CN112381398A - Electric vehicle charging station site selection method and system considering limited trip of user - Google Patents
Electric vehicle charging station site selection method and system considering limited trip of user Download PDFInfo
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Abstract
The disclosed electric vehicle charging station site selection method and system considering limited trip of users comprises the following steps: acquiring traffic information and user information in a city to be planned; inputting the traffic information and the user information into a charging station address planning model, and solving to obtain a charging station address; the charging station address planning model is used for selecting a travel path based on user limitation, determining the space-time distribution of the charging load of the electric automobile by combining the charging behavior of the electric automobile, determining the charging demand points of the electric automobile according to the space-time distribution of the charging load of the electric automobile, and acquiring the charging station address by taking the minimum construction quantity of the charging stations as a target and the charging demand of any electric automobile satisfied by at least one charging station as a constraint condition. Guarantee the user actual conditions of laminating more based on limited rationality analysis trip to reflect electric automobile actual charging demand, under the condition that satisfies user's demand of charging, guarantee that the charging station quantity of construction is minimum.
Description
Technical Field
The disclosure relates to an electric vehicle charging station site selection method and system considering limited trip of a user.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The electric automobile has the remarkable characteristics of high efficiency, energy conservation, zero emission and the like, and is stepping into the industrialized era of high-speed development as a green travel tool. However, the travel of large-scale electric vehicles and the charging and replacing behaviors thereof bring huge challenges to the coordinated development of urban traffic networks and power grids, so that the addresses of the electric vehicle charging stations are reasonably planned, the charging requirements of the urban electric vehicles are met, the traffic drainage function of the charging stations is exerted, and the method has important significance in further promoting the upgrading of the electric vehicle industry.
At present, a planning method of an electric vehicle charging station is mainly based on charging demand analysis of an electric vehicle on a typical day, the charging demand analysis is more dependent on statistical analysis and a maximum utility theory, incomplete rationality of travelers in aspects of travel modes and charging behaviors is neglected in the analysis, actual charging demands are difficult to accurately reflect, and the actual utilization rate of part of charging stations is low.
Disclosure of Invention
In order to solve the problems, the invention provides an electric vehicle charging station site selection method and system considering limited travel of a user, when the site selection is carried out on an electric vehicle charging station, a travel path is selected based on the limited travel analysis of the user, the space-time distribution of the charging load of the electric vehicle is determined according to the charging behavior of the electric vehicle, the minimum number of charging station constructions is taken as a target, the charging station address is determined, the user travel is ensured to be more fit to the actual situation based on the limited travel analysis, the actual charging requirement of the electric vehicle is reflected, the minimum number of the constructed charging stations is ensured under the condition of meeting the charging requirement of the user, the utilization rate of the charging stations is improved, and the cost is reduced.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
in one or more embodiments, an electric vehicle charging station location method considering limited user travel is provided, which includes:
acquiring traffic information and user information in a city to be planned;
inputting the traffic information and the user information into a charging station address planning model, and solving to obtain a charging station address;
the charging station address planning model is used for selecting a travel path based on user limitation, determining the space-time distribution of the charging load of the electric automobile by combining the charging behavior of the electric automobile, determining the charging demand points of the electric automobile according to the space-time distribution of the charging load of the electric automobile, and acquiring the charging station address by taking the minimum construction quantity of the charging stations as a target and the charging demand of any electric automobile satisfied by at least one charging station as a constraint condition.
In one or more embodiments, an electric vehicle charging station location system considering limited user travel is provided, including:
the data acquisition module is used for acquiring traffic information and user information in the city to be planned;
the data processing module is used for inputting the traffic information and the user information into a charging station address planning model and solving to obtain a charging station address; the charging station address planning model is used for selecting a travel path based on user limitation, determining the space-time distribution of the charging load of the electric automobile by combining the charging behavior of the electric automobile, determining the charging demand points of the electric automobile according to the space-time distribution of the charging load of the electric automobile, and acquiring the charging station address by taking the minimum construction quantity of the charging stations as a target and the charging demand of any electric automobile satisfied by at least one charging station as a constraint condition.
In one or more embodiments, a computer-readable storage medium is provided for storing computer instructions which, when executed by a processor, perform the steps of the electric vehicle charging station location method considering limited user travel.
Compared with the prior art, the beneficial effect of this disclosure is:
1. this openly when electric automobile charging station siting, the trip route has been selected based on user's limited rational analysis, and the spatial and temporal distribution of electric automobile charging load has been confirmed according to electric automobile's the action of charging, and then regard charging station construction quantity as the target at least, the charging station address has been confirmed, guarantee the user actual conditions of laminating more of trip based on limited rational analysis, thereby reflect electric automobile actual charging demand, under the condition that satisfies user's demand of charging, guarantee that the charging station quantity of construction is minimum, the utilization ratio of charging station has been improved, and the cost is reduced.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
Fig. 1 is a flow chart of embodiment 1 of the present disclosure.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only relational terms determined for convenience in describing structural relationships of the parts or elements of the present disclosure, and do not refer to any parts or elements of the present disclosure, and are not to be construed as limiting the present disclosure.
In the present disclosure, terms such as "fixedly connected", "connected", and the like are to be understood in a broad sense, and mean either a fixed connection or an integrally connected or detachable connection; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present disclosure can be determined on a case-by-case basis by persons skilled in the relevant art or technicians, and are not to be construed as limitations of the present disclosure.
Example 1
The limited rationality is a rationality between the perfect rationality and the imperfect rationality under a certain constraint. Under the complete rationality, a traveler comprehensively considers characteristic factors such as travel time and travel cost of alternative travel schemes, and selects an optimal travel mode based on the utility maximization principle; in the limited rationality, the traveler cannot be completely sensitive to the characteristic factors, and the selection of the travel mode is influenced by subjective factors, "i.e., conscious and rational, but the rationality is limited".
In this embodiment, a method for locating an electric vehicle charging station in consideration of limited user travel is provided, which includes:
acquiring traffic information and user information in a city to be planned, wherein the traffic information comprises traffic network information, and the user information comprises information of a residential area, a working area and a functional area of a user, travel habit information of the user and the like;
inputting the traffic information and the user information into a charging station address planning model, and solving to obtain a charging station address;
acquiring traffic information and user information in a city to be planned;
inputting the traffic information and the user information into a charging station address planning model, and solving to obtain a charging station address;
the charging station address planning model is used for selecting a travel path based on user limitation, determining the space-time distribution of the charging load of the electric automobile by combining the charging behavior of the electric automobile, determining the charging demand points of the electric automobile according to the space-time distribution of the charging load of the electric automobile, and acquiring the charging station address by taking the minimum construction quantity of the charging stations as a target and the charging demand of any electric automobile satisfied by at least one charging station as a constraint condition.
Further, the charging station address planning model comprises an electric vehicle space-time distribution model and an electric vehicle charging behavior model, wherein the electric vehicle space-time distribution model is used for obtaining a trip path of an electric vehicle and the space-time distribution of the electric vehicle by taking the prospect value of an electric vehicle alternative path selected by a user in a limited manner as a maximum target, combining the space-time distribution of the electric vehicle and the electric vehicle charging behavior model to obtain the space-time distribution of a charging load of the electric vehicle, determining a charging demand point of the electric vehicle according to the space-time distribution of the charging load of the electric vehicle, taking the condition that at least one charging station meets the charging demand of any electric vehicle as a constraint condition, and obtaining the charging station address by taking the minimum construction number of the charging stations as a target.
Further, the electric vehicle space-time distribution model comprises a trip chain and trip path decision model which accord with the actual trip rule of the electric vehicle, and the space-time distribution condition of the electric vehicle is obtained by taking the maximum prospect value of the electric vehicle alternative path selected by the user in a limited manner as a target;
the trip chain comprises the starting time, the parking time, the space transfer probability, the driving distance and the driving time of the electric automobile which embody the space-time characteristics of the electric automobile;
and the trip path decision model comprises a foreground value function and a decision weight function, and the foreground value of the electric vehicle alternative path selected by the user in a limited manner is obtained.
Further, the urban area involved in the trip chain includes a residential area, a working area and a functional area, and the residential area is used as the origin and destination of the electric vehicle.
Further, fitting the initial departure time of the electric automobile by adopting a lognormal distribution function.
Further, a travel chain is solved through a Floyd algorithm, and an alternative path, a running time and an arrival time of the electric automobile from a starting point to a destination are determined.
Further, the electric vehicle charging behavior model comprises a charging condition model and a charging duration model;
judging whether the electric automobile needs to be charged or not through the charging condition model;
and calculating the charging time of the electric automobile through the charging time model.
Further, when the charging condition model calculates that the remaining electric quantity of the electric automobile cannot meet the remaining mileage or the electric quantity of the electric automobile is lower than a set threshold value after the electric automobile arrives at the destination, it is determined that the electric automobile needs to be charged.
Further, the specific process of obtaining the space-time distribution of the electric vehicle is as follows:
(1) initializing a trip chain model;
(2) determining a starting point of the electric automobile, and determining a destination of the electric automobile according to the spatial transition probability of the trip chain;
(3) obtaining an alternative path between an initial point and a destination according to a Floyd algorithm, calculating the foreground value of the alternative path according to a travel path decision model, selecting the path with the maximum foreground value as a travel path, and generating the travel distance, the travel time and the residual electric quantity when the electric vehicle reaches the destination under the travel path;
(4) judging whether the electric automobile needs to be charged according to the charging condition model, and calculating the charging time and the position of the electric automobile when the electric automobile needs to be charged;
(5) judging whether the destination is the end point of a trip in one day, if so, executing (6), and if not, executing (2);
(6) and generating the space-time distribution of the charging load of the electric automobile through the acquired position and charging time of the electric automobile.
The embodiment discloses an electric vehicle charging station site selection method considering limited user travel. Firstly, constructing a space-time distribution model of the electric automobile based on a trip chain, considering incomplete cognition of a user on information such as traffic conditions, charging electricity prices, distances between the current electric automobile and a charging station and the like, analyzing the selection of the user on a trip path based on rationality, and combining charging behavior analysis such as charging conditions, charging duration and the like of the electric automobile to obtain the space-time distribution of the charging load of the electric automobile; then, with the minimum number of the charging stations as a target, constructing a charging station site selection optimization model, and specifically comprising the following steps:
firstly, a space-time distribution model of the electric automobile is constructed based on a trip chain. The travel chain links the individual's travel and activities throughout the day, reflecting the temporal and spatial evolution that occurs to complete a series of activities. And integrating limited rational analysis in the decision-making of the user travel path, and describing data such as initial departure time, times of participating in activities, activity types, transfer among activities and the like of the electric vehicle user by using a travel chain to construct a space-time distribution model of the electric vehicle.
(1.1) user travel chain analysis
According to the similarity of the urban regional function division and the regional power failure duration, the urban space can be divided into three categories, namely a residential area (H), a working area (W) and a functional area (S). Therefore, the travel behavior of the electric vehicle user in one day can be represented by the mutual transfer process among the three major areas. This patent uses the residential area to come and go the origin to destination as the vehicle, for simplifying the analysis, assumes that electric automobile user trip chain mainly includes two kinds of modes: (1) travel onlyThere are 2 cases when there is a purpose to go to a work area or a functional area; (2) trip includes two purposes, goes to the workspace before, goes to the functional area again or goes to the functional area before, goes to the workspace again, has 2 kinds of situations. Assuming that the number of times of day activity schedule of user i is n, his trip chain can be represented as fin：
fin＝(gi1,gi2,...,gin)
Wherein, gijA feature set representing an ith user participating in a jth activity.
(1.2) analysis of spatiotemporal characteristics of trip chain
And (1.2.1) starting time and parking time.
The initial starting time of the electric automobile is mainly concentrated at 7:00-8:30 in the morning, and a lognormal distribution function is adopted to fit the initial starting time of the electric automobile.
Where x denotes the departure time, f (x) denotes the probability, and the parameters μ and σ2The method is related to the traveling habits of urban electric vehicle users and can be determined according to the statistical data of research.
The parking time is related to the activity type, and for simplifying analysis, the parking time can be estimated through the statistical data of the parking time of urban population in a residential area, a working area and a functional area, and is respectively assigned with a constant.
(1.2.2) spatial transition probability
Travelling chain finIncluding activities of the user i within a day, the transitions between activities may be described using a markov chain. I.e. the destination of the electric vehicle, is only related to the previous destination, and is recorded with pijIs in slave state EiIs converted into EjThe state transition probability of (2) can construct a destination transition probability matrix of the electric automobile
Wherein p isijThe following conditions are satisfied:
(1.2.3) distance traveled and time traveled
If the departure place and the destination of the electric vehicle are different, the driving distance of the electric vehicle is different. Slave state E with Markov chainiIs converted into EjCorresponding to the state transition, the driving distance is recorded as lijThen a travel distance matrix may be constructed:
the travel time is related to the travel distance, and the embodiment passes through the travel time tijDistance l from the vehicleijThe relationship of (1):
constructing a travel time matrix:
(1.3) travel path decision model based on user finite rationality analysis
(1.3.1) modeling of Foreground cost function and decision weight function
The foreground theory considers user finiteness and makes decisions through two stages of editing and evaluating. And in the editing stage, a user sets a trip reference point based on the cognition of the traffic network information, trip time constraint and the like, measures a 'profit' and 'loss' function of the foreground set on the basis of the reference point, forms subjective probability weight, and expresses the foreground value by a value function of each foreground and expectation of the subjective weight multiplier in the evaluation stage. At present, price function and decision weight research shows that a person shows certain deterministic effect (facing income, when the probability of two results is larger, the risk of an investor is aversive, when the probability of two results is smaller, the risk of the investor is chased) and reflective effect (facing loss, when the probability of two results is larger, the risk is better, when the probability of two results is smaller, the risk is avoided), and the finite rationality of decision can be described by an S-shaped value function and an inverted S-shaped decision weight function.
The foreground x-cost function and the decision weight function may be expressed as:
in the formula, x0As a reference point, 0<α,β<1 embodies the decision maker's preference level for risk,
and l represents the degree of curvature of the decision weight, pxTo select the objective weight of the foreground x, g (x) is the cost function of the foreground x, w+() As a function of the probability weight of revenue, w-() Is a loss probability weight function.
(1.3.2) travel route selection
The reference point directly influences the value function and user decision weight of each foreground, and the acceptable earliest arrival time T is proposed in the prior researchaAnd activity start time Ts"reference points as a function of value, between which there is a T" gain ", and" loss ", otherwisebMaximizing the revenue the user receives when it arrives at the moment.
This embodiment uses TbAs a reference point for path selection, the time distance T of the user reaching the activity place under the alternative pathbThe more recent, the higher the corresponding profit. When the Markov chain is from state EiIs converted into EjIs determined based on Floyd algorithmSet of candidate paths RijAnd calculating the travel time t to the activity point through the alternative pathxThe arrival time T can be determined by combining the departure time of the activityxThen, r of each candidate path is calculated according to the following formulaxAnd selecting the path with the highest value as the travel path.
Vrx＝g(rx)*w(p(rx))
In the formula (I), the compound is shown in the specification,
is a path rxForeground value of g (r)x) Is a path rxW (r) ofx) Is a path r)xSubjective weight of (1).
And secondly, obtaining the space-time distribution condition of the charging load of the electric automobile. The present embodiment sets the charging conditions as follows: (1) the remaining electric quantity cannot meet the remaining mileage in the driving process; (2) after the user arrives at the activity place, the residual electric quantity of the electric automobile is lower than the threshold value. The charging duration is the time from the current residual capacity to full charge of the electric automobile. And finally, the space-time distribution condition of the charging load of the electric automobile is obtained by combining the space-time distribution of the electric automobile and the charging behavior of the electric automobile.
(2.1) electric vehicle charging behavior analysis
(2.1.1) charging conditions
At present, there are modes such as one charge a day and one charge a plurality of days in the electric vehicle charging mode, but actually the user often produces the charging demand in the driving process, and the charging condition is set in this embodiment as follows: (a) the remaining capacity in the driving process cannot meet the remaining mileage, and the mileage anxiety of an electric vehicle user is considered, so that the remaining capacity of the battery is not lower than 10% when the battery reaches the next charging station; (b) after the user arrives at the activity place, the residual electric quantity of the electric automobile is lower than a threshold epsilon.
The charging condition model is expressed as follows:
Ce*SOCi-celij<0.1Ce
SOCi*Ce<ε
in the formula, CeFor the battery capacity, SOC of the electric vehicleiTo the remaining battery capacity after reaching destination i (in percent), ceFor the power consumption of electric vehicles, |ijThe running distance of the electric automobile.
(2.1.2) charging duration
The charging time is the time from the current residual electric quantity to full charge of the electric automobile and is related to the battery capacity, the battery charge state and the charging power, and the charging time model is as follows:
in the formula, SOCmState of charge of the battery, P, at charging station meFor charging power, TchIs the charging period.
(2.2) space-time distribution of charging load of electric vehicle
Firstly, establishing a space-time distribution model of the electric automobile based on a trip chain, and analyzing information such as initial departure time, driving destination, parking duration and the like of the electric automobile; then, selecting the most travel path during activity transfer based on a travel path decision method of user rational analysis; and finally, combining the space-time distribution of the electric automobile and the charging behavior of the electric automobile to obtain the space-time distribution condition of the charging load of the electric automobile. The charging load distribution of the electric vehicle considering the limited trip of the user can be obtained according to the following steps:
(1) initializing the departure time t of an electric automobile user i0And the length j of the initialization trip chain is 0, and the number of times of activity scheduling in one day is n.
(2) The travel chain length j is j +1, and the living area (H) is used as the travel starting point djGenerating the next destination d of the electric automobile according to the state transition matrixj+1。
(3) Obtaining d based on Floyd algorithmjAnd dj+1Alternative path Rj(j+1)Calculating each alternative path rxSelecting the path with the maximum foreground value as the travel path, and generating the travel distance l under the travel pathj(j+1)Calculating the arrival dj+1Time t ofj+1And SOCj+1。
(4) According to the charging condition of the electric automobile, judging that the electric automobile is in dj+1Whether charging is required. If charging is needed, calculating the charging time and recording the coordinates (x) of the electric automobilej,yj)。
(5) Judgment of dj+1Whether the terminal point is a trip end point of one day or not, and if the terminal point is the trip end point, ending the simulation; if not, go to step (2).
(6) And (5) simulating all electric vehicle users in the steps (1) to (5), and generating the space-time distribution of the electric vehicle charging load based on the simulation result.
And thirdly, selecting an optimal station address from the candidate point set of the station addresses. The charging station address selection needs to consider the charging load distribution condition of the electric vehicles, cover the charging requirements of the electric vehicles in the area, enhance the mileage confidence of users, and select the optimal charging station address by taking the minimum construction quantity of the comprehensive charging stations as a target.
Determining a set Q of electric vehicle charging demand points based on the obtained space-time distribution situation of the electric vehicle charging loadeDefinition of NeIs the number of elements in the set of charge demand points. Definition of QDSet of candidate points for charging station address, NDIs the number of elements in the candidate point set. Suppose for any j ∈ Qe，i∈QDIf the electric vehicle j can drive to the candidate point i in the remaining electric quantity, the candidate point i can meet the charging requirement of j, and a relation matrix of the demand point and the candidate point can be defined as follows:
taking the minimum number of the charging stations as an objective function, and introducing a decision variable
If a charging station is built at candidate point i, zi1, otherwise ziThe objective function can be expressed as:
the charging station address selection needs to ensure that at least one charging station meets the charging requirement of any electric vehicle, and then the constraint condition can be expressed as:
and solving the problem of converting the model solution into an integer linear programming, directly solving the problem to obtain a result based on a solver, and determining the address of the charging station.
This openly when electric automobile charging station siting, the trip route has been selected based on user's limited rational analysis, and the spatial and temporal distribution of electric automobile charging load has been confirmed according to electric automobile's the action of charging, and then regard charging station construction quantity as the target at least, the charging station address has been confirmed, guarantee the user actual conditions of laminating more of trip based on limited rational analysis, thereby reflect electric automobile actual charging demand, under the condition that satisfies user's demand of charging, guarantee that the charging station quantity of construction is minimum, the utilization ratio of charging station has been improved, and the cost is reduced.
Example 2
In this embodiment, an electric vehicle charging station location system considering limited user travel is disclosed, including:
the data acquisition module is used for acquiring traffic information and user information in the city to be planned;
the data processing module is used for inputting the traffic information and the user information into a charging station address planning model and solving to obtain a charging station address; the charging station address planning model is used for selecting a travel path based on user limitation, determining the space-time distribution of the charging load of the electric automobile by combining the charging behavior of the electric automobile, determining the charging demand points of the electric automobile according to the space-time distribution of the charging load of the electric automobile, and acquiring the charging station address by taking the minimum construction quantity of the charging stations as a target and the charging demand of any electric automobile satisfied by at least one charging station as a constraint condition.
Example 3
In this embodiment, a computer readable storage medium is disclosed for storing computer instructions which, when executed by a processor, perform the steps of the electric vehicle charging station location method considering limited user travel.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. An electric vehicle charging station site selection method considering limited user trip is characterized by comprising the following steps:
acquiring traffic information and user information in a city to be planned;
inputting the traffic information and the user information into a charging station address planning model, and solving to obtain a charging station address;
the charging station address planning model is used for selecting a travel path based on user limitation, determining the space-time distribution of the charging load of the electric automobile by combining the charging behavior of the electric automobile, determining the charging demand points of the electric automobile according to the space-time distribution of the charging load of the electric automobile, and acquiring the charging station address by taking the minimum construction quantity of the charging stations as a target and the charging demand of any electric automobile satisfied by at least one charging station as a constraint condition.
2. The electric vehicle charging station location method considering user limited rational travel as claimed in claim 1, it is characterized in that the charging station address planning model comprises an electric automobile space-time distribution model and an electric automobile charging behavior model, wherein, the electric vehicle space-time distribution model obtains the travel path of the electric vehicle and the space-time distribution of the electric vehicle by taking the prospect value of the electric vehicle alternative path selected by the user in a limited way as the maximum target, combines the space-time distribution of the electric vehicle and the electric vehicle charging behavior model to obtain the space-time distribution of the charging load of the electric vehicle, the charging demand points of the electric automobile are determined according to the space-time distribution of the charging load of the electric automobile, the charging demand of any electric automobile is met by at least one charging station serving as a constraint condition, and the address of the charging station is obtained by taking the minimum construction quantity of the charging stations as a target.
3. The method as claimed in claim 2, wherein the electric vehicle charging station site selection method considering user limited rational travel is characterized in that the electric vehicle space-time distribution model comprises a travel chain and travel path decision model according with the actual travel rule of the electric vehicle, and the space-time distribution condition of the electric vehicle is obtained with the maximum prospect value of the electric vehicle alternative path selected by the user limited rational as a target;
the trip chain comprises the starting time, the parking time, the space transfer probability, the driving distance and the driving time of the electric automobile which embody the space-time characteristics of the electric automobile;
and the trip path decision model comprises a foreground value function and a decision weight function, and the foreground value of the electric vehicle alternative path selected by the user in a limited manner is obtained.
4. The method of locating electric vehicle charging stations considering limited user travel according to claim 3, wherein the urban area involved in the travel chain includes residential areas, work areas and functional areas, and the residential areas are used as the origin and destination of the electric vehicles.
5. The method of locating electric vehicle charging stations with limited user travel taken into account as set forth in claim 3, wherein a lognormal distribution function is used to fit the starting departure time of the electric vehicle.
6. The method for locating an electric vehicle charging station considering limited rational travel of a user according to claim 3, wherein a travel chain is solved through a Floyd algorithm, and an alternative path, a travel time and an arrival time of the electric vehicle from a starting point to a destination are determined.
7. The method of locating a charging station of an electric vehicle considering limited rational travel of a user as set forth in claim 2, wherein the charging behavior model of the electric vehicle includes a charging condition model and a charging duration model;
judging whether the electric automobile needs to be charged or not through the charging condition model;
and calculating the charging time of the electric automobile through the charging time model.
8. The method of claim 3, wherein the specific process of obtaining the space-time distribution of the electric vehicle comprises:
(1) initializing a trip chain model;
(2) determining a starting point of the electric automobile, and determining a destination of the electric automobile according to the spatial transition probability of the trip chain;
(3) obtaining an alternative path between an initial point and a destination, calculating the foreground value of the alternative path according to a travel path decision model, selecting the path with the maximum foreground value as a travel path, and generating the travel distance, the travel time and the residual electric quantity when the electric vehicle reaches the destination under the travel path;
(4) judging whether the electric automobile needs to be charged or not according to the electric automobile charging behavior model, and calculating charging time and the position of the electric automobile when the electric automobile needs to be charged;
(5) judging whether the destination is the end point of a trip in one day, if so, executing (6), and if not, executing (2);
(6) and generating the space-time distribution of the charging load of the electric automobile through the acquired position and charging time of the electric automobile.
9. Electric automobile charging station site selection system of user limited rational trip is considered, its characterized in that includes:
the data acquisition module is used for acquiring traffic information and user information in the city to be planned;
the data processing module is used for inputting the traffic information and the user information into a charging station address planning model and solving to obtain a charging station address; the charging station address planning model is used for selecting a travel path based on user limitation, determining the space-time distribution of the charging load of the electric automobile by combining the charging behavior of the electric automobile, determining the charging demand points of the electric automobile according to the space-time distribution of the charging load of the electric automobile, and acquiring the charging station address by taking the minimum construction quantity of the charging stations as a target and the charging demand of any electric automobile satisfied by at least one charging station as a constraint condition.
10. A computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the method for electric vehicle charging station location considering limited user travel according to any one of claims 1 to 8.
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