occupation of daytime operating hours and maximizes the use of electric buses during operating hours. Vehicle resources.

In addition, the charging power consumption of the electric bus system for each hour in a 24-hour operation cycle is shown in Figure 8. It can be seen that the charging power consumption of the electric bus system is opposite to the fluctuation trend of the time-of-use electricity price, indicating that the optimal electric bus charging scheduling plan maximizes the use of low electricity prices for charging, and saves electricity costs to the greatest extent.

In this embodiment, under the background of the development of the electric bus system replacing the traditional bus system, without changing the constraints of the original bus schedule, the improved self-adaptive genetic algorithm is applied to simultaneously optimize the schedule scheduling plan, charging scheduling plan and charging schedule of the electric bus. The facility layout plan minimizes the total cost of electric bus system construction and operation and maintenance. The electric bus operation scheduling and charging facility layout scheme obtained by applying the model and algorithm has good operational feasibility and economy, and can provide reference for the planning, operation and management of the electric bus system.

Claims (8)

1. An electric bus system planning method is characterized by comprising the following steps:

step S1: acquiring bus network information and cost information, wherein the bus network information comprises bus network station information, inter-station empty driving duration information and line operation timetable information, and the bus network station information comprises the number of vehicle positions of the first and last stations of each line in a network, configuration facilities and power grid conditions;

step S2: utilizing bus network information and cost information to establish an electric bus network operation scheduling and charging facility layout optimization model with the minimum total cost of an electric bus system as a target;

step S3: solving an electric bus network operation scheduling and charging facility layout optimization model by utilizing a self-adaptive genetic algorithm of the continuous variation crossover probability and the continuous variation probability to obtain an electric bus system related to electric bus network operation scheduling and electric bus charging facility layout;

the objective function of the electric bus network operation scheduling and charging facility layout optimization model is as follows:

z is the total cost of the electric bus system and consists of four parts, namely electric bus acquisition cost, electric bus charging facility acquisition cost, electric charge cost and electric bus operation cost; c _b C, the acquisition cost of a single electric bus is c _c C, for the single purchase cost of the charging facility _e For electricity price, c _t E, operating cost per hour of electric bus _i For the amount of electricity consumed by shift i, t _i For the operating duration of class i, E _ij As the battery power change amount at the beginning of shift j compared with the end of shift i, T _ij The time period required from the end of the shift i to the arrival of the vehicle at the start station of the shift j is set; x is x _ij Taking 1 when the electric bus operates in the shift j after the operation of the shift i is finished, or taking 0; y is _pq Taking 1 for the charging pile when the charging event q is executed after the charging event p is finished, otherwise taking 0;

constraint conditions of the electric bus network operation scheduling and charging facility layout optimization model comprise:

vehicle scheduling constraints:

power consumption constraint:

charging scheduling constraints:

E _ij 、T _ij and l _p The calculation formula of (2) is as follows:

wherein i, j is the number of the shift or the first and last station, S is the set of all bus shifts, D is the set of all first and last stations, U is the union of S and D, P and Q are the numbers of charging events or the first and last stations, P is the set of all charging events, and Q is the union of P and D; a, a _i For the start time of shift i, l _i The remaining power of the vehicle after the shift i is finished; SOC (State of Charge) _max ,SOC _min The upper limit and the lower limit of the battery electric quantity are preset; z _ip Taking 1 when charging event p is carried out after operation shift i of electric bus is finished, otherwise taking 0, z _pj Taking 1 when operating the shift j after the electric bus carries out the charging event p, otherwise taking 0; a, a _p To start time of charging event p, t _p For the charge duration of the charge event p, l _p The remaining capacity of the electric bus before the charging event p begins; e, e _ip E is the electric quantity consumed by the electric bus running to the charging station where the charging event p is located after the operation shift i is finished _pj For the electric quantity consumed by the electric bus to travel to the start station of shift j after the charging event p is finished, t _ip For the time t consumed by the electric bus to travel to the charging station where the charging event p is located after the operation shift i is finished _pj The time consumed for the electric bus to travel to the start station of shift j after the charging event p is finished; t is t _ij For the time spent by the electric bus from the stop of class i to the start of class j, e _ij The electric bus runs from the destination station of class i to the starting station of class j to consume electric quantity; m is a sufficiently large number; f (l) _p ,t _p ) For the charging function, let l _p And t _p The dependent variable is the residual electric quantity of the electric bus after the charging event p is ended.

2. A method of planning an electric bus system according to claim 1 wherein the cost information includes a price of electricity charging standard, the price of electricity charging standard including a single price of electricity charging standard and a time-of-use price of electricity charging standard.

3. The electric bus system planning method according to claim 1, wherein the continuous variation crossover probability of the adaptive genetic algorithm is:

the continuous variation mutation probability of the adaptive genetic algorithm is as follows:

wherein P is _c1 、P _c2 、P _m1 、P _m2 Are constants greater than 0 and less than 1, G is the current genetic algebra, G is the maximum genetic algebra, and f' is the cross operationThe fitness of the individuals before the mutation operation, f is the average fitness of all the individuals in the population, f _max For maximum fitness, k, among all individuals of a population _c K is the change rate of the adaptive crossover probability along with the change of the genetic algebra _m Is the change rate of the adaptive mutation probability along with the change of the genetic algebra.

4. The electric bus system planning method according to claim 1, wherein the adaptive genetic algorithm is based on a feasible solution transformation method, that is, the crossover and mutation operations of the genetic algorithm are all within the feasible solution range.

5. The method for planning an electric bus system according to claim 1, wherein the adaptive genetic algorithm adopts an integer coding form, the number of chromosome gene digits is 2 times of the number of shifts of the electric bus system in one day, the odd gene digits represent the sequence from the left to the right, the odd gene digits b (b not less than 1) of the chromosome represent the number of vehicles operating the shift, the even gene digits c (c not more than 0) represent the number of charging stations, no charging station is charged after the shift is finished when c=0, and the charging station with the number of charging stations c is charged after the shift is finished when c < 0.

6. The electric bus system planning method according to claim 1, wherein the adaptive genetic algorithm fitness function is calibrated by the following formula:

wherein TC is _reci Inverse, TC, of total cost of electric bus system calculated for chromosome individual decoding _{reci_min} For all individuals TC in the population _reci Minimum value of (C) TC _{reci_max} For all individuals TC in the population _reci R is a positive constant.

7. The method of claim 1, wherein the electric bus network operation schedule comprises an electric bus fleet size, a vehicle scheduling and a vehicle charging schedule, the vehicle scheduling is a multi-yard multi-line scheduling, the vehicle charging schedule comprises a daytime charging schedule and a nighttime charging schedule, and the charging mode comprises partial charging.

8. The electric bus system planning method according to claim 1, wherein the electric bus charging facility layout comprises a position layout of electric bus charging piles and a number layout of positions.