CN114118515A - Method and system for determining electric vehicle charging station fusing traffic network - Google Patents
Method and system for determining electric vehicle charging station fusing traffic network Download PDFInfo
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Abstract
The invention provides a method and a system for determining an electric vehicle charging station fused with a traffic network, which comprises the following steps: determining that the electric automobile needs to be charged in the driving process based on a pre-planned path: selecting a charging station with the lowest charging cost of the electric vehicle as a target, and adjusting the planned path; the pre-planned path is obtained based on the travel characteristics of the electric automobile and the traffic network; the charging cost includes a time cost and a price cost determined through a traffic network; according to the method, the time cost and the price cost are considered, the charging station is selected with the minimum sum of the time cost and the price cost as a target, and the travel path of the electric automobile is rationally planned.
Description
Technical Field
The invention relates to the technical field of electric vehicles, in particular to a method and a system for determining an electric vehicle charging station fused with a traffic network.
Background
In order to deal with the problems of exhaustion and pollution of fossil energy, pollution-free electric energy gradually replaces the fossil energy, and under the great trend, the electric energy becomes important secondary energy in the life nowadays, so that the life of people is facilitated. Nowadays, the development of electric vehicles is vigorously pursued, which is a necessary choice for dealing with two problems of exhaustion and environmental pollution of the current nonrenewable fossil energy. Meanwhile, under the background, the multiple disciplines realize cross fusion, and further development of internet economy and artificial intelligence is promoted. The problem of the existing novel electric automobile is vigorous development under the background that the communication speed of the internet is accelerated and the intelligent interaction is quicker.
Pollution-free electric vehicles are gradually widely used, the number of electric vehicles is increasing, the requirements for electric vehicle charging piles are expanding, charging stations are basic supporting service facilities popularized by electric vehicles, and it is necessary to plan a charging station building model.
At present, along with the popularization of electric vehicles and the construction of charging piles, the planning of electric vehicle charging stations is very important. And nowadays of intelligent high development, the integration of a traffic network, a power grid and an information network can be realized, parameters of a charging station are adjusted by establishing a model, an electric vehicle owner is guided to go to the relatively idle charging station, the problems of traffic jam and queuing of the charging station are reduced, and energy consumption is reduced. Meanwhile, the energy consumption of each charging station time period can be predicted, and the route of the electric vehicle and the load of the charging station can be reasonably planned in advance.
In the research of electric vehicle charging composite prediction, a statistical model is mostly established and a correlation function is added, and finally, an electric vehicle charging load prediction model is obtained. For example: establishing a travel chain of the electric vehicle, and establishing a model based on the travel chain; and (3) establishing a prediction model by utilizing probability statistics and a Monte Carlo simulation method. However, these methods have certain disadvantages.
1) In the research, the consideration of the user for selecting the charging station is to assume that the user can select the charging station with the optimal combination, but the decision of people in real life is not completely rational.
2) Most researches cannot consider the traffic jam problem of vehicles caused by charging decisions, and relatively fine models are not established.
3) When a model is established in research, data is completely static data, real-time change cannot be reflected, and information lacks of timely interaction. In the actual market, various factors may influence the charging price.
Disclosure of Invention
In order to solve the problem that traffic jam of vehicles caused by charging decisions cannot be considered in reality and people cannot reasonably select charging stations, the invention provides a method for selecting electric vehicle charging stations fusing a traffic network, which comprises the following steps:
determining that the electric automobile needs to be charged in the driving process based on a pre-planned path:
selecting a charging station with the lowest charging cost of the electric vehicle as a target, and adjusting the planned path;
the pre-planned path is obtained based on the travel characteristics of the electric automobile and the traffic network;
the charging cost includes a time cost and a price cost determined by a traffic network.
Preferably, the selecting a charging station with the lowest charging cost of the electric vehicle as a target and adjusting the planned path includes:
taking the charging stations passing through the planned path as passing points, and planning the path by adopting a pre-constructed path planning model to obtain a planned path passing through each charging station and the duration of the planned path;
determining the time to reach each charging station and the amount to be charged based on the planned path passing through each charging station;
acquiring waiting time at each charging station and a pre-calculated electricity price based on time of arrival at each charging station;
calculating the charging cost of reaching each charging station based on the time length, waiting time, amount to be charged and electricity price of the planned path;
taking the charging station with the minimum charging cost as a selected charging station, and adjusting a planned path by using the selected charging station;
the planning path comprises a passing road section and power consumption;
the path planning model is constructed by adopting a shortest path algorithm to determine an optimal planned path according to a traffic network topological structure and determining the duration of the planned path by combining a following model;
the electricity price is calculated through a pre-constructed charging electricity price formulation model based on the voltage of the power grid node and the time-of-use electricity price.
Preferably, the calculating the charging cost to each charging station based on the length of time, the waiting time, the amount to be charged, and the electricity price of the planned path includes:
calculating time cost for reaching each charging station based on the duration and waiting time of the planned path;
calculating a price cost to reach each charging station based on the amount to be charged and the electricity price;
and taking the sum of the time cost and the price cost as the charging cost of reaching each charging station.
Preferably, the planning of the path includes:
determining the travel information of the electric automobile according to the type of the electric automobile and the travel characteristic corresponding to the type;
obtaining a planned path of the electric automobile based on the travel information of the electric automobile and a pre-constructed path planning model;
wherein, electric automobile's trip information includes: travel time, departure place, destination and remaining capacity at departure.
Preferably, the path planning by the path planning model includes:
setting traffic stations on a traffic network based on a graph theory mode, and constructing a topological structure of the traffic network by taking a road section between two adjacent traffic stations as a line segment;
determining a length of time to pass through the line segment based on a follow-up model;
and obtaining an optimal planned path from the starting place to the destination by adopting a Dijkstra algorithm or a Bellman-ford algorithm on the topological structure of the traffic network based on the starting place and the destination.
Preferably, the electricity price is determined by the following steps:
calculating the current power grid node voltage according to the acquired basic load and the acquired charging load;
and determining by adopting a charging electricity price formulation model based on the current time-of-use electricity price of the charging station, the power grid node voltage, the service fee of the charging station and the adjustment coefficient.
Preferably, the charging price is formulated as follows:
Si,t＝St+Scs+St·θ·(1-Vi,t)
in the formula, Si,tFor electricity prices of ith grid node, StTime of day, ScsFor charging station service fee, theta is the adjustment factor, Vi,tThe voltage of the ith grid node at time t.
Preferably, the time cost is calculated as follows:
F1,i＝αt·(Tdrive,i,t+Tqueue,i,t)
in the formula, F1,iFor the cost of time, αtValue that can be created per unit time for a vehicle, Tdrive,i,tFor the running time of the vehicle, Tqueue,i,tThe car waiting time.
Preferably, the vehicle running time Tdrive,i,tCalculated as follows:
in the formula, VaveL is the total distance, which is the average travel speed of the road section.
Preferably, the price cost is calculated as follows:
F2,i＝(1-SOCt)·Cap·Ci,t
in the formula, F2,iTo cost of price, Ci,tFor charging station i price of electricity, SOCtThe ratio of the remaining electric quantity of the battery of the electric automobile is shown, and Cap is the capacity of the battery of the electric automobile.
Based on the same inventive concept, the invention also provides a system for determining the electric vehicle charging station fused with the traffic network, which comprises the following steps:
the judging module is used for determining that the electric automobile needs to be charged in the driving process based on a pre-planned path:
the charging station determining module is used for selecting a charging station with the lowest charging cost of the electric vehicle as a target and adjusting the planned path;
the pre-planned path is obtained based on the travel characteristics of the electric automobile and the traffic network;
the charging cost includes a time cost and a price cost determined by a traffic network.
Preferably, the charging station determination module includes:
the path planning submodule is used for taking the charging stations passing through the planned path as path points and adopting a pre-constructed path planning model to plan the path so as to obtain the planned path passing through each charging station and the duration of the planned path;
the calculation submodule is used for determining the time of arriving at each charging station and the amount to be charged based on the planned path passing through each charging station, and acquiring the waiting time and the pre-calculated electricity price at each charging station based on the time of arriving at each charging station;
the cost calculation submodule is used for calculating the charging cost of each charging station based on the time length, the waiting time, the amount to be charged and the electricity price of the planned path;
the selection submodule is used for taking the charging station with the minimum charging cost as a selected charging station and adjusting the planned path by using the selected charging station;
the planning path comprises a passing road section, and time and power consumption of passing each road section;
the path planning model is constructed by adopting a shortest path algorithm to determine an optimal planning path according to a traffic network topological structure and combining the duration determined by the following model;
the electricity price is calculated through a pre-constructed charging electricity price formulation model based on the voltage of the power grid node and the time-of-use electricity price.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention provides a travel path planning method for an electric automobile, which comprises the following steps: determining that the electric automobile needs to be charged in the driving process based on a pre-planned path: selecting a charging station with the lowest charging cost of the electric vehicle as a target, and adjusting the planned path; the pre-planned path is obtained based on the travel characteristics of the electric automobile and the traffic network; the charging cost includes a time cost and a price cost determined through a traffic network; according to the method, the time cost and the price cost are considered, the charging station is selected with the minimum sum of the time cost and the price cost as a target, and the travel path of the electric automobile is rationally planned.
(2) The invention utilizes a traffic network topological structure to determine an optimal planned path by adopting a shortest path algorithm, determines path planning time by combining a following model, calculates power price and waiting time by a power grid according to the time of an electric vehicle reaching a charging station and the information of a vehicle to be charged in the charging station, obtains time cost by the path planning time and the waiting time, and calculates charging cost by the information to be charged and the power price.
Drawings
FIG. 1 is a flow chart of a control method of an electric vehicle charging station according to the present invention;
FIG. 2 is a flow chart of a charging tariff assignment model of the present invention;
FIG. 3 is a flow chart of the trip characteristics of the electric vehicle of the present invention;
FIG. 4 is a schematic diagram of a road network micro traffic model based on a following model according to the present invention;
FIG. 5 is a schematic diagram of a simple trip chain model according to the present invention;
FIG. 6 is a diagram of a general trip chain model according to the present invention;
FIG. 7 is a schematic diagram of a complex trip chain model according to the present invention;
FIG. 8 is a schematic diagram of the Dijkstra algorithm;
FIG. 9 is a flowchart illustrating an overall method of controlling the electric vehicle charging station according to the present invention;
fig. 10 shows the charging station load of the present invention as a function of time.
Detailed Description
Under the condition that the construction of an internet technology, an electric power technology high-speed station and a traffic network is successfully realized, the invention discloses an electric vehicle travel path planning method, which is driven by electricity price and solves the problem of pressure on urban power loads caused by centralized charging of a large number of electric vehicles at a certain charging station under the limit of urban limited power distribution capacity. On the basis, the urban electric vehicle charging network can be predicted, so that traffic planning is realized, electric power resources are saved, and the travel path of the electric vehicle is scientifically and reasonably planned.
Example 1: a regulation and control method for an electric vehicle charging station is disclosed as shown in figure 1: the method comprises the following steps:
step 1: determining that the electric automobile needs to be charged in the driving process based on a pre-planned path:
step 2: selecting a charging station by taking the lowest charging cost of the electric vehicle as a target, and adjusting the planned path;
the pre-planned path is obtained based on the travel characteristics of the electric automobile and the traffic network; the charging cost includes a time cost and a price cost determined by a traffic network.
The invention provides a method and a system for regulating and controlling an electric vehicle charging station, which mainly comprise the following aspects as shown in fig. 9:
obtaining travel time through travel characteristics and sampling of the electric vehicle; selecting a shortest travel time path; simulating the running of the electric automobile by using a following model; judging whether the automobile needs to be charged or not and selecting a charging place; planning a route to a charging station; queuing for charging and recording data; and acquiring the load of the charging station at regular time and establishing a charging electricity price appointed model to adjust the electricity price.
Step 1: when the electric automobile is determined to need to be charged in the driving process based on the pre-planned path, the method comprises the following steps:
A. determining the travel information of the electric automobile according to the type of the electric automobile and the travel characteristic corresponding to the type;
B. obtaining a planned path of the electric automobile based on the travel information of the electric automobile and a pre-constructed path planning model;
wherein, electric automobile's trip information includes: travel time, departure place, destination and remaining capacity at departure.
The following is a detailed description of the travel characteristics of each type of electric vehicle according to the type of the electric vehicle.
The trip characteristics of the electric vehicle comprise an electric power system dispatching model, a main trip chain mode of a private car and taxi trip characteristics based on an origin-destination matrix, as shown in fig. 2: s1 electric vehicle travel characteristics, S2: road network micro traffic model based on following model, S3: the charging electricity price making model comprises the following specific contents:
s1: the trip characteristic of the electric vehicle, as shown in fig. 3, includes S101: private car trip chain and S102: the trip characteristics of the taxi are respectively described in detail as follows:
s101: private car trip chain
For a private car trip chain, the invention mainly divides a city into three functional areas: the method comprises the steps of selecting private cars and taxis occupying the vast majority of urban electric vehicle traffic as study objects of travel characteristics in residential areas, working areas and business areas, and constructing a travel chain.
In real life, for private cars, people often go with a residential area where the house is located as a starting point and an end point, so the invention constructs three models of a trip chain, as shown in fig. 5, 6 and 7. Taking 2009 vehicle travel data of the U.S. department of transportation as an example, fitting the time of leaving a residential area, the time of leaving a working area and the residence time of a business area respectively, specifically as follows:
1) time of leaving resident zone
Fitting with a gaussian distribution function:
wherein the coefficient mu of the Gaussian functione＝6.92，σe1.24; and a is the time of leaving the residential area.
2) Time of departure from work zone
Fitting with a gaussian distribution function:
wherein the coefficient mu of the Gaussian functions＝17.47，σs1.8; and a is the time of leaving the working area.
3) Residence time in commercial zone
Residence times in commercial zones follow a generalized distribution of extremes:
where t is the residence time in the commercial zone and z is the distribution coefficient of the extremum.
S102: taxi trip characteristics
For a taxi, under the ideal condition, the taxi is operated for twenty-four hours, and obviously, the starting point of the taxi does not have clear regularity like a private car, so that the taxi travel characteristic model based on an origin-destination (OD) matrix is selected, and the OD matrix is constructed as follows:
wherein the content of the first and second substances,
representing the number and probability of automobiles taking i as a starting point and j as a destination in the time period; and n represents the number of nodes in the urban road topology.
The following describes the construction of the path planning model in detail:
s2, road network micro traffic modeling based on the following model: the method comprises the steps of confirming the shortest driving path, introducing a Dijkstra algorithm, and modeling the driving speed of the vehicle, as shown in FIG. 4, and comprises the following steps of S201: confirmation of shortest driving distance, S202: dijkstra algorithm, S203: the following describes each step in detail, respectively, of a model of the vehicle running speed.
S201: determination of shortest path of driving
The invention firstly constructs the topological structure G { N, T of the traffic networkLN represents the set of nodes in the road network, TLThe method is used for describing the time length of the vehicle passing through each road section and the connection relation between the nodes. The construction of the structure selects a graph theory mode, and the specific construction mode is as follows:
in the formula: t is tijRepresenting the length of time that the vehicle has traveled through the road between nodes i and j. Initial time tijObtained from the length of the road and the highest speed limit, and then the vehicle passes through the roadCalculating the average travel time between intervals to obtain TL(i, j) is a description of the length of time that the vehicle passes through each road section and the connection relationship between the nodes.
S202: dijkstra algorithm
For the selection of the best path, the present invention compares the efficiency of the Dijkstra algorithm with the Bellman-ford algorithm and proposes an improvement to them to obtain the highest efficiency. And finally, selecting the optimal path calculated by a Dijkstra algorithm, and refreshing the path at regular time.
The Dijkstra algorithm is applicable to a positive cost directed network, so that when weighting is given to a road section, the weight needs to be ensured to be positive; meanwhile, the minimum weight path from the starting point to the end point under different meanings can be obtained by endowing the road sections with weights with different physical meanings. The basic schematic of the Dijkstra algorithm is shown in fig. 8.
S203: model of vehicle speed
The invention carries out dynamic simulation on microscopic driving of road vehicles, and the simulation mainly comprises a following model, a lane change model and an intersection model.
Let τ be the driver reaction time, b be the maximum deceleration, vl(t) is the speed of the vehicle ahead at time t, Δ t is the time step, gl(t) is the distance from the rear head to the rear of the front vehicle at time t, vmaxThe maximum speed of the vehicle, a is the maximum acceleration, and eta is a random disturbance factor.
The maximum safe speed of the driver is:
the best driving speed that can be achieved by the driver is:
vdes(t)＝min[vmax,v(t-△t)+a·△t,vsafe(t)] (10)
the real-time speed of the car is:
v(t)＝max[0,vdes(t)-η] (11)
the vehicle is located at the following positions:
x(t)＝x(t-△t)+v(t-△t)·△t (12)
step 2: the method comprises the following steps of selecting a charging station by taking the lowest charging cost of the electric vehicle as a target, and adjusting a planned path, wherein the method specifically comprises the following steps:
taking the charging stations passing through the planned path as path points, and planning the path by adopting a path planning model constructed in advance to obtain the planned path passing through each charging station and the time length of the planned path;
determining the time to reach each charging station and the amount to be charged based on the planned path passing through each charging station;
acquiring waiting time at each charging station and a pre-calculated electricity price based on time of arrival at each charging station;
calculating the charging cost of each charging station based on the time length, waiting time, amount to be charged and electricity price of the planned path;
taking the charging station with the minimum charging cost as the selected charging station, and adjusting the planned path by using the selected charging station;
the planning path comprises a passing road section and power consumption;
the path planning model is constructed by adopting a shortest path algorithm to obtain and determine an optimal planning path according to a traffic network topological structure and combining the duration of the planning path obtained by the following model;
the electricity price is calculated through a pre-constructed charging electricity price formulation model based on the voltage of the power grid node and the time-of-use electricity price.
The electricity price is determined according to the following steps:
calculating the current power grid node voltage according to the acquired basic load and the acquired charging load;
and determining by adopting a charging electricity price formulation model based on the current time-of-use electricity price of the charging station, the voltage of the power grid node, the service fee of the charging station and the adjustment coefficient.
The following describes in detail the selection of charging stations and the charging electricity price formulation model used when selecting charging stations:
and S3, making a model of the charging electricity price, wherein the model comprises reasonably making the electricity price for adjustment according to load prediction.
For the driver of an electric vehicle, the choice of a charging station for charging the vehicle depends overall on the time cost and the price cost of the electric power, while the time cost is often determined by the travel time of the vehicle and the waiting time at the charging station, namely:
Tqueue,i,t＝Tleave,i,t{h}+g·Tqueue_charge (14)
F1,i＝αt·(Tdrive,i,t+Tqueue,i,t) (15)
wherein, F1,iFor the time cost of the vehicle, αtValue that can be created per unit time for a vehicle, Tdrive,i,tFor the running time of the vehicle, Tqueue,i,tFor car waiting time, VaveThe average driving speed of the road section is L, the total distance is I, the charging station number is I, Tleave, i, t { Tleave, i, t,1, Tleave, i, t,2, …, Tleave, i, t, k } is a collection set of the electric vehicles which are being charged in the ith charging station at the time t and are sorted from small to large according to the time required for full charge, and k is the number of the electric vehicles which are being charged in the i charging station. h is the remainder of n/M, and M is the charging pile number of the charging station. Tleave, i, t { h } represents the time required to leave the charging station for the h-th time after the sorting. g is a quotient of n/M, which indicates that g queued vehicles are charged in the charging pile after the electric vehicle arrives at the charging station, and Tqueue _ charge is the time required for fully charging one queued electric vehicle.
And the price cost of charging is:
F2,i＝(1-SOCt)·Cap·Ci,t (16)
wherein C isi,tFor charging station i price of electricity, SOCtThe ratio of the remaining electric quantity of the battery of the electric automobile is shown, and Cap is the capacity of the battery of the electric automobile.
The principle of the user selecting a charging station is therefore:
F＝min(F1,i+F2,i) (17)
and specifically for the electricity prices of the charging stations, it includes the electricity prices for purchasing electricity from the power grid, the service fees of the charging stations, and the fees adjusted in consideration of the node voltages. Therefore, the invention calculates the voltage of the grid node according to the basic load and the charging load, and each charging station adjusts the electricity price according to the voltage drop condition of each node based on the current time-of-use electricity price, thereby forming the time-of-use electricity price. One power grid node corresponds to a plurality of network nodes, and the drop of the voltage of the power grid node can influence the price of the power of the charging station on the plurality of network nodes corresponding to the power grid node.
The electric vehicle load prediction framework based on the road network-power grid interaction is mainly divided into three parts, namely road network calculation, vehicle and charging station calculation and power grid calculation, as shown in fig. 10. The calculation process takes T1 seconds as a step, data are interacted among the road network, the vehicle, the charging station and the power grid once every T2 minutes, and the charging station updates the charging electricity price every T3 minutes.
The specific formula is as follows:
Si,t＝St+Scs+St·θ·(1-Vi,t) (18)
in the formula, Si,tFor electricity prices of ith grid node, StTime of day, ScsFor charging station service fee, theta is the adjustment factor, Vi,tThe voltage of the ith grid node at time t.
Finally, the purpose of adjusting the load of the charging station through the real-time electricity price is achieved through interaction of a road network and a power grid.
The method is different from the traditional method that modeling planning is carried out through static data, and a real-time model is established through road network-power grid real-time interaction and taking the electricity price as driving, so that the regulation and control of the charging station are realized more scientifically and reasonably. Meanwhile, the invention considers the problem of traffic jam in reality, further refines the model, stands at the visual angle of the user as much as possible, and considers the multi-aspect consideration of the user for the selection of the charging station, thereby avoiding the over-ideal planning.
Example 2
The invention based on the same invention concept also provides an electric automobile travel path planning system, which comprises:
the judging module is used for determining that the electric automobile needs to be charged in the driving process based on a pre-planned path:
the route adjusting module is used for selecting a charging station by taking the lowest charging cost of the electric vehicle as a target and adjusting the planned route;
the method comprises the following steps that a pre-planned path is obtained based on the traveling characteristics of the electric automobile and the traffic network;
the charging cost includes a time cost and a price cost determined by a traffic network.
The path adjustment module includes:
the path planning submodule is used for taking the charging stations passing through the planned path as path points and adopting a pre-constructed path planning model to plan the path so as to obtain the planned path passing through each charging station and the time length of the planned path;
the calculation submodule is used for determining the time of arriving at each charging station and the amount to be charged based on the planned path passing through each charging station, and acquiring the waiting time and the pre-calculated electricity price at each charging station based on the time of arriving at each charging station;
the cost calculation submodule is used for calculating the charging cost of each charging station based on the time length of the planned path, the waiting time, the amount to be charged and the electricity price;
the selection submodule is used for taking the charging station with the minimum charging cost as the selected charging station; taking the planned route of the charging station selected by the route as an electric vehicle travel route;
the planning path comprises a passing road section, time passing each road section and power consumption;
the path planning model is constructed by adopting a shortest path algorithm to determine an optimal planned path according to a traffic network topological structure and determining the duration of the planned path by combining a following model;
the electricity price is calculated through a pre-constructed charging electricity price formulation model based on the acquired service charge of the charging station, the adjustment coefficient, the electricity price of the power grid node and the time-of-use electricity price.
The electricity price is determined according to the following steps:
calculating the current power grid node voltage according to the acquired basic load and the acquired charging load;
and determining by adopting a charging electricity price formulation model based on the current time-of-use electricity price of the charging station, the voltage of the power grid node, the service fee of the charging station and the adjustment coefficient.
The cost calculation submodule calculates the charging cost specifically by the following formula:
F＝min(F1,i+F2,i)
wherein F is the charging cost, F1,iFor cost of time, F2,iTo be the cost of the price.
The price cost is calculated as follows:
F2,i＝(1-SOCt)·Cap·Ci,t
in the formula, F2,iTo cost of price, Ci,tFor charging station i price of electricity, SOCtThe ratio of the remaining electric quantity of the battery of the electric automobile is shown, and Cap is the capacity of the battery of the electric automobile.
The time cost is calculated as follows:
F1,i＝αt·(Tdrive,i,t+Tqueue,i,t)
in the formula, F1,iFor the cost of time, αtValue that can be created per unit time for a vehicle, Tdrive,i,tFor the running time of the vehicle, Tqueue,i,tThe car waiting time.
Vehicle travel time Tdrive,i,tCalculated as follows:
in the formula, VaveL is the total distance, which is the average travel speed of the road section.
The charging price is formulated as follows:
Si,t＝St+Scs+St·θ·(1-Vi,t)
in the formula, Si,tFor electricity prices of ith grid node, StTime of day, ScsFor charging station service fee, theta is the adjustment factor, Vi,tThe voltage of the ith grid node at time t.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may 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, and the like) having computer-usable program code embodied therein.
The present 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 flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention are included in the scope of the claims of the present invention.
Claims (12)
1. A method for selecting an electric vehicle charging station fused with a traffic network is characterized by comprising the following steps:
determining that the electric automobile needs to be charged in the driving process based on a pre-planned path:
selecting a charging station with the lowest charging cost of the electric vehicle as a target, and adjusting the planned path;
the pre-planned path is obtained based on the travel characteristics of the electric automobile and the traffic network;
the charging cost includes a time cost and a price cost determined by a traffic network.
2. The method of claim 1, wherein the selecting a charging station with the goal of lowest cost for charging the electric vehicle and adjusting the planned path comprises:
taking the charging stations passing through the planned path as passing points, and planning the path by adopting a pre-constructed path planning model to obtain a planned path passing through each charging station and the duration of the planned path;
determining the time to reach each charging station and the amount to be charged based on the planned path passing through each charging station;
acquiring waiting time at each charging station and a pre-calculated electricity price based on time of arrival at each charging station;
calculating the charging cost of reaching each charging station based on the time length, waiting time, amount to be charged and electricity price of the planned path;
taking the charging station with the minimum charging cost as a selected charging station, and adjusting a planned path by using the selected charging station;
the planning path comprises a passing road section and power consumption;
the path planning model is constructed by adopting a shortest path algorithm to determine an optimal planned path according to a traffic network topological structure and determining the duration of the planned path by combining a following model;
the electricity price is calculated through a pre-constructed charging electricity price formulation model based on the voltage of the power grid node and the time-of-use electricity price.
3. The method of claim 2, wherein calculating a charging cost to each charging station based on the length of time, wait time, amount to be charged, and price of electricity for the planned path comprises:
calculating time cost for reaching each charging station based on the duration and waiting time of the planned path;
calculating a price cost to reach each charging station based on the amount to be charged and the electricity price;
and taking the sum of the time cost and the price cost as the charging cost of reaching each charging station.
4. The method of claim 2, wherein the planning of the path comprises:
determining the travel information of the electric automobile according to the type of the electric automobile and the travel characteristic corresponding to the type;
obtaining a planned path of the electric automobile based on the travel information of the electric automobile and a pre-constructed path planning model;
wherein, electric automobile's trip information includes: travel time, departure place, destination and remaining capacity at departure.
5. The method of claim 4, wherein the path planning model performs path planning by:
setting traffic stations on a traffic network based on a graph theory mode, and constructing a topological structure of the traffic network by taking a road section between two adjacent traffic stations as a line segment;
determining a length of time to pass through the line segment based on a follow-up model;
and obtaining an optimal planned path from the starting place to the destination by adopting a Dijkstra algorithm or a Bellman-ford algorithm on the topological structure of the traffic network based on the starting place and the destination.
6. The method of claim 3, wherein the electricity price is determined by:
calculating the current power grid node voltage according to the acquired basic load and the acquired charging load;
and determining by adopting a charging electricity price formulation model based on the current time-of-use electricity price of the charging station, the power grid node voltage, the service fee of the charging station and the adjustment coefficient.
7. The method of claim 6, wherein the charging tariff is modeled as follows:
Si,t＝St+Scs+St·θ·(1-Vi,t)
in the formula, Si,tFor electricity prices of ith grid node, StTime of day, ScsFor charging station service fee, theta is the adjustment factor, Vi,tThe voltage of the ith grid node at time t.
8. The method of claim 3, wherein the time cost is calculated as:
F1,i＝αt·(Tdrive,i,t+Tqueue,i,t)
in the formula, F1,iFor the cost of time, αtFor vehicle unit timeValue that can be created, Tdrive,i,tFor the running time of the vehicle, Tqueue,i,tThe car waiting time.
9. Method according to claim 8, characterized in that the vehicle travel time Tdrive,i,tCalculated as follows:
in the formula, VaveL is the total distance, which is the average travel speed of the road section.
10. The method of claim 3, wherein the price cost is calculated as:
F2,i＝(1-SOCt)·Cap·Ci,t
in the formula, F2,iTo cost of price, Ci,tFor charging station i price of electricity, SOCtThe ratio of the remaining electric quantity of the battery of the electric automobile is shown, and Cap is the capacity of the battery of the electric automobile.
11. A system for determining an electric vehicle charging station fused with a traffic network is characterized by comprising:
the judging module is used for determining that the electric automobile needs to be charged in the driving process based on a pre-planned path:
the charging station determining module is used for selecting a charging station with the lowest charging cost of the electric vehicle as a target and adjusting the planned path;
the pre-planned path is obtained based on the travel characteristics of the electric automobile and the traffic network;
the charging cost includes a time cost and a price cost determined by a traffic network.
12. The system of claim 11, wherein the charging station determination module comprises:
the path planning submodule is used for taking the charging stations passing through the planned path as path points and adopting a pre-constructed path planning model to plan the path so as to obtain the planned path passing through each charging station and the duration of the planned path;
the calculation submodule is used for determining the time of arriving at each charging station and the amount to be charged based on the planned path passing through each charging station, and acquiring the waiting time and the pre-calculated electricity price at each charging station based on the time of arriving at each charging station;
the cost calculation submodule is used for calculating the charging cost of each charging station based on the time length, the waiting time, the amount to be charged and the electricity price of the planned path;
the selection submodule is used for taking the charging station with the minimum charging cost as a selected charging station and adjusting the planned path by using the selected charging station;
the planning path comprises a passing road section, and time and power consumption of passing each road section;
the path planning model is constructed by adopting a shortest path algorithm to determine an optimal planning path according to a traffic network topological structure and combining the duration determined by the following model;
the electricity price is calculated through a pre-constructed charging electricity price formulation model based on the voltage of the power grid node and the time-of-use electricity price.
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| CN114936666B (en) * | 2022-03-24 | 2024-05-10 | 国网河北省电力有限公司营销服务中心 | Electric automobile charging navigation method and system based on vehicle-station-platform system |
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