CN112199384A - Electric vehicle charging station accessibility evaluation method - Google Patents
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Abstract
The invention discloses a reachability evaluation method for an electric vehicle charging station, which is characterized by comprising the following steps: carrying out spatial modeling on a research area to determine the spatial distribution position of demand points of the electric vehicle charging service; carrying out spatial discretization modeling on a research area at certain intervals, and taking obtained discrete points as electric automobile charging service demand points of the research area; secondly, the method overcomes the defect that the method for estimating the charging service demand point of the electric automobile according to the maximum bearing distance of the cruising ability of the electric automobile in the prior art is inconsistent with the behavior habit of people using the electric automobile; the method has the advantage that the latest concept of human trip behavior activities, namely the concept of '15-minute life circle', is introduced into the reachability evaluation research of the electric vehicle charging station.
Description
Technical Field
The invention relates to the technical field of electric automobile charging stations, in particular to a reachability evaluation method for an electric automobile charging station.
Background
1) Development of electric automobile industry
The global climate deterioration becomes a problem to be solved in the world of this century; in 2016, 170, multiple national leaders in the headquarters of the united nations of new york signed the climate change agreement paris agreement, the global average temperature was controlled within 2 ℃ of the rise range of the previous industrialization period, and the temperature rise range was controlled within 1.5 ℃ of the effort to be taken as a long-term target for changing climate change.
China actively promotes Paris climate agreement and proposes one hundred percent of agreement commitments to be honored in 2020; meanwhile, global climate change and green low-carbon development are taken as important subjects of early research; therefore, the new energy electric vehicle with the characteristics of low emission, low noise and the like gradually enters the visual field of people and becomes the key for dealing with global climate problems.
In 2019, the Ministry of industry and communications released a survey of development planning of New energy vehicles industry (2021-.
By 2025 years, the market competitiveness of new energy automobiles is obviously improved, and the sales volume accounts for 20 percent of the total sales volume of the automobiles in the current year;
by 2030, the new energy automobile forms market competitive advantage, and the sales volume accounts for 40% of the total sales volume of the automobile in the current year;
meanwhile, international automobile enterprises also put forward a green solution, so that the carbon emission is reduced from the whole process of energy production, storage, consumption and the like, and the aim of carbon neutralization close to zero carbon emission is achieved; therefore, the number and the technology of the new energy electric automobiles occupy an important position in the automobile industry.
The energy-saving and environment-friendly characteristics of the electric automobile have important significance in improving global climate problems, guaranteeing national energy safety and reducing dependence of China on petroleum import, and the construction of an electric automobile charging station is an important factor for promoting the development of the electric automobile.
The convenience degree of the charging station determines the desire of people to purchase electric vehicles, so the spatial location of the charging station of the electric vehicles directly influences the popularization of the electric vehicles.
With the rapid development of the new energy automobile industry, the problem of space site selection of the electric automobile charging station is concerned by a plurality of scholars at home and abroad; relevant scholars develop researches on site selection and space optimization configuration of electric vehicle charging stations from the aspects of electrical engineering, transportation, urban planning and the like, and obtain a large number of research results.
At present, a site selection and optimization configuration model of an electric vehicle charging station is mainly constructed from the field of electrical engineering in China on the basis of power grid layout economic cost, electric vehicle charging requirements and charging cost and on the basis of game theory, intelligent optimization algorithm and the like.
2) Electric vehicle charging station accessibility evaluation method
The accessibility in the geographic information system refers to the difficulty of reaching a destination from any point in space, and reflects the size of space resistance overcome in the process of reaching the destination, and is measured by indexes such as common distance, time and the like.
The accessibility of the electric vehicle charging station is a decisive factor for judging the equalization of the charging service of the electric vehicle, and is an important index for discriminating the surplus or shortage of the resource allocation of the charging service of the electric vehicle; meanwhile, the accessibility is an analysis means of the current situation of the space configuration of the urban electric vehicle charging station and is also a premise for site selection of the urban electric vehicle charging station in the future.
Therefore, based on the reachability theory, the reachability evaluation method for the urban electric vehicle charging station is researched, and a decision basis can be provided for space address selection of the electric vehicle charging station.
In the reachability evaluation method of the electric vehicle charging station, the following three key problems are mainly involved:
(1) determination of electric vehicle charging service demand point
The accessibility of an electric vehicle charging station is usually measured by the distance or transit time between the point of demand and the point of supply for the electric vehicle charging service;
in the accessibility evaluation research of the electric vehicle charging station, currently, few students in China carry out research, and some students carry out research by using the estimated maximum distance borne by the electric vehicle as a determination mode of a charging service demand point of the electric vehicle from the perspective of the cruising ability of the electric vehicle;
when performing reachability research on other public service facilities, the central point of a residential community of a research area is generally used as a demand point of the electric vehicle charging service.
However, for the research on the accessibility of the electric vehicle charging station, the method of taking a residential community as a charging service demand point ignores the demand of electric vehicles outside the community on charging; meanwhile, in geographic space, for cells of different sizes, the spatial position of the whole cell is only represented by a certain point of the cell, which is not in accordance with the actual requirement.
The method for estimating the electric vehicle charging service demand point according to the maximum bearing distance of the cruising ability of the electric vehicle is a method for determining the demand point by using a certain special distance critical node, and the method is inconsistent with the behavior habit of using the electric vehicle by people, for example, most people can select to search for the charging service when the electric quantity is reduced to a certain degree rather than search for the charging service under the condition of the cruising ability limit of the electric vehicle.
Therefore, it is a difficult problem to be solved to find a method for determining a demand point of an electric vehicle charging service, which is more reliable and conforms to human activities.
(2) Research on human travel activities in reachability evaluation
In the reachability evaluation of the electric vehicle charging station, most of the prior scholars use a research area road network created at a certain past definite time point (i.e. a prior map or a prior coordinate pile as a reference point) as a basis for measuring the distance and the transit time of the travel activity of human beings between the electric vehicle charging service demand point and the electric vehicle charging station.
And the behavior of the human being on the trip is generally analyzed by adopting a certain determined trip scheme according to different trip modes such as walking, riding, public transportation and driving, such as adopting a definite route and trip speed.
However, the rapid development of the urbanization process in China makes the urban road network updating speed extremely high, and the time resolution can be in units of minutes; in different road sections on the road network, even if the same trip mode is adopted, the time consumed by the passing estimation is different; therefore, analyzing the travel activities of the human in an explicit travel scheme based on the urban road network created at a certain past time point as a basis for evaluating the travel activities of the human in the reachability of the electric vehicle charging station may cause an error in the evaluation result.
The magnitude of this error is inversely proportional to the time of creation of the road network, i.e.: the earlier the road network creation time is, the more old the road network data is, the larger the error of the evaluation result is, and the later the road network creation time is, the newer the data is, the smaller the reachability evaluation error of the electric vehicle charging station performed by using the data is; meanwhile, the error is in direct proportion to the difference between the human trip scheme and the actual trip activity; the requirement is that the basic road network for the accessibility evaluation of the electric vehicle charging station has stronger timeliness, and the research on the human travel behaviors is more scientific and reasonable.
Therefore, researchers who study the accessibility evaluation of public infrastructures have begun to find a technology that is highly time-efficient for road networks and more realistic for human travel activity estimation.
The rapid development of the internet, map navigation technology and big data mining technology, and the realization of map API technologies such as Baidu, Google, Tencent, sky map, Google and the like provide a new technical support path for the research and application based on location services, and the application of the network map application development API technology has penetrated the fields of games, social contact, E-commerce, travel, sports, intelligent hardware, logistics, real estate, intelligent transportation and the like.
(3) Determination of reachability evaluation index of electric vehicle charging station
Reachability is usually measured by the distance and time between two points; the rapid development of modern urbanization also enables the daily activities of urban residents to pursue high efficiency, and improves the requirement for enjoying urban public facility service; therefore, the theory of '15-minute life circle' is provided by scholars, and the meaning of the theory is that in daily life, the tolerance time of most residents reaching living facilities is 15 minutes; the proposal of the concept is greatly popularized and applied, and a good research effect is achieved; the electric vehicle charging station is used as an electric vehicle charging infrastructure required by residents in daily life, and is suitable for the research concept of '15-minute life circle'.
3) API calling technology for route planning of Gaode map
The Gaode map path planning API is a set of inquiry and driving distance calculation interfaces for walking, public transport and driving provided in the form of HTTP, returns inquiry data in the form of JSON or XML and is used for realizing the development of a path planning function;
by calling the API, the route query of various travel modes (walking, public transportation, driving, riding and truck driving) of human beings can be carried out based on the real-time road network data of the Goodpasts map without displaying map data, and the query result comprises various key information in the travel activities of the human beings, including information such as the distance between a travel starting point and a travel destination, time consumption estimation, route information, a travel scheme and the like; the technology can achieve the purposes of finishing the latest road network data without collecting road network data and fitting the study of human trip activities, namely, taking and using the road network data.
Meanwhile, in recent years, the rapid development of a computer program language Python which is widely popular due to cross-platform, light in magnitude, simple and easy to learn makes the processing, analysis and application research of network big data easier and prompts a large amount of research based on multi-source big data mining and analysis; it becomes easier and easier to invoke the high-resolution map API using Python language to achieve the collection, processing, analysis and application of geographic information.
Therefore, on the premise of the current Chinese development, a method which is suitable for a research concept of '15-minute life circle' and can acquire an actual trip scheme of a human being in real time is urgently needed.
Disclosure of Invention
The invention aims to overcome the defects of the background technology and provides a reachability evaluation method for an electric vehicle charging station.
The purpose of the invention is implemented by the following technical scheme: a reachability evaluation method for an electric vehicle charging station comprises the following steps;
carrying out spatial modeling on a research area to determine the spatial distribution position of demand points of the electric vehicle charging service;
carrying out spatial discretization modeling on a research area at certain intervals, and taking obtained discrete points as electric automobile charging service demand points of the research area;
in order to improve the calculation efficiency, a large-area water area is excluded in advance in the space discretization modeling;
determining the reachable initial range of the electric vehicle charging station;
calculating the actual passing distance and the expected time meeting the requirements of human beings by adopting a neighborhood analysis method, and taking the result larger than the actual service range of the electric vehicle charging station as an initial range;
by using a neighborhood analysis method, an electric vehicle charging service demand point is used as a neighborhood analysis input element, an electric vehicle charging station is used as a neighborhood element, and the charging service charging station provided as the charging service demand point in an estimated range calculated based on a '15-minute living circle' concept and traditional human trip activities is screened;
in order to reduce calculation and ensure scientific effectiveness of results, three electric vehicle charging stations which meet the concept of '15-minute life circle' and the traditional human trip activity are screened out according to the existing electric vehicle charging stations in the region;
therefore, all electric vehicle charging service demand point sets which can be served by each electric vehicle charging station can be determined, and the demand point sets form reachable initial ranges of all electric vehicle charging stations;
according to the concept of '15-minute living circle', searching a neighborhood range which can be reached by driving in 15 minutes as a radius, and searching 3 electric vehicle charging stations which can provide charging service for electric vehicle charging service demand points and are closest to the electric vehicle charging service demand points; the neighborhood search radius is determined and estimated according to the average driving speed of about 30 kilometers per hour in a city, and the linear distance which can be reached in 15 minutes is about 7.5 kilometers; searching three charging stations which can provide charging service for the charging stations within the radius to obtain demand points meeting the conditions and a neighborhood list of the charging stations;
compiling Python codes to call a route planning API (application program interface) of the high-grade map, and searching a charging demand point set of the electric automobile meeting the estimated time of human actual travel within 15 minutes from the neighborhood table obtained in the step two according to the actual road network of the high-grade map;
calculating the actual driving passing time of the electric vehicle charging service demand point (IN _ FID) and the corresponding electric vehicle charging station (NEAR _ FID), and connecting all electric vehicle charging demand points meeting the condition that the estimated passing time is within 15 minutes IN space, so that the serviceable range of all electric vehicle charging stations IN the current city can be obtained.
In the above technical scheme: in the step (I); the spatial discretization interval can be determined according to different characteristics and topographic features of the city.
In the above technical scheme: in the third step; writing Python code calls the gold map path planning API interface.
A, applying for an account number on a high-grade open platform, creating an application for calling a path planning API service, and acquiring a Key for calling corresponding API authorization;
b, installing Python packages required by Python language for data processing, network computing and transmission, wherein the Python packages comprise requests packages, pandas packages, xlrd packages and Json packages; the requests packet is used for the Python program to easily send HTTP/1.1 request information to the website and check a return response; the Pandas package can rapidly provide an accurate and flexible data structure, and simply and intuitively process the structured data; the Xlrd packet is a data processing module of Python based on an Excel table, so that the numbers of the electric vehicle charging service demand points (IN _ FID) and the corresponding electric vehicle charging stations (NEAR _ FID) IN the neighborhood table obtained IN the step 2, and the longitude and latitude and distance information (NEAR _ DIST) of the two points need to be prestored IN the Excel table; the Json packet is used for processing Json data returned by the API;
compiling Python codes, calling a route planning API of the Gade map to process and perform network calculation on the results of the neighborhood table IN the step (2), and outputting the distance between two points between an electric vehicle charging service demand point (IN _ FID) and a corresponding electric vehicle charging station (NEAR _ FID) IN the neighborhood analysis results according to the real-time road network of the Gade map and the driving traffic queried through the route planning API of the Gade map and actual estimated consumed time; the key code is shown in figure 5;
d, screening a point pair set of the electric vehicle charging service demand point (IN _ FID) and the corresponding electric vehicle charging station (NEAR _ FID) within 15 minutes of the actual passing predicted time from the result calculated IN the step C;
and E, connecting all the electric vehicle charging service demand points which can be served by the electric vehicle charging stations in the D acquisition point pair set into pieces, so that the actual service range which can be reached by all the electric vehicle charging stations in the current city in 15 minutes of real-time road network traffic according to the Gade map can be obtained.
In the above technical scheme: in the second step; in order to reduce errors, the neighborhood search radius of the electric vehicle charging service demand point can be properly enlarged to 8 kilometers, 3 charging stations which can provide charging service for the electric vehicle charging service demand point within 8 radiuses are searched, and a neighborhood table is generated.
The invention has the following advantages: 1. in the reachability evaluation method for the electric vehicle charging station, the measurement of the reachability of time and distance between a charging service demand point and a supply point is based on the concept of '15-minute living circle', and the estimated time consumption of driving between two points is used as a time and distance conversion index for the reachability evaluation of the electric vehicle charging station.
2. The method aims to evaluate the accessibility of the electric vehicle charging station, is suitable for calling a high-resolution map path planning API (application programming interface) technology by utilizing Python language, inquiring the actual travel information of the human driving electric vehicle, and estimating the actual distance and time of the human passing between a demand point and a supply point of the electric vehicle.
3. The method is a scientific and effective technical means for solving the problem of insufficient method for determining the demand points by taking a whole research area (not only a single cell as a target point) as a spatial modeling and taking any spatial position point in the research area as the demand point of the electric vehicle charging service.
4. The method utilizes a space discretization modeling mode to construct scientific and reasonable space distribution of the electric automobile charging service demand points.
5. The method calls the real-time updated Goodpasture map path planning API technology by using the Python language, obtains the reachability evaluation result of the electric vehicle charging station, and ensures the timeliness and accuracy of the result.
6. The invention introduces the latest concept of human trip behavior activities, namely the concept of '15-minute life circle', into the reachability evaluation research of the electric vehicle charging station.
7. The method not only provides a new thinking direction for promoting the technological method for evaluating the accessibility of the electric vehicle charging station to develop scientifically, reasonably and in real time, but also enriches the content and method of related research; the invention not only provides a new research idea for the accessibility evaluation of other public service infrastructures, but also provides an important scientific basis for the space layout planning decision of the electric vehicle charging station and other public infrastructures.
Drawings
Fig. 1 is a technical route diagram of the present invention.
Fig. 2 is a schematic diagram of the result of the spatial discretization in the present invention.
FIG. 3 is a partial diagram of discrete points of the electric vehicle charging service requirement according to the present invention.
Fig. 4 is a neighborhood table of the charging demand points and the electric vehicle charging stations according to the present invention.
FIG. 5 shows the key code of the Python height-adjusted map path planning API of the present invention.
FIG. 6 shows the required points that can be reached by the electric vehicle charging station in 15 minutes.
FIG. 7 shows the reachable service range of an electric vehicle charging station in a research area according to the present invention.
FIG. 8 shows the achievable analysis results of the electric vehicle charging station in the research area according to the present invention.
Detailed Description
The embodiments of the present invention will be described in detail with reference to the accompanying drawings, but they are not to be construed as limiting the invention, and are merely illustrative, and the advantages of the invention will be more clearly understood and appreciated by those skilled in the art.
Referring to FIGS. 1-8: a reachability evaluation method for an electric vehicle charging station comprises the following steps;
carrying out spatial modeling on a research area to determine the spatial distribution position of demand points of the electric vehicle charging service;
carrying out spatial discretization modeling on a research area at certain intervals, and taking obtained discrete points as electric automobile charging service demand points of the research area;
in order to improve the space calculation efficiency, a large-area water area needs to be excluded in advance through space discretization modeling;
determining the reachable initial range of the electric vehicle charging station;
calculating the actual passing distance and the expected time of the human being, wherein the actual passing distance and the expected time are met, and the result is larger than the actual service range of the electric vehicle charging station and is used as an initial range by adopting a neighborhood analysis method;
by using a neighborhood analysis method, an electric vehicle charging service demand point is used as a neighborhood analysis input element, an electric vehicle charging station is used as a neighborhood element, and the charging service charging station provided as the charging service demand point in an estimated range calculated based on a '15-minute living circle' concept and traditional human trip activities is screened;
in order to reduce calculation and ensure scientific effectiveness of results, three electric vehicle charging stations which meet the concept of '15-minute life circle' and the traditional human trip activity are screened out according to the existing electric vehicle charging stations in the region;
therefore, all electric vehicle charging service demand point sets which can be served by each electric vehicle charging station can be determined, and the demand point sets form reachable initial ranges of all electric vehicle charging stations;
according to the concept of '15-minute living circle', searching a neighborhood range which can be reached by driving in 15 minutes as a radius, and searching 3 electric vehicle charging stations which can provide charging service for electric vehicle charging service demand points and are closest to the electric vehicle charging service demand points; the neighborhood search radius is determined and estimated according to the average driving speed of about 30 kilometers per hour in a city, and the linear distance which can be reached in 15 minutes is about 7.5 kilometers; searching three charging stations which can provide charging service for the charging stations within the radius to obtain demand points meeting the conditions and a neighborhood list of the charging stations;
compiling Python codes to call a route planning API (application program interface) of the high-grade map, and searching a charging demand point set of the electric automobile meeting the estimated time of human actual travel within 15 minutes from the neighborhood table obtained in the step two according to the actual road network of the high-grade map;
calculating the actual driving passing time of the electric vehicle charging service demand point (IN _ FID) and the corresponding electric vehicle charging station (NEAR _ FID), and connecting all electric vehicle charging demand points meeting the condition that the estimated passing time is within 15 minutes IN space, so that the serviceable range of all electric vehicle charging stations IN the current city can be obtained.
In the step (I); the spatial discretization interval can be determined according to different characteristics and topographic features of the city.
In the third step; writing Python codes and calling a route planning API (application program interface) of the high-grade map;
a, applying for an account number on a high-grade open platform, creating an application for calling a path planning API service, and acquiring a Key for calling corresponding API authorization;
b, installing Python packages required by Python language for data processing, network computing and transmission, wherein the Python packages comprise requests packages, pandas packages, xlrd packages and Json packages; the requests packet is used for the Python program to easily send HTTP/1.1 request information to the website and check a return response; the Pandas package can rapidly provide an accurate and flexible data structure, and simply and intuitively process the structured data; the Xlrd package is a data processing module of Python based on an Excel table, so that the numbers of the electric vehicle charging service demand points (IN _ FID) and the corresponding electric vehicle charging stations (NEAR _ FID) IN the neighborhood table obtained IN the step (II), and the longitude and latitude and distance information (NEAR _ DIST) of the two points need to be prestored as the Excel table; the Json packet is used for processing Json data returned by the API;
compiling Python codes, calling a route planning API of the Gade map, processing results of the neighborhood table IN the step two, performing network calculation, outputting the distance between a charging service demand point (IN _ FID) of the electric vehicle and a corresponding charging station (NEAR _ FID) of the electric vehicle IN a neighborhood analysis result between the two points according to the real-time road network of the Gade map and the driving passing distance inquired by the route planning API of the Gade map and actual estimated consumed time; the key code is shown in figure 5;
d, screening a point pair set of the electric vehicle charging service demand point (IN _ FID) and the corresponding electric vehicle charging station (NEAR _ FID) within 15 minutes of the actual passing predicted time from the result calculated IN the step C;
and E, connecting all the electric vehicle charging service demand points which can be served by the electric vehicle charging stations in the D acquisition point pair set into pieces, so that the actual service range which can be reached by all the electric vehicle charging stations in the current city in 15 minutes of real-time road network traffic according to the Gade map can be obtained.
In the second step; in order to reduce errors, the neighborhood search radius of the electric vehicle charging service demand point can be properly enlarged to 8 kilometers, 3 charging stations which can provide charging service for the electric vehicle charging service demand point within 8 radiuses are searched, and a neighborhood table is generated.
Referring to FIGS. 1-8: the invention also comprises the following specific use process: the accessibility evaluation of the geographic information system should have clear demand points and supply points for the charging service; for the accessibility evaluation problem of the electric vehicle charging station, it is already clear that the existing electric vehicle charging station is a supply point for providing charging service for an electric vehicle, and the determination of a demand point of the electric vehicle charging service needs to be scientific, reasonable and practical; meanwhile, the geographic information space accessibility evaluation usually relates to the accessibility of a road network and the accuracy of human trip activities, so that the accessibility evaluation is required to obtain a result which is most consistent with the actual condition, the used road network has stronger timeliness, and the trip scheme research is more consistent with the actual trip behavior activities and concepts of human beings; the introduction of the geographic information spatial modeling technology and the concept of '15-minute life circle' and the Application Program Interface (API) calling technology based on the route planning of the Gade map are effective solutions for predicting human driving travel activities by utilizing a Geographic Information System (GIS) technology and a real-time road network so as to solve three key problems in reachability evaluation of an electric vehicle charging station.
The technical scheme of the invention is realized by adopting a GIS and a programming technology; the technical scheme of the invention is described below by combining the attached drawings and a case of evaluating the accessibility of the electric vehicle charging station in a certain research area; the method is an electric vehicle reachability evaluation method based on the Goodpasture map path planning API technology by combining a geographic information spatial modeling method and a 15-minute living circle concept, and is concretely implemented as follows.
In the case, according to the concept of '15-minute life circle', the neighborhood range which can be reached by driving in 15 minutes is searched for as the radius, and 3 electric vehicle charging stations which can provide charging service for the electric vehicle charging service demand points and are closest to the electric vehicle charging service demand points are searched; the neighborhood search radius is determined and estimated according to the average driving speed of about 30 kilometers per hour in a city, and the linear distance which can be reached in 15 minutes is about 7.5 kilometers; in order to reduce errors, the neighborhood search radius of the electric vehicle charging service demand point can be properly enlarged to 8 kilometers, 3 charging stations which can provide charging service for the electric vehicle charging service demand point within the radius are searched, and a neighborhood table is generated as shown in fig. 4.
As shown IN fig. 4, the electric vehicle charging service demand point with an IN _ FID number of 0 has the nearest 3 charging station numbers (NEAR _ FID) of 176, 76, and 167 within the search radius of 8 km, and the distances (NEAR _ DIST) from the charging service demand point with the charging station number of 0 to the 3 electric vehicle charging stations are 1901 meter, 1127 meter, and 1864 meter, respectively, all within the search radius of 8 km; that is, it is said that, IN the neighborhood table, as long as any one electric vehicle charging service demand point (IN _ FID) has a corresponding electric vehicle charging station (NEAR _ FID), it is said that the charging service demand point has an electric vehicle charging station capable of providing charging service for the charging service demand point; the set of all electric vehicle charging service demand points (IN _ FID) IN the neighborhood table is the range that the electric vehicle charging station can serve IN the current city.
(1) applying for an account number on a high-grade open platform, creating an application for calling a path planning API service, and acquiring a Key for calling corresponding API authorization;
(2) installing Python packages required by Python language for data processing, network computing and transmission, wherein the Python packages comprise requests packages, pandas packages, xlrd packages and Json packages; the requests packet is used for the Python program to easily send HTTP/1.1 request information to the website and check a return response; the Pandas package can rapidly provide an accurate and flexible data structure, and simply and intuitively process the structured data; the Xlrd packet is a data processing module of Python based on an Excel table, so that the numbers of the electric vehicle charging service demand points (IN _ FID) and the corresponding electric vehicle charging stations (NEAR _ FID) IN the neighborhood table obtained IN the step 2, and the longitude and latitude and distance information (NEAR _ DIST) of the two points need to be prestored IN the Excel table; the Json packet is used for processing Json data returned by the API;
(3) compiling Python codes, calling a route planning API of the Gade map to process and perform network calculation on results of the neighborhood table IN the step (2), and outputting the distance between two points between an electric vehicle charging service demand point (IN _ FID) and a corresponding electric vehicle charging station (NEAR _ FID) IN a neighborhood analysis result according to the real-time road network of the Gade map and the driving passing time inquired by the route planning API of the Gade map and actual estimated time consumption; the key code is shown in figure 5;
(4) screening a point pair set of an electric vehicle charging service demand point (IN _ FID) and a corresponding electric vehicle charging station (NEAR _ FID) within 15 minutes of the actual passing predicted time from the result of the calculation IN the step (3); FIG. 6 shows a set of all available electric vehicle charging service demand points that can be met by the actual road network of the Gade map within 15 minutes by the actual driving traffic between the electric vehicle charging service demand points and the electric vehicle charging stations, the result being shown in FIG. 6;
(5) connecting all electric vehicle charging service demand points which can be served by the electric vehicle charging stations in the point pair set obtained in the step (4) into pieces, so that an actual service range which can be reached by all electric vehicle charging stations in the current city in 15 minutes of real-time road network traffic according to the Gade map can be obtained, and the result is shown in an attached figure 7;
based on the reachability analysis result of the electric vehicle charging station in the research area in this case, decision basis can be provided for site selection of the charging station in the future research area, for example, along with the increase of the number of electric vehicles, the electric vehicle charging stations can be properly added in the inaccessible space range of the electric vehicle charging station in the attached figure 7, and meanwhile, for the purpose of saving and utilizing resources, a scientific and reasonable model is constructed to optimize the configuration of the existing and newly added electric vehicle charging stations; however, as shown in fig. 8, the area indicated by the red circle is an area which is not completely covered by the current urban road network, and therefore, even if the electric vehicle charging station is built in the red circle area, there is a possibility that the charging service cannot be provided within a certain travel time; when planning and site selection of all electric vehicle charging stations are carried out, the planning of a traffic road network is also required to be considered;
the research area electric vehicle charging station reachability evaluation embodiments described herein are merely illustrative of the spirit of the present invention; those skilled in the art to which the invention relates may modify, supplement, or substitute the particular embodiments described, without departing from the spirit of the invention or exceeding the scope of the claims.
The above-mentioned parts not described in detail are prior art.
Claims (4)
1. A reachability evaluation method for an electric vehicle charging station is characterized by comprising the following steps: it comprises the following steps;
carrying out spatial modeling on a research area to determine the spatial distribution position of demand points of the electric vehicle charging service;
carrying out spatial discretization modeling on a research area at certain intervals, and taking obtained discrete points as electric automobile charging service demand points of the research area;
in order to improve the calculation efficiency, a large-area water area is excluded in advance in the space discretization modeling;
determining the reachable initial range of the electric vehicle charging station;
calculating the actual passing distance and the expected time meeting the requirements of human beings by adopting a neighborhood analysis method, and taking the result larger than the actual service range of the electric vehicle charging station as an initial range;
by using a neighborhood analysis method, an electric vehicle charging service demand point is used as a neighborhood analysis input element, an electric vehicle charging station is used as a neighborhood element, and the charging service charging station provided as the charging service demand point in an estimated range calculated based on a '15-minute living circle' concept and traditional human trip activities is screened;
in order to reduce calculation and ensure scientific effectiveness of results, three electric vehicle charging stations which meet the concept of '15-minute life circle' and the traditional human trip activity are screened out according to the existing electric vehicle charging stations in the region;
therefore, all electric vehicle charging service demand point sets which can be served by each electric vehicle charging station can be determined, and the demand point sets form reachable initial ranges of all electric vehicle charging stations;
according to the concept of '15-minute living circle', searching a neighborhood range which can be reached by driving in 15 minutes as a radius, and searching 3 electric vehicle charging stations which can provide charging service for electric vehicle charging service demand points and are closest to the electric vehicle charging service demand points; the neighborhood search radius is determined and estimated according to the average driving speed of about 30 kilometers per hour in a city, and the linear distance which can be reached in 15 minutes is about 7.5 kilometers; searching three charging stations which can provide charging service for the charging stations within the radius to obtain demand points meeting the conditions and a neighborhood list of the charging stations;
compiling Python codes to call a route planning API (application program interface) of the high-grade map, and searching a charging demand point set of the electric automobile meeting the estimated time of human actual travel within 15 minutes from the neighborhood table obtained in the step two according to the actual road network of the high-grade map;
calculating the actual driving passing time of the electric vehicle charging service demand point (IN _ FID) and the corresponding electric vehicle charging station (NEAR _ FID), and connecting all electric vehicle charging demand points meeting the condition that the estimated passing time is within 15 minutes IN space, so that the serviceable range of all electric vehicle charging stations IN the current city can be obtained.
2. The electric vehicle charging station reachability evaluation method according to claim 1, wherein: in the step (I); the spatial discretization interval can be determined according to different characteristics and topographic features of the city.
3. The electric vehicle charging station reachability evaluation method according to claim 1, wherein: in the third step; writing Python codes and calling a route planning API (application program interface) of the high-grade map;
a, applying for an account number on a high-grade open platform, creating an application for calling a path planning API service, and acquiring a Key for calling corresponding API authorization;
b, installing Python packages required by Python language for data processing, network computing and transmission, wherein the Python packages comprise requests packages, pandas packages, xlrd packages and Json packages; the requests packet is used for the Python program to easily send HTTP/1.1 request information to the website and check a return response; the Pandas package can rapidly provide an accurate and flexible data structure, and simply and intuitively process the structured data; the Xlrd package is a data processing module of Python based on an Excel table, so that the numbers of the electric vehicle charging service demand points (IN _ FID) and the corresponding electric vehicle charging stations (NEAR _ FID) IN the neighborhood table obtained IN the step (II), and the longitude and latitude and distance information (NEAR _ DIST) of the two points need to be prestored as the Excel table; the Json packet is used for processing Json data returned by the API;
compiling Python codes, calling a route planning API of the Gade map, processing results of the neighborhood table IN the step two, performing network calculation, outputting the distance between a charging service demand point (IN _ FID) of the electric vehicle and a corresponding charging station (NEAR _ FID) of the electric vehicle IN a neighborhood analysis result between the two points according to the real-time road network of the Gade map and the driving passing distance inquired by the route planning API of the Gade map and actual estimated consumed time; the key code is shown in figure 5;
d, screening a point pair set of the electric vehicle charging service demand point (IN _ FID) and the corresponding electric vehicle charging station (NEAR _ FID) within 15 minutes of the actual passing predicted time from the result calculated IN the step C;
and E, connecting all the electric vehicle charging service demand points which can be served by the electric vehicle charging stations in the D acquisition point pair set into pieces, so that the actual service range which can be reached by all the electric vehicle charging stations in the current city in 15 minutes of real-time road network traffic according to the Gade map can be obtained.
4. The electric vehicle charging station reachability evaluation method according to claim 1, wherein: in the second step; in order to reduce errors, the neighborhood search radius of the electric vehicle charging service demand point can be properly enlarged to 8 kilometers, 3 charging stations which can provide charging service for the electric vehicle charging service demand point within 8 radiuses are searched, and a neighborhood table is generated.
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