Electric bus scheduling method considering charging facility capacity and battery degradation effect

Abstract

The application discloses a method for scheduling electric buses considering the influence of charging facility capacity and battery attenuation, which simultaneously considers the influence of charging facility capacity and battery attenuation characteristics on a vehicle scheduling scheme, including a driving plan of the vehicle and a charging scheme, and on this basis, an electric bus scheduling model is constructed in combination with a time-of-use electricity price mechanism, and the objective function of the model is to minimize the total cost of an enterprise, including a bus vehicle use cost, a charging cost and a battery attenuation cost. The application can be used for formulating an electric bus scheduling scheme, and helps to reduce the operation cost of the enterprise and prolong the service life of the battery.

Description

Technical Field

[0001] This invention relates to a method for scheduling electric buses that takes into account the capacity of charging facilities and the impact of battery degradation, and belongs to the field of urban public transportation operation and management technology. Background Technology

[0002] Electric buses offer advantages such as zero emissions and low noise, and the electrification of public transportation is of great significance for my country to achieve its dual-carbon goals. Compared with fuel-powered buses, electric buses have a shorter operating range, therefore, they need to be charged during the day to meet their electricity needs. Vehicle scheduling plans include driving schedules and charging schemes. To save on charging costs, vehicles often charge in areas with lower electricity prices. However, given the limited capacity of charging facilities, without reasonable scheduling, vehicles concentrating on charging in low-price areas often leads to queuing, which in turn affects subsequent driving schedules, increasing operating costs and scheduling complexity.

[0003] Batteries are a significant component of the procurement cost of electric buses. When the usable capacity of a battery degrades to 80%, it needs to be replaced, which significantly increases the costs for bus companies. Battery degradation is related to the initial charge level during charging and discharging, as well as the number of charge / discharge cycles and the depth of charge / discharge. Vehicle driving plans and charging schemes both affect battery life. To extend battery life, vehicles may reduce the number of charging cycles, which increases the number of vehicles in operation and prevents full utilization of low electricity price periods, thus impacting the company's procurement costs. Therefore, it is necessary to coordinate and optimize vehicle driving plans, charging schemes, charging station capacity, and the impact of battery degradation. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method for scheduling electric buses that takes into account the capacity of charging facilities and the impact of battery degradation. By considering the impact of charging facility capacity and battery degradation characteristics on the scheduling of electric buses, this method can help reduce the operating costs of enterprises and extend the battery life.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] The electric bus dispatching method, which takes into account the capacity of charging facilities and the impact of battery degradation, includes the following steps:

[0007] Step 1: Obtain electric bus operation data, calculate the average speed of the vehicles after data cleaning, and collect bus route operation data, electric bus data, and charging station data.

[0008] Step 2: Calculate the configuration cost of electric buses;

[0009] Step 3: Calculate the charging cost of electric buses;

[0010] Step 4: Calculate the battery degradation cost of electric buses;

[0011] Step 5: Based on Steps 2-4, establish an electric bus dispatching model that considers the capacity of charging facilities and the impact of battery degradation, and solve the model to obtain the optimal electric bus dispatching scheme.

[0012] In a preferred embodiment of the present invention, in step 1, the bus route operation data includes the route length and schedule information of the electric buses, the electric bus data includes the vehicle's charging power, discharging power, battery capacity and average daily usage cost, and the charging station data includes the available charger data and maximum charging power inside the charging station.

[0013] In a preferred embodiment of the present invention, in step 2, the configuration cost of electric buses is related to the number of vehicles used in the dispatching process, and the calculation formula is as follows:

[0014]

[0015]

[0016] In the formula, Z1 represents the configuration cost of electric buses; c f Let G be the unit configuration cost of the vehicle; G = {1,...,K} is the set of vehicles; r k The variable is 0-1; it is 1 if vehicle k runs at least one trip, and 0 otherwise; n is the total number of trips; x kij It is a 0-1 variable. If vehicle k runs consecutively on trains i and j, it is 1; otherwise, it is 0.

[0017] As a preferred embodiment of the present invention, the formula for calculating the charging cost of electric buses in step 3 is as follows:

[0018]

[0019] In the formula, Z2 is the charging cost; K is the total number of vehicles; and T is the total time interval. It is a 0-1 variable; if vehicle k is charged at time interval t, it is 1; otherwise, it is 0. The charging power of vehicle k during time interval t; Δ t f is the length of the time interval; t Let t be the electricity price over time interval t.

[0020] As a preferred embodiment of the present invention, the formula for calculating the battery degradation cost of the electric bus in step 4 is as follows:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] In the formula, Z3 represents the battery degradation cost; E represents the battery capacity; W(h) represents the battery degradation cost coefficient at the SOC interval h; n h The number of SOC intervals; This represents the change in SOC of vehicle k over interval h during charging. This represents the change in SOC (State of Charge) of vehicle k over interval h during discharge; e kij The amount of charge generated by vehicle k between train numbers i and j; ε kij T represents the power consumption of vehicle k between trains i and j; i e Let i be the arrival time of train number i. e0 represents the departure time of train j; e0 represents the discharge power; l i The length of train number i; Let i be the distance between the terminal station and the charging station for train i. This is the distance between the charging station and the starting station of train j; Let be the distance between the terminal station of train i and the starting station of train j.

[0027] As a preferred embodiment of the present invention, the electric bus dispatching model in step 5, which considers the capacity of charging facilities and the impact of battery degradation, is expressed as follows:

[0028] minZ=Z1+Z2+Z3

[0029] st

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039] ε min ≤ε kij ≤ε max ,

[0040] In the formula, Z represents the total cost; E ki Let M be the battery level of vehicle k at the starting station of train i; M is a positive number; v is the average speed of the vehicle. Let be the battery level of vehicle k during time interval T; P be the number of chargers in the charging station; Q be the total power of the charging station; L = {1,...,T} be the set of time intervals; ε min and ε max These are the minimum and maximum power of the charger, respectively.

[0041] In a preferred embodiment of the present invention, in step 5, the model is solved using a genetic algorithm, and the solution process is as follows:

[0042] 1) Integer encoding is used, and an initial solution is generated using a random method. The encoding process includes two parts: the first part is the vehicle scheduling plan, which allocates vehicles to trains in chronological order; the second part is the vehicle charging plan, which includes the charging power of the vehicles at each time interval.

[0043] 2) A tournament strategy is used for selection. Each time, a certain number of individuals are selected from the population, and the best individuals are added to the offspring population. The selection process is repeated until the new population size reaches the original population size.

[0044] 3) Multiple crossover points are randomly set on the chromosome, vehicles at the crossover points are swapped, and charging plans that meet the capacity constraints of charging facilities are randomly generated;

[0045] 4) The reciprocal of the objective function of the scheduling model is used as the fitness function. The chromosome with the highest fitness is the optimal electric bus scheduling scheme.

[0046] A computer device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the steps of the electric bus dispatching method described above, which takes into account the effects of charging facility capacity and battery degradation.

[0047] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the electric bus dispatching method described above, taking into account the effects of charging facility capacity and battery degradation.

[0048] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0049] 1. The electric bus dispatching method proposed in this invention considers the impact of charging facility capacity and battery degradation characteristics on vehicle dispatching schemes, including vehicle driving plans and charging schemes. Based on this, an electric bus dispatching model is constructed, which effectively reduces the total cost of enterprises, including bus usage costs, charging costs and battery degradation costs, and can reduce battery degradation and extend battery life.

[0050] 2. This invention can be used to formulate bus dispatching schemes that are more in line with actual conditions in electric bus dispatching, and has a very wide range of application scenarios. Attached Figure Description

[0051] Figure 1 This is a flowchart of the electric bus dispatching method of the present invention, which takes into account the capacity of charging facilities and the impact of battery degradation. Detailed Implementation

[0052] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0053] like Figure 1 As shown, this invention proposes a method for dispatching electric buses that considers the capacity of charging facilities and the impact of battery degradation. The specific steps are as follows:

[0054] Step 1: Obtain basic data;

[0055] In this step, the vehicle's operating data is first obtained through AVL data. After data cleaning, the average speed of the vehicle is calculated. Then, the operation data of the bus route, the vehicle, and the charging station are collected. The bus route data includes the route length and schedule information of the vehicle. The vehicle data includes the vehicle's charging power, discharging power, battery capacity, and average daily usage cost. The charging station data includes the available chargers and maximum charging power inside the charging station.

[0056] Step 2: Calculate the configuration cost of electric buses;

[0057] In this step, the configuration cost of electric buses is calculated. The configuration cost of the vehicles is related to the number of vehicles used in the dispatching process, and the calculation formula is as follows:

[0058]

[0059]

[0060] In the formula, Z1 represents the vehicle's operating cost (unit: yuan); c f The unit configuration cost of the vehicle (unit: yuan / vehicle); G = {1,...,K} is the set of vehicles; r k The variable is 0-1; it is 1 if vehicle k runs at least one trip, and 0 otherwise; n is the total number of trips; x kij It is a 0-1 variable. If vehicle k runs consecutively for trains i and j, it is 1; otherwise, it is 0.

[0061] Step 3: Calculate the vehicle charging cost;

[0062] In this step, the vehicle's charging cost is calculated. The charging cost is the product of charging time, power output, and electricity price, as shown in the following formula:

[0063]

[0064]

[0065]

[0066] E kj =E ki -ε kij +e kij (6)

[0067] In the formula, Z2 is the charging cost (unit: yuan); L = {1,...,T} is the set of time intervals; It is a 0-1 variable; if vehicle k is charged at time interval t, it is 1; otherwise, it is 0. The charging power of vehicle k at time interval t (unit: kW); Δ t The time interval is set to 1 minute; f t The electricity price for time interval t (unit: yuan / kWh); e kij The amount of electricity charged by vehicle k between trains i and j (in kWh); T i e Let i be the arrival time of train number i. Let ε be the departure time of train number j; kij e0 represents the power consumption of vehicle k between trains i and j (unit: kWh); e0 represents the discharge power (unit: kWh / km); l i The length of train number i (in km); The distance between the terminal station and the charging station for train number i (unit: km); The distance between the charging station and the starting station of train number j (in km); E represents the distance (in km) between the terminal station of train i and the starting station of train j;ki The energy level of vehicle k at the starting station of train i (unit: kWh).

[0068] Step 4: Calculate the cost of vehicle battery degradation;

[0069] In this step, the battery degradation cost of the vehicle is calculated as shown in the following formula:

[0070]

[0071]

[0072]

[0073] In the formula, Z3 represents the battery degradation cost (unit: yuan); n h Where: E is the number of SOC intervals; E is the battery capacity (unit: kWh); W(h) is the battery degradation cost coefficient for SOC interval h (unit: yuan / kWh); and These represent the changes in SOC of vehicle k during charging and discharging at intervals h, respectively.

[0074] The commonly used battery degradation cost factor is related to four SOC intervals, namely h = 1, 2, 3, 4, which correspond to SOC intervals of [0, 0.25), [0.25, 0.5), [0.5, 0.75), and [0.75, 1], respectively. The calculation formula is shown below:

[0075]

[0076] Step 5: Establish an electric bus dispatching model based on steps 2, 3, and 4; design an algorithm to solve the dispatching model and obtain the optimal electric bus dispatching scheme.

[0077] In this step, an electric bus dispatching model is established, as shown in the following formula:

[0078] minZ=Z1+Z2+Z3(11)

[0079] st

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] In the formula, M is a sufficiently large positive number; v is the average speed of the vehicle (unit: km / h); Let ε be the battery charge of vehicle k during time interval T; Q be the total power of the charging station (unit: kW); P be the number of chargers in the charging station; ε be the total ... ε be the total battery charge of vehicle k during time interval T; ε be the total battery charge of vehicle k during time interval min and ε max These are the minimum and maximum power of the charger (unit: kW).

[0091] Constraints (12) and (13) indicate that each trip can only be run by one vehicle; constraint (14) indicates that the charging amount cannot exceed the remaining capacity of the battery; constraint (15) indicates the time continuity of two consecutive trips; constraint (16) indicates that vehicle k can only be charged between trips i and j when vehicle k is running continuously between trips i and j; constraint (17) indicates the power constraint during operation; constraint (18) indicates that the vehicle is fully charged after running all trips; constraint (19) indicates the constraint on the number of chargers in the charging station; constraint (20) indicates the constraint on the power of the charging station; constraint (21) indicates the range of charging power.

[0092] In this step, a genetic algorithm is designed to solve the model. The parameters of the genetic algorithm are set as follows: population size of 30, number of iterations of 50, crossover probability of 0.8, and mutation probability of 0.2. The specific process of the genetic algorithm is as follows:

[0093] (1) Encoding

[0094] Integer encoding is used, and an initial solution is generated using a random method. The encoding process consists of two parts: the first part is the vehicle scheduling plan, which allocates vehicles to trains in chronological order; the second part is the vehicle charging plan, which includes the charging power of the vehicles at each time interval.

[0095] (2) Selection

[0096] A tournament-style selection strategy is employed. First, a certain number of individuals are selected from the population each time. Second, the best individuals are selected to enter the offspring population. Then, the selection process is repeated until the new population size reaches the original population size.

[0097] (3) Cross

[0098] The crossover process is a multi-point crossover, which means that multiple crossover points are randomly set on the chromosome. Specifically, the first part of the chromosome is crossed over, that is, vehicles at the crossover points are exchanged, and a charging plan that meets the capacity constraints of the charging facilities is randomly generated.

[0099] (4) Fitness

[0100] Using the reciprocal of the objective function shown in (11) as the fitness function, the chromosome with the highest fitness is the optimal electric bus scheduling scheme.

[0101] Based on the same inventive concept, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned electric bus dispatching method that takes into account the capacity of charging facilities and the impact of battery degradation.

[0102] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned electric bus dispatching method that considers the capacity of charging facilities and the impact of battery degradation.

[0103] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A method of scheduling electric bus vehicles taking into account charging infrastructure capacity and battery degradation effects, characterized in that, Includes the following steps: Step 1: Obtain electric bus operation data, calculate the average speed of the vehicles after data cleaning, and collect bus route operation data, electric bus data, and charging station data. Step 2: Calculate the configuration cost of electric buses; the configuration cost of electric buses is related to the number of vehicles used in the dispatching process, and the calculation formula is as follows: ， ， wherein Costs for configuring the electric public transport vehicles; Costs for configuring the units of vehicles; A set of vehicles; Is a 0-1 variable, 1 if vehicle k runs at least one trip, otherwise 0; n is the total number of trips; Is a 0-1 variable, 1 if vehicle k runs trip i and trip j consecutively, otherwise 0; Step 3: Calculate the charging cost of the electric bus. The calculation formula is as follows: ， wherein is the charging cost; K is the total number of vehicles, T is the total time interval; is a 0-1 variable, equal to 1 if vehicle k is charging at time interval t, and 0 otherwise; is the charging power of vehicle k at time interval t; is the length of the time interval; is the electricity price at time interval t; Step 4: Calculate the battery degradation cost of the electric bus. The calculation formula is as follows: ， ， ， ， ， In the formula, For battery degradation costs; Battery capacity; The battery degradation cost coefficient is the SOC interval h. The number of SOC intervals; This represents the change in SOC of vehicle k over interval h during charging. This represents the change in SOC of vehicle k over interval h during discharge; The amount of charge generated by vehicle k between train numbers i and j; The power consumption of vehicle k between train numbers i and j; Let i be the arrival time of train number i. Let J be the departure time of train number j; This refers to the discharge power. The length of train number i; Let i be the distance between the terminal station and the charging station for train i. This is the distance between the charging station and the starting station of train j; Let be the distance between the terminal station of train i and the starting station of train j; Step 5: Based on Steps 2-4, establish an electric bus dispatching model that considers the capacity of charging facilities and the impact of battery degradation, and solve the model to obtain the optimal electric bus dispatching scheme. The expression for the electric bus dispatching model is as follows: ， ， ， ， ， ， ， ， ， ， ， In the formula, Total cost; Let K be the battery level of vehicle k at the starting station of train i. It is a positive number; The average speed of the vehicle; Let K be the battery charge of vehicle K during time interval T. This refers to the number of chargers in the charging station. This refers to the total power of the charging station; A set of time intervals; and These are the minimum and maximum power of the charger, respectively.

2. The electric bus dispatching method considering the capacity of charging facilities and the impact of battery degradation according to claim 1, characterized in that, In step 1, the bus route operation data includes the route length and schedule information of electric buses, the electric bus data includes the vehicle's charging power, discharging power, battery capacity and average daily usage cost, and the charging station data includes the available chargers and maximum charging power inside the charging station.

3. The electric bus dispatching method considering the capacity of charging facilities and the impact of battery degradation according to claim 1, characterized in that, In step 5, the model is solved using a genetic algorithm, and the solution process is as follows: 1) Integer encoding is used, and an initial solution is generated using a random method. The encoding process includes two parts: the first part is the vehicle scheduling plan, which allocates vehicles to trains in chronological order; the second part is the vehicle charging plan, which includes the charging power of the vehicles at each time interval. 2) A tournament strategy is used for selection. Each time, a certain number of individuals are selected from the population, and the best individuals are added to the offspring population. The selection process is repeated until the new population size reaches the original population size. 3) Multiple crossover points are randomly set on the chromosome, vehicles at the crossover points are swapped, and charging plans that meet the capacity constraints of charging facilities are randomly generated; 4) The reciprocal of the objective function of the scheduling model is used as the fitness function. The chromosome with the highest fitness is the optimal electric bus scheduling scheme.

4. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the electric bus dispatching method as described in any one of claims 1 to 3, which takes into account the capacity of charging facilities and the impact of battery degradation.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the electric bus dispatching method as described in any one of claims 1 to 3, which takes into account the impact of charging facility capacity and battery degradation.

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