CN107145983A - A kind of electric automobile charging station site selecting method based on city virtual traffic platform - Google Patents
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Abstract
The invention discloses a kind of electric automobile charging station site selecting method based on city virtual traffic platform, this method is by building city virtual traffic platform, and the road traffic flow that urban transportation distribution obtains city is carried out based on the platform, the representative magnitude of traffic flow is calculated, the address of electric automobile charging station is determined.It is excessively random that this method has abandoned conventional city electric car charging station addressing, and charging station location multidigit is the problem of city is from far-off regions, electric automobile charging station is positioned over the region that vehicle most often drives through, lifting electric automobile utilization rate is of great immediate significance.
Description
Technical Field
The invention belongs to the field of urban traffic planning and optimization, and particularly relates to an electric vehicle charging station site selection method based on an urban virtual traffic platform.
Background
The electric automobile is a vehicle which takes a vehicle-mounted power supply as power and drives wheels by a motor to run, and meets various requirements of road traffic and safety regulations. The influence on the environment is smaller than that of the traditional automobile, so that the prospect of the electric automobile is widely seen, but the development of the electric automobile in China is still in the starting stage at present, and the related technical standard is not mature. From the energy source perspective of electric automobile use, electric automobile can be subdivided into pure electric vehicles, hybrid electric vehicles, fuel cell vehicles.
In order to encourage the development of electric vehicles, state and local governments have also issued a series of policy and regulations in recent years, such as "notification about 2016 + 2020 policy for supporting the popularization and application of new energy vehicles," new energy vehicle manufacturing enterprise and product admission management rules (revised edition of survey papers) "," notification about accelerating the construction of electric vehicle charging infrastructure in residential areas ", and the like. The policy and regulation play an important role in popularizing the electric automobile, and the holding quantity of new energy automobiles in cities is greatly improved.
However, in an actual city, the number of charging facilities (charging stations, charging piles, etc.) and the layout of the facilities greatly limit the utilization rate of electric vehicles, and many owners of electric vehicles consider the convenience of charging facilities along a trip line before using the electric vehicles. If charging is inconvenient, the user often abandons the use of the electric automobile and goes out in other modes. The invention is provided on the background, and realizes the optimization of charging station site selection by virtue of the virtual reappearance of the urban virtual traffic platform to the urban traffic problem and based on a large amount of actual measurement data of traffic and urban development.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems that in the prior art, pile layout of an urban charging station is unreasonable and site selection is remote, so that the use of electric automobiles is not facilitated, the invention provides the site selection method of the electric automobile charging station based on the urban virtual traffic platform.
The technical scheme is as follows: in order to achieve the above object, the electric vehicle charging station address selecting method based on the urban virtual traffic platform in the invention acquires the road traffic flow of the city by constructing the urban virtual traffic platform and performing urban traffic distribution based on the platform, and determines the address of the electric vehicle charging station according to the sequence of the road traffic flow, which specifically comprises 5 steps: step A) constructing an urban virtual traffic platform, step B) carrying out traffic distribution based on the urban virtual traffic platform, recording the traffic flow of each road section, step C) calculating the representative traffic flow, step D) determining the number of charging stations, and step E) determining the addresses of the charging stations.
A) Constructing an urban virtual traffic platform;
the method comprises the steps of collecting urban road network information, traffic management information, public traffic network information, traffic demand information and traffic cell information, converting the collected information into a data file according with a 'star of transit-TransStar' software rule, and leading the converted data file into a 'star of transit-TransStar' software database through a 'traffic network basic database establishing module' in 'star of transit-TransStar' software to complete the construction of an urban virtual traffic platform;
the urban road network information comprises the positions of traffic nodes, an adjacent catalogue of a traffic network, the length of each road section in the traffic network, the number of lanes of each road section in the traffic network and the actual traffic capacity of each road section in the traffic network, and each item of data of the urban road network information can be acquired by a planning department, a traffic management department, a traffic design department and a homeland department of a city; the traffic management information comprises node traffic management modes and ranges of various traffic management modes, and various data of the traffic management information can be acquired by a traffic management department; the public transportation network information comprises the general public transportation line trend and the public transportation station position, the rapid public transportation line trend and the public transportation station position, the rail transportation line trend and the station position, and all data of the public transportation network information can be acquired by a public transportation company, a subway operation company and a traffic management department; the traffic demand information contains nine common travel purposes for resident travel: working, going to school, business affairs work, shopping, cultural and physical activities, visiting friends, seeing doctor and hospitalizing, returning trip, and others, and the daily trip times of each trip purpose and the six common trip modes of resident trip: the system comprises a plurality of traffic demand information sets, a plurality of traffic demand information sets and a plurality of data sets, wherein the traffic demand information sets comprise a plurality of traffic demand information sets, each traffic demand information set comprises a plurality of traffic demand information sets and a plurality of data sets; the traffic cell information comprises the boundary of each traffic cell and all road traffic nodes in each traffic cell, and each item of data of the traffic cell information can be directly acquired by a planning department or acquired by a mode of autonomously dividing the traffic cells.
B) Carrying out traffic distribution based on the urban virtual traffic platform, and recording the traffic flow of each road section;
and B) carrying out traffic distribution on the car travel demand in the city by adopting the urban virtual traffic platform constructed in the step A), and recording the traffic flow of each road section after distribution. The recorded traffic flow includes: peak hour traffic flow for road segment iAnd the all-day traffic flow of the link iThe subscript i represents the serial number of the road sections, i is an integer greater than 0 and is not greater than N, and N is the total number of the road sections;
C) calculating representative traffic flow;
calculating the traffic flow of each road section obtained in the step B) according to the following formula to obtain the representative traffic flow q of each road sectioni：
D) Determining the number of charging stations;
and determining the number M of the charging stations needing location selection according to the charging requirement of the city. The number of charging stations M may be determined by:wherein,Dcmaximum charge demand for electric vehicles during peak hours in cities, DcElectric automobile holdup size P capable of passing through citycAnd the daily average charging frequency C of the electric automobilecHigh peak hour coefficient F for charging electric automobilecCalculation acquisition, Dc＝PcCcFcWherein the peak hour coefficient F of the electric vehicle chargingcRatio of charging demand of electric vehicle to total charging demand of full day for peak hour, CaThe maximum charging requirement which can be met by a single charging station in a unit hour;
E) determining an address of a charging station;
the address of the charging station can be obtained by solving the following formula through a genetic algorithm:
wherein d isijManhattan distance, d, from the geometric centroid of road segment i to the geometric centroid of road segment jminIs the minimum allowable distance between charging stations. For section i, when γiIf 1, a charging station is arranged on the road section; otherwise when gamma isiIf 0, then no charging stations will be located on that route.
Has the advantages that: the electric automobile charging station address selecting method based on the urban virtual traffic platform acquires the road section traffic flow of the city by constructing the urban virtual traffic platform and performing urban traffic distribution based on the urban virtual traffic platform, and determines the address of the electric automobile charging station according to the sequence of the road section traffic flow. The method of the invention abandons the prior scheme that the urban charging station has too random site selection and the charging station positions are located in the remote areas of the city, and the method can obtain and determine the important traffic areas of the city that the positions of the charging station positions are both located in the large traffic demand and the large charging demand; the method relies on the virtual traffic platform and traffic big data, and can easily realize the synchronous update of the address selection information of the charging station by modifying the parameter values in the virtual traffic platform along with the development of the city.
Drawings
FIG. 1 is a flow chart of an electric vehicle charging station site selection method based on an urban virtual traffic platform according to the present invention;
FIG. 2 shows the results of traffic distribution using the "Star of Commodity-TranStar" software of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
In the invention, a city and a peripheral parcel thereof in China are selected, and the provided electric vehicle charging station site selection method based on the city virtual traffic platform is verified, wherein the city has a total number of main road sections N of 2124. As shown in fig. 1, the main steps of the method of the present invention are as follows:
A) adopting software of 'star of transit-TransStar' to construct an urban virtual traffic platform;
the method comprises the steps of collecting urban road network information, traffic management information, public traffic network information, traffic demand information and traffic cell information, converting the collected information into a data file according with the software regulation of 'star of transit-TransStar', and leading the data file into a database of 'star of transit-TransStar' software through a 'traffic network basic database establishing module' in the 'star of transit-TransStar' software to form an urban virtual traffic platform;
B) carrying out traffic distribution based on the urban virtual traffic platform, and recording the traffic flow of each road section;
and B) carrying out traffic distribution on the car travel demand in the city by adopting the city virtual traffic platform constructed in the step A), wherein the traffic distribution method is capacity limit-shortest route distribution, namely, distribution is carried out according to the shortest travel total route under the condition of the capacity of the road sections, and the traffic flow of each road section after distribution is recorded. The recorded traffic flow includes: peak hour traffic flowAnd total daily traffic flowThe result of the allocation is shown in fig. 2, the lines in the graph represent the traffic flow of the road sections, and the thicker the lines, the larger the traffic flow;
C) calculating representative traffic flow;
calculating the traffic flow of each road section obtained in the step B) according to the following formula to obtain a representative traffic flow:after calculation, representative traffic flow for 2124 road segments was obtained. The first five road segments representing the maximum intercourse traffic are the road segment serial numbers 1471, 1476, 1466, 97 and 1452.
D. Determining the number of charging stations;
according to the charging requirement of a city, the number of charging stations needing location selection is determined to be M-3.
E. Determining an address of a charging station;
the address of the charging station can be obtained by solving the following formula through a genetic algorithm:
wherein d isijManhattan distance, d, from the geometric centroid of road segment i to the geometric centroid of road segment jminIs the minimum distance between charging stations. For section i, when γiIf 1, a charging station is arranged on the road section; otherwise when gamma isiIf 0, then no charging stations will be located on that route.
The final charging station location, located along the route 1471, 1476, 1466, is obtained by computer programming and solving the above equation with a genetic algorithm.
The above is only a preferred embodiment of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (4)
1. An electric vehicle charging station site selection method based on an urban virtual traffic platform is characterized by comprising the following steps:
(1) collecting urban road network information, traffic management information, public traffic network information, traffic demand information and traffic community information, and constructing an urban virtual traffic platform by using the collected information;
(2) carrying out traffic distribution based on the urban virtual traffic platform, recording the traffic flow of each road section, and for a certain road section, the traffic flow comprises the peak hour traffic flow and the full day traffic flow of the road section;
(3) calculating representative traffic flow of each road section according to the traffic flow obtained in the step (2);
(4) determining the number of charging stations needing site selection according to the charging requirement of a city;
(5) constructing a constraint problem by taking the maximum sum of representative traffic flow of the road sections set by the charging stations as an optimization target, and solving the constraint problem by utilizing a genetic algorithm to determine the address of each charging station; the constructed constraint problem is as follows:
<mrow> <mi>M</mi> <mi>a</mi> <mi>x</mi> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>&gamma;</mi> <mi>i</mi> </msub> <msub> <mi>q</mi> <mi>i</mi> </msub> </mrow>
<mrow> <mi>s</mi> <mo>.</mo> <mi>t</mi> <mo>.</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>&gamma;</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>&gamma;</mi> <mi>j</mi> </msub> <mo>&Element;</mo> <mo>{</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>}</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>&Element;</mo> <mo>{</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>...</mo> <mi>N</mi> <mo>}</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>&gamma;</mi> <mi>i</mi> </msub> <mo>=</mo> <mi>M</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&GreaterEqual;</mo> <msub> <mi>&lambda;</mi> <mi>j</mi> </msub> <msub> <mi>d</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&lambda;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&gamma;</mi> <mi>i</mi> </msub> <msub> <mi>&gamma;</mi> <mi>j</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
wherein q isiRepresentative traffic flow for road section i, N is number of road sections, gammaiFor characterizing whether a section i is provided with charging stations, gammajThe method is used for representing whether charging stations are arranged on the road section j or not, M is the number of the charging stations needing address selection, and dijManhattan distance, d, from the geometric centroid of road segment i to the geometric centroid of road segment jminIs the minimum allowable distance between charging stations.
2. The urban virtual transportation platform-based electric vehicle charging station addressing method according to claim 1, wherein the urban road network information in step (1) comprises positions of transportation nodes, an adjacency list table of a transportation network, lengths of each road segment in the transportation network, the number of lanes of each road segment in the transportation network, and actual traffic capacity of each road segment in the transportation network; the traffic management information comprises node traffic management modes and ranges of various traffic management modes; the public transportation network information comprises the direction of a common bus line and the position of a bus station, the direction of a rapid bus line and the position of a bus station, and the direction of a rail transit line and the position of a station; the traffic demand information comprises the travel purpose of residents, the number of residents for each travel purpose, the travel mode of residents, the number of residents for each travel mode, the population number of each traffic cell, and the land utilization type and area of each traffic cell; the traffic cell information includes the boundary of each traffic cell and all road traffic nodes in each traffic cell.
3. The urban virtual transportation platform-based electric vehicle charging station location method according to claim 1, wherein the representative traffic flow q represents a traffic flow for a road section iiThe calculation formula of (2) is as follows:
<mrow> <msub> <mi>q</mi> <mi>i</mi> </msub> <mo>=</mo> <msubsup> <mi>q</mi> <mi>i</mi> <mi>P</mi> </msubsup> <mo>+</mo> <msubsup> <mi>q</mi> <mi>i</mi> <mrow> <mi>A</mi> <mi>l</mi> <mi>l</mi> </mrow> </msubsup> <mo>/</mo> <mn>12</mn> <mo>,</mo> </mrow>
is the peak hour traffic flow for road segment i,is the traffic flow for the link i throughout the day.
4. The urban virtual transportation platform-based electric vehicle charging station site selection method according to claim 1, wherein the calculation formula of the number of charging stations M is as follows:
<mrow> <mi>M</mi> <mo>=</mo> <mfrac> <msub> <mi>D</mi> <mi>c</mi> </msub> <msub> <mi>C</mi> <mi>a</mi> </msub> </mfrac> <mo>,</mo> </mrow>1
wherein D iscMaximum charge demand for electric vehicles during peak hours in cities, CaIs the maximum charging requirement that can be met by a single charging station in a unit hour.
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Cited By (7)
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| CN109508828A (en) * | 2018-11-15 | 2019-03-22 | 河南城建学院 | The calculation method of trip distance in a kind of area |
| CN110009205A (en) * | 2019-03-21 | 2019-07-12 | 东南大学 | A kind of model split and method of traffic assignment of regional complex traffic integrated |
| CN111695942A (en) * | 2020-06-17 | 2020-09-22 | 云南省设计院集团有限公司 | Electric vehicle charging station site selection method based on time reliability |
| CN116385062A (en) * | 2023-06-06 | 2023-07-04 | 和元达信息科技有限公司 | Store area site selection determining method and system based on big data |
| CN116777517A (en) * | 2023-07-27 | 2023-09-19 | 苏州德博新能源有限公司 | Battery box position determining method |
| CN118380122A (en) * | 2024-05-29 | 2024-07-23 | 山东松颉数字科技有限公司 | Big data cross analysis overall operation management system |
| CN118380122B (en) * | 2024-05-29 | 2024-10-29 | 北京普华基业科技发展有限公司 | Big data cross analysis overall operation management system |
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| CN106503845A (en) * | 2016-10-21 | 2017-03-15 | 国网山东省电力公司烟台供电公司 | A kind of charging station method of allocation plan that is schemed based on V with HS algorithms |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109508828A (en) * | 2018-11-15 | 2019-03-22 | 河南城建学院 | The calculation method of trip distance in a kind of area |
| CN109508828B (en) * | 2018-11-15 | 2021-04-23 | 河南城建学院 | Method for determining travel distance in area |
| CN110009205A (en) * | 2019-03-21 | 2019-07-12 | 东南大学 | A kind of model split and method of traffic assignment of regional complex traffic integrated |
| CN110009205B (en) * | 2019-03-21 | 2021-08-03 | 东南大学 | Regional comprehensive traffic integrated mode division and traffic distribution method |
| CN111695942A (en) * | 2020-06-17 | 2020-09-22 | 云南省设计院集团有限公司 | Electric vehicle charging station site selection method based on time reliability |
| CN116385062A (en) * | 2023-06-06 | 2023-07-04 | 和元达信息科技有限公司 | Store area site selection determining method and system based on big data |
| CN116385062B (en) * | 2023-06-06 | 2023-09-19 | 和元达信息科技有限公司 | Store area site selection determining method and system based on big data |
| CN116777517A (en) * | 2023-07-27 | 2023-09-19 | 苏州德博新能源有限公司 | Battery box position determining method |
| CN116777517B (en) * | 2023-07-27 | 2024-06-04 | 苏州德博新能源有限公司 | Battery box position determining method |
| CN118380122A (en) * | 2024-05-29 | 2024-07-23 | 山东松颉数字科技有限公司 | Big data cross analysis overall operation management system |
| CN118380122B (en) * | 2024-05-29 | 2024-10-29 | 北京普华基业科技发展有限公司 | Big data cross analysis overall operation management system |
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Application publication date: 20170908 |